Dissertations / Theses on the topic 'Multi-modal imaging'

The development of hybrid PET/MRI imaging systems needs to be paralleled with the development of a hybrid intrinsic PET/MRI probes. The aim of this work was to develop and validate a novel radio-superparamagnetic nanoparticle (r-SPNP) for hybrid PET/MRI imaging. This was achieved with the synthesis of superparamagnetic iron oxide nanoparticles (SPIONs) that intrinsically incorporated 59Fe and manganese iron oxide nanoparticles (MIONs) that intrinsically incorporated 52Mn. Both [59Fe]-SPIONs and [52Mn]-MIONs were produced through thermal decomposition synthesis. The physiochemical characteristics of the r-SPNPs were assessed with TEM, DLS, and zeta-potential measurements, as well as in imaging phantom studies. The [59Fe]-SPIONs were evaluated in vivo with biodistribution and MR imaging studies. The biodistrubution studies of [59Fe]-SPIONs showed uptake in the liver. This corresponded with major MR signal contrast measured in the liver. 52Mn was produced on natural chromium through the 52Cr(p,n)52Mn reaction. The manganese radionuclides were separated from the target material through a liquid-liquid extraction. The αVβ3 integrin binding of [52Mn]-MION-cRGDs was evaluated with αVβ3 integrin solid phase assays, and the expression of αVβ3 integrin in U87MG xenograft tumors was characterized with fluorescence flow cytometry. [52Mn]-MION-cRGDs were used for in vivo PET and MR imaging of U87MG xenograft tumor bearing mice. PET data showed increased [52Mn]-MION-cRGD uptake compared with untargeted [52Mn]-MIONs. ROI analysis of PET and MRI data showed that MR contrasted corresponded with PET signal. Future work will utilize [52Mn]-MION-cRGDs in other tumor models and with hybrid PET/MRI imaging systems.

Neurocognitive models of speech comprehension generally outline either the spatial or temporal organisation of speech processing and rarely consider combining the two to provide a more complete model. Simultaneous EEG-fMRI recordings have the potential to link these domains, due to the complementary high spatial (fMRI) and temporal (EEG) sensitivities. Although the neural basis of speech comprehension has been investigated intensively during the past few decades there are still some important outstanding questions. For instance, there is considerable evidence from neuropsychology and other convergent sources that the anterior temporal lobe (ATL) should play an important role in accessing meaning. However, fMRI studies do not usually highlight this area, possibly because magnetic susceptibility artefacts cause severe signal loss within the ventral ATL (vATL). In this thesis EEG and fMRI were used to refine the spatial and temporal components of neurocognitive models of speech comprehension, and to attempt to provide a combined spatial and temporal model. Chapter 2 describes an EEG study that was conducted while participants listened to intelligible and unintelligible single words. A two-pass processing framework best explained the results, which showed comprehension to proceed in a somewhat hierarchical manner; however, top-down processes were involved during the early stages. These early processes were found to originate from the mid-superior temporal gyrus (STG) and inferior frontal gyrus (IFG), while the late processes were found within ATL and IFG regions. Chapter 3 compared two novel fMRI methods known to overcome signal loss within vATL: dual-echo and spin-echo fMRI. The results showed dual-echo fMRI outperformed spin-echo fMRI in vATL regions, as well as extra temporal regions. Chapter 4 harnessed the dual-echo method to investigate a speech comprehension task (sentences). Intelligibility related activation was found in bilateral STG, left vATL and left IFG. This is consistent with converging evidence implicating the vATL in semantic processing. Chapter 5 describes how simultaneous EEG-fMRI was used to investigate word comprehension. The results showed activity in superior temporal sulcus (STS), vATL and IFG. The temporal profile showed that these nodes were most active around 400 ms (specifically the anterior STS and vATL), while the vATL was consistently active across the whole epoch. Overall, these studies suggest that models of speech comprehension need to be updated to include the vATL region, as a way of accessing semantic meaning. Furthermore, the temporal evolution is best explained within a two-pass framework. The early top-down influence of vATL regions attempt to map speech-like sounds onto semantic representations. Successful mapping, and therefore comprehension, is achieved around 400 ms in the vATL and anterior STS.

Registration of medical imaging is essential for aligning in time and space different modalities and hence consolidating their strengths for enhanced diagnosis and for the effective planning of treatment or therapeutic interventions. The primary objective of this study is to develop an integrated registration method that is effective for registering both brain and whole-body images. We seek in the proposed method to combine in one setting the excellent registration results that FMRIB Software Library (FSL) produces with brain images and the excellent results of Statistical Parametric Mapping (SPM) when registering whole-body images. To assess attainment of these objectives, the following registration tasks were performed: (1) FDG_CT with FLT_CT images, (2) pre-operation MRI with intra-operation CT images, (3) brain only MRI with corresponding PET images, and (4) MRI T1 with T2, T1 with FLAIR, and T1 with GE images. Then, the results of the proposed method will be compared to those obtained using existing state-of-the-art registration methods such as SPM and FSL. Initially, three slices were chosen from the reference image, and the normalized mutual information (NMI) was calculated between each of them for every slice in the moving image. The three pairs with the highest NMI values were chosen. The wavelet decomposition method is applied to minimize the computational requirements. An initial search applying a genetic algorithm is conducted on the three pairs to obtain three sets of registration parameters. The Powell method is applied to reference and moving images to validate the three sets of registration parameters. A linear interpolation method is then used to obtain the registration parameters for all remaining slices. Finally, the aligned registered image with the reference image were displayed to show the different performances of the 3 methods, namely the proposed method, SPM and FSL by gauging the average NMI values obtained in the registration results. Visual observations are also provided in support of these NMI values. For comparative purposes, tests using different multi-modal imaging platforms are performed.

The current 'gold standard method' of detecting cancer relies on microscopic examination by specialised pathologists. However, there are risks associated with surgery and biopsies and so the ability to diagnose cancer and other diseases in a non-invasive manner is highly attractive. There are many imaging techniques suitable for this, each with their own advantages and disadvantages, which can be improved by the use of contrast agents. The incorporation of targeting vectors allows for the specific imaging of desired tissues. Further to this, the incorporation of more than one contrast agent into one imaging agent allows for multi-modal imaging of cancerous tissue and other diseases. This allows for the advantages of different techniques to be used simultaneously and is an emerging field. The methods for the synthesis of these drugs can be synthetically demanding and low yielding due to linear synthetic strategies. The use of multi-component reactions would be a major benefit and the Ugi reaction is particularly attractive due to the incorporation of four components and the biocompatible bis-amide motif of Ugi products. This work serves as an extension to previous work based on Ugi reactions of metal complexes, which showed that amine and carboxylic acid appended lanthanide and carboxylic acid appended d-metal complexes can be used as stable building blocks in the formation of mono-metallic complexes. This work presents the synthesis of aldehyde appended lanthanide complexes and their use in Wittig and Ugi chemistry in the synthesis of mono-metallic complexes. The previously synthesised amine appended lanthanide complexes 1, 3, 4 were also synthesised to be used as a feedstock in subsequent Ugi reactions. A number of carboxylic acid appended d-metal complexes and cyanine dyes were synthesised according literature procedures. Both the bis-acid appended d-metal complexes and cyanine dyes were used unsuccessfully in the Ugi reaction. However, the mono-acid d-metal complexes were used successfully in the Ugi reaction in keeping with previous reports. These were used as the third feedstock for the synthesis of trimetallic complexes along with the aldehyde and amine appended lanthanide complexes via the Ugi reaction. In addition, a number of Ugi reactions were performed on organic compounds. The use of p-toluic acid gave five Ugi compounds, which were characterised and gave the expected results. However, the use of biotin as the carboxylic acid component gave four compounds that were complex to characterise and suggested that the incorporated biotin may not serve as a viable targeting vector. One of the p-toluic acid Ugi products was reacted further and a biotin moiety was incorporated with a (CH2)6 spacer. Spectroscopic evidence suggested that the biotin would still act as a viable targeting vector. Overall, this work serves to set the scene for the synthesis of targeted tri-metallic multi-modal imaging agents using stable metal complexes as building blocks in the Ugi reaction.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 60-62). Also available in electronic version. Access restricted to campus users.

