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Al-Abdul, Salam Amal. "Image quality in MRI." Thesis, University of Exeter, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288250.
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Cui, Xuelin. "Joint CT-MRI Image Reconstruction." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86177.
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Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging
techniques. In return, medical images are required to provide more accurate and detailed
information than ever. Aside from the evolution of hardware and software, multimodal
imaging techniques offer a promising solution to produce higher quality images by fusing
medical images from different modalities. This strategy utilizes more structural and/or
functional image information, thereby allowing clinical results to be more comprehensive
and better interpreted.
Since their inception, multimodal imaging techniques have received a great deal of attention
for achieving enhanced imaging performance. In this work, a novel joint reconstruction
framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI)
data is developed and evaluated. The method proposed in this study is part of the planned
joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The
CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI
platform. However, since their image data are highly undersampled, analytical methods, such
as filtered backprojection, are unable to generate images of sufficient quality. To overcome
this drawback, we resort to compressed sensing techniques, which employ sparse priors that
result from an application of L1-norm minimization.
To utilize multimodal information, a projection distance is introduced and is tuned to tailor
the texture and pattern of final images. Specifically CT and MRI images are alternately
reconstructed using the updated multimodal results that are calculated at the latest step
of the iterative optimization algorithm. This method exploits the structural similarities
shared by the CT and MRI images to achieve better reconstruction quality. The improved
performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI
body images and a pair of undersampled CT-MRI head images. These images are
tested using joint reconstruction, analytical reconstruction, and independent reconstruction
without using multimodal imaging information. Results show that the proposed method
improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity
measurements compared to independent reconstruction methods. It offers a similar quality
as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and
a 30% sampling rate for MRI. It is concluded that structural similarities and correlations
residing in images from different modalities are useful to mutually promote the quality of
image reconstruction.Ph. D.
Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.
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Carmo, Bernardo S. "Image processing in echography and MRI." Thesis, University of Southampton, 2005. https://eprints.soton.ac.uk/194557/.
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This work deals with image processing for three medical imaging applications: speckle detection in 3D ultrasound, left ventricle detection in cardiac magnetic resonance imaging (MRI) and flow feature visualisation in velocity MRI. For speckle detection, a learning from data approach was taken using pattern recognition principles and low-level image features, including signal-to-noise ratio, co-occurrence matrix, asymmetric second moment, homodyned k-distribution and a proposed specklet detector. For left ventricle detection, template matching was used. Forvortex detection, a data processing framework is presented that consists of three main steps: restoration, abstraction and tracking. This thesis addresses the first two problems, implementing restoration with a total variation first order Lagrangian method, and abstraction with clustering and local linear expansion.
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Gu, Wei Q. "Automated tracer-independent MRI/PET image registration." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29596.pdf.
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Ivarsson, Magnus. "Evaluation of 3D MRI Image Registration Methods." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139075.
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Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within thefield of medical IT. The purpose could be to detect image variations, pathologicaldevelopment or in the company AMRA’s case, to quantify fat tissue in variousparts of the human body.From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image isobtained. Currently, AMRA is using the Morphon algorithm to register and segment the water image in order to quantify fat and muscle tissue. During the firstpart of this master thesis, two alternative registration methods were evaluated.The first algorithm was Free Form Deformation which is a non-linear parametricbased method. The second algorithm was a non-parametric optical flow basedmethod known as the Demon algorithm. During the second part of the thesis,the Demon algorithm was used to evaluate the effect of using the fat images forregistrations.
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Lin, Xiangbo. "Knowledge-based image segmentation using deformable registration: application to brain MRI images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001121.pdf.