Background Visual hallucinations (VH) are an important non-motor complication of Parkinson’s disease (PD) which carries a negative prognosis, but their biological basis is unclear. Multi-modal magnetic resonance imaging (MRI) can be used to evaluate structural and functional brain mechanisms underpinning VH in PD. Methods To assess cerebral microstructure and resting functional activities in patients with idiopathic PD and VH, I compared PD patients with VH (PDVH) and PD patients without VH (PDnonVH), while healthy controls (HC) were also recruited for comparison. Diffusion tensor imaging was used to calculate mean diffusivity (MD) and fractional anisotropy (FA). Structural MRI was used to calculate voxel-based intensity of grey matter (GM) and white matter (WM) across the entire brain and compared among groups. Furthermore, functional magnetic resonance imaging of the brain, acquired during rest, was processed to calculate the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) to inform a model of VH. In addition, hippocampal volume, shape, mean diffusivity and FC across the whole brain was further examined. Hippocampal dependent visual spatial memory performance was compared between groups, and predicted correlations with hippocampal microstructural indices and VH severity were tested. Results In the first study, PDVH had lower FA than both PDnonVH and HC in the right occipital lobe and left parietal lobe, but increased FA in the right infero-medial fronto-occipital fasciculus and posterior inferior longitudinal fasciculus. Moreover, PDVH patients showed less GM volume compared to PDnonVH in the right lingual gyrus of the occipital lobe. In the second study, PDVH patients compared to non-hallucinators showed lower ALFF in occipital lobes, with greater ALFF in temporo-parietal region, limbic lobe and right cerebellum. The PDVH group also showed alteration in functional connectivity between occipital region and corticostriatal regions. Finally in the third study, although there were no gross hippocampal volume and shape differences across groups, individuals with PDVH had higher diffusivity in hippocampus than PDnonVH and HC. Both PD groups had significantly poorer visuospatial memory compared to HC. Poorer visuospatial memory was correlated with higher hippocampal diffusivity in HC and more severe VH in the PDVH group.FC between hippocampus and primary visual cortex, dorsal/ventral visual pathways was also lower in PDVH than other groups, whereas FC between hippocampus and default mode network regions was greater in PDVH group compared to others. Conclusion Compared to PDnonVH groups, the PDVH group had multiple structural deficits in primary and associative visual cortices. In term of hemodynamic activity, the PDVH group had lower ALFF in occipital lobe, but greater ALFF in regions that comprise the dorsal visual pathway. Moreover, this lower ALFF in the primary visual cortex was accompanied by lower functional connectivity across components of the ventral/dorsal visual pathway in the PDVH group compared to the PDnonVH group. Moreover, evidence supporting a specific role for the hippocampus in PDVH was obtained. In the absence of gross macrostructural anomalies, hippocampal microstructure and functional connectivity was compromised in PDVH. I observed an association between visuospatial memory and hippocampal integrity and suggest that hippocampal pathology and consequent disruption in visuospatial memory plays a key contribution to VH in PD. Thus, in the PDVH group, "bottom-up" primary visual cortex and “top-down” visual association pathways and attentional networks appear to be disrupted.
published_or_final_version
Psychiatry
Doctoral
Doctor of Philosophy

The development, characterisation and testing of a novel all-optical, multi-modal preclinical biomedical imaging system is presented. The system aims to provide a new way of accurately visualising the spatial distribution and activity of molecular structures and processes in small animals by combining 3D bioluminescence tomography (BLT; reconstruction-based 3D imaging of internal bioluminescent reporter distributions), diffuse optical tomography (DOT; reconstruction-based imaging of optical parameter distributions) and optical surface capture techniques. The key principle of the imaging system is to use surface capture results to enhance the accuracy of DOT image reconstruction, and to use the results of both surface capture and DOT to enhance the accuracy of BLT. Presented experiments show that the developed system can reconstruct luminescent source distributions and optical parameters accurately and that small animal imaging is feasible with the system.

Myocardial ischaemia causes progressive cellular injury. Initially there is loss of function, followed by formation of oedema. Ischemia, if prolonged, will eventually lead to cell death. Revascularisation of occluded coronary arteries is an efficient tool to reduce infarct size in acute myocardial infarction. However, reperfusion in itself can be associated with a significant amount of myocardial damage. The structural and metabolic changes occurring in myocardial ischemia and reperfusion can now be studied in-vivo using magnetic resonance imaging (MRI) and are targets to optimise the treatment of patients with acute myocardial ischemia. The development of new MRI techniques for T1 mapping allows the study of the evolution of myocardial oedema in both humans and animals. Moreover, hyperpolarised MR spectroscopy (MRS) allows the non-invasive assessment of myocardial metabolism, as injected hyperpolarised molecules can be used to study the function of metabolic pathways and enzymes in the setting of ischaemia and reperfusion. The aims of this project were to develop an animal model to allow the study of myocardial ischemia in real time, and then use this model to study the acute development of oedema and the metabolic changes in the metabolism of pyruvate. With the use of a vascular occluder, we successfully developed a closed-chest model of ischemia in the rat. This model allowed the animals to recover from the stress of surgery while also allowing ischemia experiments to be carried while the animals remain in the bore of the scanner. We then proceeded to validate this model and validate a new MR sequence, which produces cine-MR, T1 mapping and inversion-recovery prepared images. We studied the effects of varying durations of myocardial ischemia on the development of myocardial infarction and used this to validate the Small Animal Look-Locker Inversion Recovery (SALLI) multimodal imaging sequence. We were also able to study the development of myocardial oedema in real time, and demonstrate that preconditioning attenuated the T1 lengthening effects of myocardial ischemia. Following this, the acute changes in pyruvate metabolism occurring in the myocardial area at risk following 15 minutes of myocardial ischemia were investigated. We were able to detect abnormal metabolism in the area at risk for the first 60 minutes following ischemia and demonstrated that metabolism returned to normal 1 week after ischemia. The methods used show much promise in the study of the changes occurring in myocardial ischemia-reperfusion, and study of the effects of treatments such as pre- and postconditioning.

Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this project, a self-supervised approach is adapted to attain domain adaptation across images while retaining important 3D information from medical images using a simple 3D-UNet with a few auxiliary tasks. The method comprises of two main steps: representation learning via self-supervised learning (pre-training) and fully supervised learning (fine-tuning). Pre-training is done using a 3D-UNet as a base model along with some auxiliary data augmentation tasks to learn representation through texture, geometry and appearances. The second step is fine-tuning the same network, without the auxiliary tasks, to perform the segmentation tasks on CT and MR images. The annotations of all organs are not available in both modalities. Thus the first step is used to learn general representation from both image modalities; while the second step helps to fine-tune the representations to the available annotations of each modality. Results obtained for each modality were submitted online, and one of the evaluations obtained was in the form of DICE score. The results acquired showed that the highest DICE score of 0.966 was obtained for CT liver prediction and highest DICE score of 0.7 for MRI abdominal segmentation. This project shows the potential to achieve desired results by combining both self and fully-supervised approaches.

In this thesis, we describe several imaging techniques specifically designed and developed for the assessment of pulmonary structure, function and pathology. We then describe the application of this technology within appropriate biological systems, including the identification, tracking and assessment of lung tumors in a mouse model of lung cancer. The design and development of a Large Image Microscope Array (LIMA), an integrated whole organ serial sectioning and imaging system, is described with emphasis on whole lung tissue. This system provides a means for acquiring 3D pathology of fixed whole lung specimens with no infiltrative embedment medium using a purpose-built vibratome and imaging system. This system enables spatial correspondence between histology and non-invasive imaging modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), providing precise correlation of the underlying 'ground truth' pathology back to the in vivo imaging data. The LIMA system is evaluated using fixed lung specimens from sheep and mice, resulting in large, high-quality pathology datasets that are accurately registered to their respective CT and H&E histology. The implementation of an in vivo micro-CT imaging system in the context of pulmonary imaging is described. Several techniques are initially developed to reduce artifacts commonly associated with commercial micro-CT systems, including geometric gantry calibration, ring artifact reduction and beam hardening correction. A computer controlled Intermittent Iso-pressure Breath Hold (IIBH) ventilation system is then developed for reduction of respiratory motion artifacts in live, breathing mice. A study validating the repeatability of extracting valuable pulmonary metrics using this technique against standard respiratory gating techniques is then presented. The development of an ex vivo laser scanning confocal microscopy (LSCM) and an in vivo catheter based confocal microscopy (CBCM) pulmonary imaging technique is described. Direct high-resolution imaging of sub-pleural alveoli is presented and an alveolar mechanic study is undertaken. Through direct quantitative assessment of alveoli during inflation and deflation, recruitment and de-recruitment of alveoli is quantitatively measured. Based on the empirical data obtained in this study, a new theory on alveolar mechanics is proposed. Finally, a longitudinal mouse lung cancer study utilizing the imaging techniques described and developed throughout this thesis is presented. Lung tumors are identified, tracked and analyzed over a 6-month period using a combination of micro-CT, micro-PET, micro-MRI, LSCM, CBCM, LIMA and H&E histology imaging. The growth rate of individual tumors is measured using the micro-CT data and traced back to the histology using the LIMA system. A significant difference in tumor growth rates within mice is observed, including slow growing, regressive, disappearing and aggressive tumors, while no difference between the phenotype of tumors was found from the H&E histology. Micro-PET and micro-MRI imaging was conducted at the 6-month time point and revealed the limitation of these systems for detection of small lesions ( < 2mm) in this mouse model of lung cancer. The CBCM imaging provided the first high-resolution live pathology of this mouse model of lung cancer and revealed distinct differences between normal, suspicious and tumor regions. In addition, a difference was found between control A/J mice parenchyma and Urethane A/J mice ‘normal’ parenchyma, suggesting a 'field effect' as a result of the Urethane administration and/or tumor burden. In conclusion, a comprehensive murine lung cancer imaging study was undertaken, and new information regarding the progression of tumors over time has been revealed.

Bioluminescence imaging (BLI) is widely used in pre-clinical research to monitor the location and migration of different cell types, and the growth of cancerous tumours and response to treatments within murine models. However, the quantitative accuracy of the technique is limited. The position of the animal is known to affect the measured bioluminescence, with a change in position causing a change in measurement. Work presented here will address this problem, validating a free space model in a murine model to produce surface bioluminescence measurements which are independent of the position of the animal. The position of the source within the animal and the underlying tissue attenuation also affect the quantitative accuracy of bioluminescence measurements. An extension to bioluminescence imaging, bioluminescence tomography (BLT), aims to overcome these problems by recovering the three-dimensional bioluminescent source distribution within the animal. However, there are limitations to the quantitative accuracy of BLT. Current reconstruction algorithms ignore the bandwidth of band-pass filters used for multi-spectral data collection for BLT. This work develops a model which accounts for filter bandwidth in the BLT reconstruction, improving the quantitative accuracy of the technique. An additional limitation to the quantitative accuracy of BLT is that accurate knowledge of the optical properties of the animal are required but are difficult to acquire. Work to improve the quantitative accuracy by obtaining subject-specific optical properties via a spectral derivative reconstruction method for diffuse optical tomography (DOT) is presented. The initial results are promising for the application of the method in vivo.

Honors thesis (B.S.)--Ohio State University, 2008.
Title from first page of PDF file. Includes bibliographical references (p. xxxi-xxxii). Available online via Ohio State University's Knowledge Bank.

Upconversion phosphor materials, usually consisting of crystals doped with lanthanide ions, are attracting increasing attention and several possible applications in various fields have been proposed so far: e.g. solar cells with improved efficiency, nanomaterials for bio-imaging, microtags in anti-counterfeiting inks and lasers and novel display technologies. In recent years, lanthanide (Ln3+)-doped upconverting nanoparticles (UCNPs) have proved to be photostable and basically nontoxic, thus have been presented as efficient and versatile bioimaging probes. This type of nanoparticles can be excited with near-infrared (NIR) light, while emitting higher-energy photons in a wide range of the electromagnetic spectrum, from the ultraviolet (UV), to visible (VIS) and near infrared (NIR) regions, via a multiphoton process. In particular, operating within the biological window leads to several advantages, such as drastically reduced photodamage and autofluorescence background, and remarkable tissue penetration. On the other hand, bismuth-based luminescent materials have proved to be excellent candidates for the design of bulk and nanosized phosphors, thanks to peculiar optical characteristics and appealing properties such as low cost of production and almost non-toxicity. Driven by these factors our work is mainly focused on the development of novel nanostructures, i.e. lanthanide-doped bismuth silicate-silica core-shell nanoparticles, to be employed as biological probes. A new synthetic procedure is here developed to obtain NPs composed of a crystalline Bi2SiO5 core embedded in a glassy shell of dense SiO2. Uniform, monodispersed, crystalline and non-toxic nanoparticles are obtained. The tunability of the UC emission is investigated by co-doping the system with different combinations and relative concentrations of lanthanide ions (Yb, Er, Ho, Tm). Lanthanide-doped Bi2SiO5@SiO2 NPs are thoroughly characterized, allowing to assess their potential as bioimaging and temperature sensing nanoprobes. In fact, the strongly temperature-dependent behaviour of the upconversion photoluminescence (UCPL) in lanthanide ions, allows to develop ratiometric luminescent thermal sensors, emitting in the VIS or NIR regions, with promising properties in the biological field. The Nd3+ singly-doped Bi2SiO5@SiO2 system is also investigated as thermal bio-probe and its optical properties are compared with that of two others bismuth silicate phases of the Bi2O3-SiO2 phase diagram, namely Bi4Si3O12 and Bi12SiO20. Moreover, the synthesized NPs are multifunctional, potentially being multi-modal probes for combined optical imaging and X-ray computed tomography (CT)/single-photon emission CT (SPECT)/photoacoustic tomography (PAT), thanks to the X-ray attenuating properties of the bismuth-based matrix.

Research involved in modeling human lung disease conditions has provided insight into disease development, progression, and treatment. In particular, mouse models of human pulmonary disease are increasingly utilized to characterize lung disease conditions. With advancements in small animal imaging it is now possible to investigate the phenotypic differences expressed in inbred mouse strains in vivo to investigate specific disease conditions that affect the lung. In this thesis our aim was to generate a comprehensive characterization of the normative mouse lung phenotypes in three of the most utilized strains of mice, C57BL/6, A/J, and BALB/c, through imaging techniques. The imaging techniques that we utilized in this research included micro-CT, a custom Large Image Microscope Array (LIMA) system for 3D microscopy, and classical histology. Micro-CT provided a non-destructive technique for acquiring in vivo and fixed lung images. The LIMA 3D microscopy system was utilized for direct correspondence of the gold standard histology images as well as to validate the anatomical structures and measurements that were extracted from the micro-CT images. Finally, complete lung histology slices were utilized for assessment of the peripheral airspace structures that were not resolvable using the micro-CT imaging system. Through our developed imaging acquisition and processing strategies we have been able to successful characterize important phenotypes in the mouse lung that have not previously been known as well as identify strain variations. These findings will provide the scientific community with valuable information to be better equipped and capable of pursuing new avenues of research in investigating pulmonary disease conditions that can be modeled in the mouse.

Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
Cardiovascular disease (CVD) remains the leading cause of mortality and morbidity in the United States. It encompasses a variety of conditions that involve the heart and/or the vascular system, including coronary heart disease, ischemic heart disease, cardiomyopathy, atherosclerosis and stroke. Understanding the risk factors and mechanisms underlying CVD as well as developing early diagnostic methods has great clinical importance. In this thesis work, we used Magnetic Resonance Imaging (MRI) and studied two aspects of CVD. Firstly, we explored the regional influence of pericardial/periaortic fat on the underlying organs in human subjects. Pericardial fat volume did not correlate with BMI in either obese or control subjects. Left ventricular (LV) function, including stroke volume (r=-0.29, p=0.04) and cardiac output (r=-0.33, p=0.02), was inversely related to LV fat but not right ventricular (RV) fat. LV diastolic function, including early filling rate (E-rate), early/late filling ratio (E/A), showed a stronger correlation to LV fat (E-rate, r=-0.41, p=0.005; E/A, r=-0.31, p=0.04) than RV fat. This evidence suggests local toxic effects of pericardia! fat on cardiac structure/function. Periaortic fat was also found positively linked to plaque volume, which suggests a paracrine role of periaortic fat in atherogenesis. Secondly, we combined in vivo MRI and ex vivo targeted fluorescence imaging to detect vulnerable plaques (VPs) in a rabbit model. The fluorescence signal from enzyme-activated targeted probes (PLGLAG-cy5 and DPRSFL-cy5) co-localized most strongly with vulnerable aortic plaques as detected by MRI. Statistically, fluorescence signal was enhanced (by 40-60%) in VPs as compared to stable ones. In addition, the fluorescence signal was related to the MRI plaque vulnerability measurements, such as outward remodeling and enhanced gadolinium uptake. The combination of MRI and targeted molecular imaging can help us to understand both plaque morphology and functionality, which in turn can increase the diagnostic accuracy of the vulnerable plaques. In conclusion, we identified local toxic effect of regional fat depots on cardiovascular function using MRI. We also demonstrated MRI is a powerful technique in studying CVD. Combination of MRI and molecular imaging can help us understand the morphology and function of the atherosclerotic plaques, which might help early detection of VPs.

Faced with an increasingly aging and overweight population, our modern societies, particularly in the west, are set to witness a steep rise in various orthopaedic health problems in the coming decades, especially joint diseases such as arthritis. Better understanding of the way bones of the joints work is thus imperative for studying the nature and effects of these diseases and for finding cures. The data obtained from conventional sources such as skin markers and x-ray/fluoroscopy scans are generally useful but quite limited in terms of accuracy, quantification abilities and three-dimensional visualization potential. The continuous increase in the quality and versatility of various modern imaging modalities is enabling many new means for enhanced visualization and analysis of motion data of the joints. In this thesis we make use of ultrasound (US) and magnetic resonance (MR) imaging to facilitate robust, accurate and efficient analysis of the bones of joints in motion. We achieve this by obtaining motion data using 3D US with high temporal resolution which is then fused with a high spatial resolution, but static MRI volume of the same region (we mostly focus on the knee joint area). Our contributions include novel ways for improved segmentation and localization of the bones from image data. In particular, a highly effective method for improving bone segmentation in MRI volumes by enhancing the contrast at the bone-cartilage interface is proposed. Our contribution also focuses on robust and accurate registration of image data. To achieve this, a new method for stitching US bone volumes is proposed for generating larger fields of view. Further, we also present a novel method for US-MRI bone surface registration. The tools developed during the course of this thesis facilitate orthopaedic research efforts aiming to improving our understanding of the workings of the joints. The tools and methodologies proposed are versatile and expected to be applicable to other applications.

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.
[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías
[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.
Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523
TESIS

This PhD thesis is focused in the application of brain network models obtained from different modalities of magnetic resonance imaging (MRI) to characterize anomalies in neurodevelopment of a prenatal origin, using intrauterine growth restriction (IUGR) as a clinical model. Importantly, IUGR due to placental insufficiency affects 5-10% of all pregnancies and is a leading cause of fetal morbidity and mortality. The thesis is presented as a compendium of four studies published in international journals. Each of the studies is focused in the characterization of IUGR using brain networks obtained from a specific MRI modality (structural, diffusion and functional MRI) in a specific pediatric stage in the life of subjects with IUGR (neonatal, early infancy and pre-adolescence age). The first study focuses in the characterization of brain reorganization produced by IUGR at one year of age using brain networks based on a tractography obtain from diffusion MRI, using diffusion tensor imaging (DTI) approach. In this study it is demonstrated that brain network features of IUGR infants have alterations associated with an altered neurodevelopment later in life. The second study assess the viability to use a novel methodology to obtain structural brain networks based on simple anatomical MRI based on the similitude of gray matter (GM) patterns among different areas of the brain. We demonstrated alterations in infants with IUGR using this technique, and that the alterations found are also associated with neurodevelopmental problems found later in life. In the third study we used a rabbit model of IUGR to explore if the alterations in the structural brain network persist at long-term, during preadolescence. We demonstrated that indeed, there are alterations in the structural brain network organization that persist at long-term and that this alterations are associated with neurobehavioral outcomes. Finally, using normalization approaches, we observed a peculiar compensatory effect in the subjects with IUGR. In the forth study, we assessed functional brain networks of neonates with IUGR, demonstrating that this condition produces a reorganization of functional brain connectivity since such an early age, characterized by a pattern of increased co-activation and synchronization of brain regions together with a suboptimal organization when assessing normalized networks. In addition, functional brain network features were also associated to neurobehavioral alterations. Overall, our main conclusion is that IUGR condition produces structural and functional brain reorganization since early life that persists postnatally up to pre-adolescence. We hypothesize that the observed functional and structural reorganization could be a potential substrate of high risk of altered neurodevelopment in infants with IUGR, and postulate this condition as a possible brain network disorder. Importantly, the association of network features with neurobehavior and neurodevelopment since an early age opens the opportunity to further develop early image biomarkers of altered neurodevelopment, a clinical chance to improve the management of a condition that affects up to 10% of deliveries.
Aquesta tesis doctoral està centrada en l'aplicació de models de xarxa del cervell obtinguts a partir de diferents modalitats de ressonància magnètica (RM) per caracteritzar anomalies en el desenvolupament d'origen prenatal utilitzant la restricció de creixement intrauterí (RCIU) com a model clínic. La tesi està presentada com a compendi de quatre estudis publicats en revistes internacionals de primer quartil. Cada un dels estudis està centrat en la caracterització de la RCIU mitjançant xarxes cerebrals obtingudes a partir d'una modalitat de RM determinada en una etapa pediàtrica diferent, en la vida de subjectes amb RCIU. Així doncs, el primer estudi es centra en la caracterització de la reorganització cerebral produïda per RCIU a l'any de vida mitjançant xarxes cerebrals estructurals basades en RM per difusió. En aquest estudi es demostra que les característiques de xarxa en els subjectes amb RCIU presenten una sèrie d'alteracions relacionades amb un neuro-desenvolupament futur anormal. El segon projecte analitza la utilització de xarxes estructurals cerebrals basades en RM anatòmica convencional per caracteritzar alteracions en nens d'un any amb RCIU. Es demostra que efectivament amb aquesta tècnica també es troben alteracions en els infants amb IUGR, i que aquestes alteracions estan també relacionades amb problemes en el neuro-desenvolupament posterior. En el tercer projecte s'utilitza un model animal de conill amb RCIU per explorar les alteracions en la xarxa cerebral estructural que persisteix a llarg termini. Es demostra que efectivament existeixen alteracions en la organització estructural del cervell persistents a llarg termini i s'observa un efecte compensatori en els subjectes amb RCIU. En el quart projecte s'analitzen les xarxes cerebrals funcional en neonats amb RCIU, demostrant que aquesta condició prenatal genera una reorganització en la connectivitat cerebral que té un substrat funcional, que es pot observar des d'etapes molt precoces de la vida i que està relacionada amb resultats de neuro-comportament.

In recent years, the study of brain connectivity has received growing interest from neuroscience field, from a point of view both of analysis of pathological condition and of a healthy brain. Hybrid PET/MRI scanners are promising tools to study this complex phenomenon. This thesis presents a general framework for the acquisition and analysis of simultaneous multi-modal PET/MRI imaging data to study brain connectivity in a clinical setting. Several aspects are faced ranging from the planning of an acquisition protocol consistent with clinical constraint to the off-line PET image reconstruction, from the selection and implementation of methods for quantifying the acquired data to the development of methodologies to combine the complementary information obtained with the two modalities. The developed analysis framework was applied to two different studies, a first conducted on patients affected by Parkinson’s Disease and dementia, and a second one on high grade gliomas, as proof of concept evaluation that the pipeline can be extended in clinical settings.

Non-destructive testing (NDT) techniques are critical to ensure integrity and safety of engineered structures. Structural health monitoring (SHM) is considered as the next step in the field enabling continuous monitoring of structures. The first part of the thesis concerns NDT and SHM using guided waves in plates, or Lamb waves, to perform imaging of plate structures. The imaging is performed using a fixed active array setup covering a larger area of a plate. Current methods are based on conventional beamforming techniques that do not efficiently exploit the available data from the small arrays used for the purpose. In this thesis an adaptive signal processing approach based on the minimum variance distortionless response (MVDR) method is proposed to mitigate issues related to guided waves, such as dispersion and the presence of multiple propagating modes. Other benefits of the method include a significant increase in resolution. Simulation and experimental results show that the method outperforms current standard processing techniques. The second part of the thesis addresses transducer design issues for resonant ultrasound inspections. Resonant ultrasound methods utilize the shape and frequency of the object's natural modes of vibration to detect anomalies. The method considered in the thesis uses transducers that are acoustically coupled to the inspected structures. Changes in the transducer's electrical impedance are used to detect defects. The sensitivity that can be expected from such a setup is shown to highly depend on the transducer resonance frequency, as well as the working frequency of the instrument. Through simulations and a theoretical argumentation, optimal conditions to achieve high sensitivity are given.

Cancer kills more Americans under the age of 75 than any other disease. Although most cancers occur in epithelial surfaces that can be directly visualized, the majority of cases are detected at an advanced stage. Optical imaging and spectroscopy may provide a solution to the need for non-invasive and effective early detection tools. These technologies are capable of examining tissue over a wide range of spatial scales, with widefield macroscopic imaging typically spanning several square-centimeters, and high resolution in vivo microscopy techniques enabling cellular and subcellular features to be visualized. This work presents novel technologies in two important areas of optical imaging: high resolution imaging and widefield imaging. For subcellular imaging applications, new high resolution endomicroscope techniques are presented with improved lateral resolution, larger field-of-view, increased contrast, decreased background signal, and reduced cost compared to existing devices. A new widefield optical technology called multi-modal spectral imaging is also developed. This technique provides real-time in vivo spectral data over a large field-of-view, which is useful for detecting biochemical alterations associated with neoplasia. The described devices are compared to existing technologies, tested using ex vivo tissue specimens, and evaluated for diagnostic potential in a multi-patient oral cancer clinical trial.

Advancements in retinal imaging technologies have drastically improved the quality of eye care in the past couple decades. Scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) are two examples of critical imaging modalities for the diagnosis of retinal pathologies. However current-generation SLO and OCT systems have limitations in diagnostic capability due to the following factors: the use of bulky tabletop systems, monochromatic imaging, and resolution degradation due to ocular aberrations and diffraction.

Bulky tabletop SLO and OCT systems are incapable of imaging patients that are supine, under anesthesia, or otherwise unable to maintain the required posture and fixation. Monochromatic SLO and OCT imaging prevents the identification of various color-specific diagnostic markers visible with color fundus photography like those of neovascular age-related macular degeneration. Resolution degradation due to ocular aberrations and diffraction has prevented the imaging of photoreceptors close to the fovea without the use of adaptive optics (AO), which require bulky and expensive components that limit the potential for widespread clinical use.

In this dissertation, techniques for extending the diagnostic capability of SLO and OCT systems are developed. These techniques include design strategies for miniaturizing and combining SLO and OCT to permit multi-modal, lightweight handheld probes to extend high quality retinal imaging to pediatric eye care. In addition, a method for extending true color retinal imaging to SLO to enable high-contrast, depth-resolved, high-fidelity color fundus imaging is demonstrated using a supercontinuum light source. Finally, the development and combination of SLO with a super-resolution confocal microscopy technique known as optical photon reassignment (OPRA) is demonstrated to enable high-resolution imaging of retinal photoreceptors without the use of adaptive optics.

Dissertation

La risonanza magnetica (RM) riveste una grande e crescente importanza nel campo del neuroimaging. Tra le modalità piu interessanti si colloca la RM pesata in diffusione (dMRI) che, insieme alla RM funzionale, alla magneto-encefalografia (MEG), ell'elettro-encefalografia (EEG) e alla spettroscopia funzionale nel vicino infrarosso (fNIRS) contribuisce a costituire una notevole e differenziata mole di informazioni che consentono di analizzare e modellare la struttura e la funzione cerebrale. La dMRI presenta il grande vantaggio di quantificare la diffusività tissutale in modo non invasivo attraverso la misura dei micromovimenti delle molecole di acqua, consentendo non solo di caratterizzare la struttura della materia bianca con elevata risoluzione, ma anche di supportare le attività cliniche sia quale supporto alla diagnostica sia quale strumento di pianificazione prechirurgica. Allo stato dell'arte, numerosi aspetti richiedono restano da chiarire determinando un notevole impiego di risorse a livello di ricerca. Tra i principali sono la riproducibità delle misure, la ricostruzione della funzione di distribuzione delle orientazioni (orientation diffusion function, ODF), specialmente in presenza di rumore, la modellazione dei network strutturale e funzionale e lo studio delle rispettive interazioni. In questa tesi, alcuni di questi aspetti sono stati analizzati e sono state proposte alcune soluzioni a livello sia metodologico che clinico. In particolare, a partire da dati diffusion spectrum imaging (DSI), è stato proposto un metodo di denoising del segnale basato sulla multirisoluzione che ha consentito la ricostruzione piu precisa della ODF e quindi della trattografia, è stato sviluppato un metodo di analisi della rimodellazione del network corticale motorio in pazienti affetti da stroke basato sulla tract-based quantification di parametri estratti dalla dMRI e dalla RM a trasferimento di magnetizzazione (MTR), ed è stato analizzato il network funzionale attivato dallo svolgimento di task motori predefiniti in vista dell'integrazione delle informazioni strutturale e funzionale in un modello corticale globale focalizzato sul loop motorio.
In neuroimaging, a great interest is currently being directed to diffusion magnetic resonance imaging (dMRI) which, in addition to functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), functional near-infrared-spectroscopy (fNIRS) provides a large spectrum of measurements to enlighten the brain structure and function. The success of dMRI is deeply rooted in the powerful concept that during their random, diffusion-driven displacements molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. Diffusion imaging opens several perspectives for what concerns the development of new non invasive techniques not only to optimize the diagnosis and therapy planning for oncological patients but also to discover the anatomical structure of the human cortex.Though, many issues still remains to be solved. Among the most striking are the reconstruction of the ODF (orientation distribution function) in noisy conditions, its reproducibility over time points acquisitions, the intra and inter-subject registration and the integration of functional information about the cortical activity within the reconstruction of the fiber network from raw data. This is of paramount importance as it would allow to link the functional information to the structural anatomical substrate. This thesis aims at investigating a subset of such issues in order to trace the path to the overall solution. In particular, it aims at integrating multiscale space-scale processing, diffusion imaging and cortical signals to (i) improve the orientation diffusion function (ODF) reconstruction, reproducibility and robustness to noise; (ii) contribute new methods for the registration of intra and inter-modality multidimensional data (tensors, probability distributions); (iii) explore the possibility of integrating functional signals in the processing pipeline in order to guide the fiber reconstruction and as a potential mean of validation of the proposed methods.From the clinical point of view, the goal of this thesis is to make tractography exploitable in daily practice for surgical planning and follow-up, assessment of degenerative pathologies as well as of pharmacological treatments.

本研究的目的是探討長期氯胺酮使用者腦灰質容量減少與腦功能連接異常模式，以及評估腦部上述異常與認知損害的相關性。
181名受試者自2011年10月至2015年7月参加了本研究。他們分為2組：氯胺酮使用者組 （124名）和健康對照組 （57名）。所有受試者均完成了包括自評問卷篩查和面談的精神狀況評估，以及一套詳細的認知測試。該測試涵蓋一般智慧、詞語記憶、視覺記憶、執行功能和語言。然後他們均會接受腦部磁力共振掃描檢查。所獲得的結構磁共振和靜息態功能磁共振資料分別通過感興趣區域技術和獨立成分分析技術進行分析。
在氯胺酮使用者組中很多人曾經用過其他活性物質，如可卡因和大麻。在他們中，25%被診斷有抑鬱障礙，15.3%被診斷有焦慮症。氯胺酮使用者組在一般智慧、詞語記憶、視覺記憶和工作記憶、執行功能方面的測試表現也差於健康對照組。
氯胺酮使用者組右側眶額葉、右內側前額葉、左側蒼白球、左側海馬、右側伏隔核的灰質體積小於對照組，而左側尾狀核的體積則大於對照組。在氯胺酮使用者組中，右側眶額葉、右內側前額葉、右側伏隔核灰質體積與氯胺酮成癮嚴重程度呈負相關關係。右側眶額葉、右內側前額葉、左側尾狀核、左側蒼白球、左側海馬、和右側伏隔核也與認知測試表現呈相關關係。
與健康對照組相比，氯胺酮使用組靜息態功能網路連接在右側前額眶內部分、左側前扣帶回和半扣帶回部分、右側顳上回和雙側小腦vermic葉VI下降，而在左側枕中回增強。
本研究顯示長期氯胺酮使用對大腦損害有影像學依據。長期氯胺酮使用與特定腦區灰質體積下降、靜息態腦功能網絡連接改變有關。上述腦結構和腦功能異常可能也是氯胺酮使用與認知功能失調的精神病理學機制。氯胺酮所致的這些大腦結構及功能變化是否可逆轉尚需要縱向或前瞻性的研究來證實。
The objectives of this study were to explore the pattern of grey matter volume reduction and functional connectivity abnormalities in the brain, and to assess the correlations between these brain abnormalities and cognitive impairment in chronic ketamine users.
One hundred and eighty-one participants took part in this study from October 2011 to July 2015. They were divided into two groups: ketamine users (n = 124) and healthy controls (n = 57). Each participant completed self-rated questionnaires and face-to-face interviews for psychiatric assessment, and took a detailed cognitive battery test that covered general intelligence, verbal and visual memory, executive function and language. All of the participants then underwent a magnetic resonance imaging (MRI) scan of the brain. The acquired structural MRI data and resting-state functional MRI data were analysed by region of interest technique and independent component analysis, respectively.
Many ketamine users used other substances, such as cocaine and cannabis. In the ketamine users group, 25% were diagnosed with a mood disorder and 15.3% with an anxiety disorder. The ketamine users performed worse than the healthy control group in tests of general intelligence, verbal memory, visual memory and working memory and executive function.
Grey matter volume was reduced in the right orbitofrontal cortex, right medial prefrontal cortex, left globus pallidus, left hippocampus, and right nucleus accumbens in the ketamine users group. In contrast, grey matter volume in the left caudate was larger in the ketamine users group than in the healthy control group. The grey matter volumes of the right orbitofrontal cortex, right medial prefrontal cortex, and right nucleus accumbens were negatively correlated with the severity of ketamine dependence. The right orbitofrontal cortex, right medial prefrontal cortex, left caudate, left globus pallidus, left hippocampus, and right nucleus accumbens volumes were also correlated with performance in the cognitive tests.
The ketamine users group showed significantly decreased functional connectivity of the default mode network in the orbital part of the right inferior frontal gyrus, left anterior cingulate and paracingulate gyri, right superior temporal gyrus and bilateral vermic lobule VI of cerebellum; and increased functional connectivity in the left middle occipital gyrus compared with the healthy control group.
This study found imaging evidence of brain damage in chronic ketamine users. Chronic ketamine use was associated with reduced grey matter volumes and altered functional connectivity of the default mode network in certain regions of the brain. These st
Lin, Yong.
Thesis Ph.D. Chinese University of Hong Kong 2016.
Includes bibliographical references (leaves ).
Abstracts also in Chinese.
Title from PDF title page (viewed on …).
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.

In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that would otherwise be computationally prohibitive without leveraging multi-resolution techniques. Specifically, the first part of this work proposes a classification framework that is being developed in collaboration with NorthEast Structural Genomics Consoritum (NESG) to assist in the automated screening of protein crystal images. Several groups have previously proposed automated algorithms to expedite such analysis. However, none of the classifiers described in the literature are sufficiently accurate or fast enough to be practical in a structural genomics production pipeline. The second part of this work proposes a 3D image registration algorithm to register regions of emphysema as quantified by densitometry on lung CT with MR lung volumes. The ability to register quantitatively-determined regions of emphysema with perfusion MRI will allow for further exploration of the pathophysiology of Chronic Obstructive Pulmonary Disorder (COPD). The registration method involves the registration of CT volumes at different levels of inspiration (total lung capacity to functional residual capacity [FRC]) followed by another registration between FRC-CT and FRC-MR volume pairs.

Nowadays, researchers are making many efforts in the medical field leading to new therapy and diagnosis methods exploiting opportunities given by nanotechnology innovation. For example, the combination of different imaging modalities can give the opportunity to obtain morphological and functional information simultaneously, providing a more accurate diagnosis. This advancement can be reached through the use of multimodal tracers and nanotechnology-based solutions allowing the simultaneous delivery of different diagnostic compounds and their safe administration for multimodal imaging acquisition. In this way is possible to protect the cargo molecules. Furthermore, a fundamental aspect is due to a proper design of the nanovectors used, correlating it with respect to the target purpose. Among different materials and processes available, nanoprecipitation is a consolidate method for polymeric nanoparticle production and its implementation in microfluidics can further improve the control over final product features accelerating its potential clinical translation. In this scenario, a Hydrodynamic Flow Focusing (HFF) approach is proposed and investigated as a production route to synthesis through a ONE-STEP process pegylated crosslinked Hyaluronic Acid NanoParticles (PEG-cHANPs). A feasibility study has been conducted to define the principal guidelines in terms of size and stability for the produced nanosystem. Based on the obtained results, a set of conditions has been elected as “gold conditions” and used in the following parts. To exploit the versatility of our microfluidic (µF) platform, the ONE-STEP process has been implemented to generate more complex structures with differents loaded agent. First, we have demonstrated that a homogeneous population of NPs with an average size of 140 nm is obtained and Gadolinium-based contrast agent (Gd-DTPA CA) and ATTO488 compounds are co-encapsulated simultaneously during the ONE-STEP process The results showed that the obtained architectures can be used as multimodal Magnetic Resonance Imaging (MRI)/Optical imaging probe. Furthermore, in accordance with the Hydrodenticity concept, a boosting of the T1 values is obtained with respect to the free Gd-DTPA. Thereafter, we have synthesized hybrid materials combining SiO2 and HA-PEG hydrogels loaded with Gd-DTPA as new MRI probes. Pre-synthetized SiO2 NPs have been added to the solvent phase during the ONE-STEP process. In this case, silica nanoparticles act as a templating agent, interfering with the nanoprecipitation step during the HFF. Resulting hybrid nanosystems have been characterized in terms of size, morphology and T1 values. Intending to develop new probes for combining MRI/Near-Infrared Fluorescence Imaging (NIRF), we explore the possibility to co-encapsulate Gd-DTPA and Indocyanine Green (ICG) in the ONE-STEP process for PEG-cHANPs production. ICG is the only NIRF dye approved by Food and Drug Administration, but its use is restricted by its low stability in biological media. Here we report a stability study of ICG regarding its interaction with the materials involved in PEG-cHANPs production and preliminary characterization of PEG-cHANPs loaded with ICG as Reactive Oxygen Species generators. Preliminary in-vitro tests with different cells lines have been conducted to evaluate the PEG-cHANPs-Gd-DTPA-ATTO488 behaviour for biological application.

In the brain mapping case, the geodesic closest points are used as the anatomical constraints for the inter-subject non-rigid registration. The method uses the anatomical constraint in the non-rigid registration which is much more reasonable for the anatomical correspondence. The registration result shows significant improvement comparing with the iterative closest points based method.
In the third application, we use the non-rigid registration method to register the different sweeps of freehand ultrasound images. We setup a 3D freehand ultrasound imaging system to capture images of a human anatomy such as liver, prostate, brain tumor and fetus. The arbitrary scanned image slices are reconstructed and resliced into volumetric dataset. We use a B-spline based non-rigid registration method to compounding different freehand ultrasound sweeps. This technique can be used to make 3D ultrasound models of fetus and other organs.
Medical image registration is an active research area during the last two decades. The registration technique can be widely used in the applications of the computer aided surgery, brain mapping and pathological detection and analysis. With the development of the computing power, fast and accurate registration techniques have been developed into necessary tools for quantitative analysis of the medical image.
Non-rigid registration methods can be used in atlas based image segmentation, inter-subject brain image registration and 3D freehand ultrasound modeling. In one of our proposed novel segmentation methods, we interleave the segmentation and the registration processes by using the segmentation to provide the anatomical constraints for registration to improve the atlas based non-rigid registration. This updated registration can be used to improve the new segmentation. This process is repeated until a good result in segmentation is obtained.
The registration methods can be classified into rigid and non-rigid registrations according to whether the anatomy is locally deformed or not. According to the sensor by which the images are taken, the registration will be divided into mono-modal and multi-modal image registration. Since the invention of the medical imaging devices, great diversity of medical imaging sensors have been developed with different physical principles. In practice we have to face the problem of multi-modal registration. In medical image analysis, we often have to consider the images of the human anatomy with deformable characteristics. In order to achieve this goal we need to use the voxel based registration method which considers all of the voxel information of the images in matching. There are several non-rigid registration approaches. However, the variational approach of non-rigid registration can represent the registration problem into a well-posed problem with a well-founded mathematical base. In our work, we considered the forward and backward deformation functions and proposed a variational approach for a new consistent multi-modal non-rigid registration method. By this way, we will find the forward and backward transform to be close to the inverse of each other. This makes the correspondence between two images more consistent and accurate. We use both explicit and implicit difference method to solve the Euler-Lagrange equation and the results show significant improvements in the transformation inverse consistency. Although variational approach for multi-modal non-rigid registration can solve the non-rigid registration problem well, generally speaking, it is slow. The displacement of each voxel has to be calculated and the iteration time is very long since the number of the unknowns are large. Although a multi-resolution strategy can be used to speed up, the registration problem is still slow when registering large medical datasets. The 3D B-spline based method has been used as an efficient method to register medical images since only a small number of control points are used to manipulate the local deformation field. In our work, we developed a 3D B-spline based consistent multi-modal non-rigid registration method with an explicit representation of derivatives. The conventional optimization methods can be used to find the optimal parameters. We use a hierarchical B-spline refinement method to approximate the deformation function from larger to smaller scale. Since the derivatives of the cost function is represented in an explicit way, the computing is reduced. It is more efficient than directly computing the derivative of the cost function by using a numerical evaluation method. The method can be considered as a multi-grid method for solving the consistent variational registration problem. The computing speed is increased by several times. The B-spline based method needs far less iterations to converge as its number of unknowns is small.
Zhang Zhijun.
"October 2005."
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6645.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (p. 209-233).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts in English and Chinese.
School code: 1307.

Photoacoustic tomography (PAT) is a scalable imaging modality having huge potential for imaging biological samples at very high depth to resolution ratio, thereby playing pivotal role in the areas of neuroscience, cardiovascular research, tumor biology and evolution research. The crucial step in PAT is the image reconstruction or the solving the inverse problem. The reconstruction can be performed by using analytical and model-based methods. The reconstruction schemes like backprojection, filtered backprojection, time reversal, delay and sum, or Fourier-based inversion have shown potential in providing qualitative reconstructions with an advantage of having lower computational complexity, but fails in irregular geometries and limited data scenarios. Model based reconstruction involves inverting a model-matrix that is generated either using impulse response or discretizing the solution of wave equation. Inversion in limited data scenarios is difficult due to ill-conditioned nature of the problem. Therefore typically prior statistics about the image is applied in form of regularization during the inversion. The prior works have attempted to choose the regularization in an automated fashion by minimizing some error metric like residual. In contrary, other schemes were proposed to mitigate the effects of regularization by using deconvolution approach using model-resolution matrix. Another perspective of regularization lies in its ability to define the resolution characteristic in the imaging domain. The resolution characteristics are heavily influenced by factors like ultrasound transducer sensitivity field, depth dependent fluence, bandwidth of the detector, and detector position etc. This thesis work attempts to develop advanced regularization methods that were based on numerical models as well as semi-norm of the data-fidelity terms The first half of thesis proposes two regularization schemes, developed with the standard Tikhonov framework, that are spatially varying to address problems pertaining to robustness to noise characteristics in the data and non-uniform resolution arising due to limited tomographic measurement positions. Model information is utilized to perform a model-resolution based spatially varying regularization having potential to mitigate resolution concerns arising due to limited detection positions. Secondly, fidelity embedded regularization, based on orthonormality between the columns of system matrix, is studied to perform robust reconstruction without necessarily requiring the noise statistics in the acquired data. The reconstruction schemes were compared with Tikhonov and total-variation based methods using numerical simulation and in-vivo mice data. The performance of the proposed spatially varying regularization schemes were superior (with upto 2 dB SNR improvements) than the Tikhonov/total-variation based regularization. The second half of this thesis work is based on singular value decomposition (SVD) which is widely used in regularization methods to know about the filtering applied to its spectral (eigen) values of the system. The state of the art methods like Tikhonov, total variation and sparse recovery based schemes assume equal weight to all the singular values (in the data fidelity term) irrespective of the amount of noise in the data. A fractional framework was developed, wherein the singular values are weight using a fractional power. The fractional power controls the amount of damping or smoothness in the reconstructed solution. The fractional framework was implemented for Tikhonov, `1-norm and total-variation a-priori constraints. In this work, automated way of choosing the fractional power was developed. Both theoretically and with numerical experiments it was shown that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework was on-par/outperforms the standard reconstructions i.e. Tikhonov, `1-norm and total-variation on numerical simulations, experimental phantoms and in-vivo mice data using figure of merits like contrast to noise ratio (CNR) and Pearson correlation (PC).

Diffuse optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties. It has a potential to become an adjunct imaging modality for breast and brain imaging, that is capable of providing functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) tends to be non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Traditional image reconstruction methods in diffuse optical tomography employ l2 –norm based regularization, which is known to remove high frequency noises in the re-constructed images and make them appear smooth. The recovered contrast in the reconstructed image in these type of methods are typically dependent on the iterative nature of the method employed, in which the non-linear iterative technique is known to perform better in comparison to linear techniques. The usage of non-linear iterative techniques in the real-time, especially in dynamical imaging, becomes prohibitive due to the computational complexity associated with them. In the rapid dynamic diffuse optical imaging, assumption of a linear dependency in the solutions between successive frames results in a linear inverse problem. This new frame work along with the l1–norm based regularization can provide better robustness to noise and results in a better contrast recovery compared to conventional l2 –based techniques. Moreover, it is shown that the proposed l1-based technique is computationally efficient compared to its counterpart(l2 –based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames. Modern diffuse optical imaging systems are multi-modal in nature, where diffuse optical imaging is combined with traditional imaging modalities such as MRI, CT, and Ultrasound. A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in these scenarios is introduced, which is based on prior image constrained- l1 minimization scheme. This method has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the- l1 based frame work is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties. It has a potential to become an adjunct imaging modality for breast and brain imaging, that is capable of providing functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) tends to be non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Traditional image reconstruction methods in diffuse optical tomography employ l2 –norm based regularization, which is known to remove high frequency noises in the re-constructed images and make them appear smooth. The recovered contrast in the reconstructed image in these type of methods are typically dependent on the iterative nature of the method employed, in which the non-linear iterative technique is known to perform better in comparison to linear techniques. The usage of non-linear iterative techniques in the real-time, especially in dynamical imaging, becomes prohibitive due to the computational complexity associated with them. In the rapid dynamic diffuse optical imaging, assumption of a linear dependency in the solutions between successive frames results in a linear inverse problem. This new frame work along with the l1–norm based regularization can provide better robustness to noise and results in a better contrast recovery compared to conventional l2 –based techniques. Moreover, it is shown that the proposed l1-based technique is computationally efficient compared to its counterpart(l2 –based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames. Modern diffuse optical imaging systems are multi-modal in nature, where diffuse optical imaging is combined with traditional imaging modalities such as MRI, CT, and Ultrasound. A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in these scenarios is introduced, which is based on prior image constrained- l1 minimization scheme. This method has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the- l1 based frame work is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information.

Biomedical optical imaging is capable of providing functional information of the soft bi-ological tissues, whose applications include imaging large tissues, such breastand brain in-vivo. Biomedical optical imaging uses near infrared light (600nm-900nm) as the probing media, givin ganaddedadvantageofbeingnon-ionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diffuse optical tomogra-phyandphotoacoustictomography. Traditional image reconstruction methods indiffuse optical tomographyemploysa �2-norm based regularization, which is known to remove high frequency no is either econstructed images and make the mappearsmooth. Hence as parsity based image reconstruction has been deployed for diffuse optical tomography, these sparserecov-ery methods utilize the �p-norm based regularization in the estimation problem with 0≤ p<1. These sparse recovery methods, along with an approximation to utilizethe �0-norm, have been used forther econstruction of diffus eopticaltomographic images.The comparison of these methods was performed by increasing the sparsityinthesolu-tion. Further a model resolution matrix based framework was proposed and shown to in-duceblurinthe�2-norm based regularization framework for diffuse optical tomography. This model-resolution matrix framework was utilized in the optical imaged econvolution framework. A basis pursuitdeconvolution based on Split AugmentedLagrangianShrink-ageAlgorithm(SALSA)algorithm was used along with the Tikhonovregularization step making the image reconstruction into a two-step procedure. This new two-step approach was found to be robust with no iseandwasabletobetterdelineatethestructureswhichwasevaluatedusingnumericalandgelatinphantom experiments. Modern diffuse optical imaging systems are multi-modalin nature, where diffuse optical imaging is combined with traditional imaging modalitiessuc has Magnetic Res-onanceImaging(MRI),or Computed Tomography(CT). Image-guided diffuse optical tomography has the advantage of reducingthetota lnumber of optical parameters beingreconstructedtothenumber of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem fromunder-determined innaturetoover-determined. In such cases, the minimum required measurements might be farless compared to those of the traditional diffuse optical imaging. An approach to choose these measurements optimally based on a data-resolution matrix is proposed, and it is shown that it drastically reduces the minimum required measurements (typicalcaseof240to6) without compromising the image reconstruction performance. In the last part of the work , a model-based image reconstruction approaches in pho-toacoustic tomography (which combines light and ultra sound) arestudied as it is know that these methods have a distinct advantage compared to traditionalanalytical methods in limited datacase. These model-based methods deployTikhonovbasedregularizationschemetoreconstruct the initial pressure from the boundary acoustic data. Again a model-resolution for these cases tend to represent the blurinduced by the regularization scheme. A method that utilizes this blurringmodelandper forms the basis pursuit econ-volution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods. Moreover, this deconvolution including the building of model-resolution matrixis achievedvia the Lanczosbidiagonalization (least-squares QR) making this approach computationally ef-ficient and deployable inreal-time. Keywords Medical imaging, biomedical optical imaging, diffuse optical tomography, photoacous-tictomography, multi-modalimaging, inverse problems,sparse recovery,computational methods inbiomedical optical imaging.

Biomedical optical imaging is capable of providing functional information of the soft bi-ological tissues, whose applications include imaging large tissues, such breastand brain in-vivo. Biomedical optical imaging uses near infrared light (600nm-900nm) as the probing media, givin ganaddedadvantageofbeingnon-ionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diffuse optical tomogra-phyandphotoacoustictomography. Traditional image reconstruction methods indiffuse optical tomographyemploysa �2-norm based regularization, which is known to remove high frequency no is either econstructed images and make the mappearsmooth. Hence as parsity based image reconstruction has been deployed for diffuse optical tomography, these sparserecov-ery methods utilize the �p-norm based regularization in the estimation problem with 0≤ p<1. These sparse recovery methods, along with an approximation to utilizethe �0-norm, have been used forther econstruction of diffus eopticaltomographic images.The comparison of these methods was performed by increasing the sparsityinthesolu-tion. Further a model resolution matrix based framework was proposed and shown to in-duceblurinthe�2-norm based regularization framework for diffuse optical tomography. This model-resolution matrix framework was utilized in the optical imaged econvolution framework. A basis pursuitdeconvolution based on Split AugmentedLagrangianShrink-ageAlgorithm(SALSA)algorithm was used along with the Tikhonovregularization step making the image reconstruction into a two-step procedure. This new two-step approach was found to be robust with no iseandwasabletobetterdelineatethestructureswhichwasevaluatedusingnumericalandgelatinphantom experiments. Modern diffuse optical imaging systems are multi-modalin nature, where diffuse optical imaging is combined with traditional imaging modalitiessuc has Magnetic Res-onanceImaging(MRI),or Computed Tomography(CT). Image-guided diffuse optical tomography has the advantage of reducingthetota lnumber of optical parameters beingreconstructedtothenumber of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem fromunder-determined innaturetoover-determined. In such cases, the minimum required measurements might be farless compared to those of the traditional diffuse optical imaging. An approach to choose these measurements optimally based on a data-resolution matrix is proposed, and it is shown that it drastically reduces the minimum required measurements (typicalcaseof240to6) without compromising the image reconstruction performance. In the last part of the work , a model-based image reconstruction approaches in pho-toacoustic tomography (which combines light and ultra sound) arestudied as it is know that these methods have a distinct advantage compared to traditionalanalytical methods in limited datacase. These model-based methods deployTikhonovbasedregularizationschemetoreconstruct the initial pressure from the boundary acoustic data. Again a model-resolution for these cases tend to represent the blurinduced by the regularization scheme. A method that utilizes this blurringmodelandper forms the basis pursuit econ-volution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods. Moreover, this deconvolution including the building of model-resolution matrixis achievedvia the Lanczosbidiagonalization (least-squares QR) making this approach computationally ef-ficient and deployable inreal-time. Keywords Medical imaging, biomedical optical imaging, diffuse optical tomography, photoacous-tictomography, multi-modalimaging, inverse problems,sparse recovery,computational methods inbiomedical optical imaging.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.