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L'objectif de la thèse est de contribuer au recalage élastique d'images médicales intersujet-intramodalité, ainsi qu’à la segmentation d'images 3D IRM du cerveau dans le cas normal. L’algorithme des démons qui utilise les intensités des images pour le recalage est d’abord étudié. Une version améliorée est proposée en introduisant une nouvelle équation de calcul des forces pour résoudre des problèmes de recalages dans certaines régions difficiles. L'efficacité de la méthode est montrée sur plusieurs évaluations à partir de données simulées et réelles. Pour le recalage intersujet, une méthode originale de normalisation unifiant les informations spatiales et des intensités est proposée. Des contraintes topologiques sont introduites dans le modèle de déformation, visant à obtenir un recalage homéomorphique. La proposition est de corriger les points de déplacements ayant des déterminants jacobiens négatifs. Basée sur le recalage, une segmentation des structures internes est étudiée. Le principe est de construire une ontologie modélisant le connaissance a-priori de la forme des structures internes. Les formes sont représentées par une carte de distance unifiée calculée à partir de l'atlas de référence et celui déformé. Cette connaissance est injectée dans la mesure de similarité de la fonction de coût de l'algorithme. Un paramètre permet de balancer les contributions des mesures d'intensités et de formes. L'influence des différents paramètres de la méthode et des comparaisons avec d'autres méthodes de recalage ont été effectuées. De très bon résultats sont obtenus sur la segmentation des différentes structures internes du cerveau telles que les noyaux centraux et hippocampeThe research goal of this thesis is a contribution to the intra-modality inter-subject non-rigid medical image registration and the segmentation of 3D brain MRI images in normal case. The well-known Demons non-rigid algorithm is studied, where the image intensities are used as matching features. A new force computation equation is proposed to solve the mismatch problem in some regions. The efficiency is shown through numerous evaluations on simulated and real data. For intensity based inter-subject registration, normalizing the image intensities is important for satisfying the intensity correspondence requirements. A non-rigid registration method combining both intensity and spatial normalizations is proposed. Topology constraints are introduced in the deformable model to preserve an expected property in homeomorphic targets registration. The solution comes from the correction of displacement points with negative Jacobian determinants. Based on the registration, a segmentation method of the internal brain structures is studied. The basic principle is represented by ontology of prior shape knowledge of target internal structure. The shapes are represented by a unified distance map computed from the atlas and the deformed atlas, and then integrated into the similarity metric of the cost function. A balance parameter is used to adjust the contributions of the intensity and shape measures. The influence of different parameters of the method and comparisons with other registration methods were performed. Very good results are obtained on the segmentation of different internal structures of the brain such as central nuclei and hippocampus
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Soltaninejad, Mohammadreza. "Supervised learning-based multimodal MRI brain image analysis." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/30883/.
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Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving accuracy of tumour segmentation is an ongoing field. The aim of this thesis is to develop a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from multimodal MRI images. In this thesis, firstly, the whole brain tumour is segmented from fluid attenuated inversion recovery (FLAIR) MRI, which is commonly acquired in clinics. The segmentation is achieved using region-wise classification, in which regions are derived from superpixels. Several image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomised trees (ERT) classifies each superpixel into tumour and non-tumour. Secondly, the method is extended to 3D supervoxel based learning for segmentation and classification of tumour tissue subtypes in multimodal MRI brain images. Supervoxels are generated using the information across the multimodal MRI data set. This is then followed by a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The information from the advanced protocols of diffusion tensor imaging (DTI), i.e. isotropic (p) and anisotropic (q) components is also incorporated to the conventional MRI to improve segmentation accuracy. Thirdly, to further improve the segmentation of tumour tissue subtypes, the machine-learned features from fully convolutional neural network (FCN) are investigated and combined with hand-designed texton features to encode global information and local dependencies into feature representation. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The machine-learned features, along with hand-designed texton features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumour. The methods are evaluated on two datasets: 1) clinical dataset, and 2) publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2013 and 2017 dataset. The experimental results demonstrate the high detection and segmentation performance of the III single modal (FLAIR) method. The average detection sensitivity, balanced error rate (BER) and the Dice overlap measure for the segmented tumour against the ground truth for the clinical data are 89.48%, 6% and 0.91, respectively; whilst, for the BRATS dataset, the corresponding evaluation results are 88.09%, 6% and 0.88, respectively. The corresponding results for the tumour (including tumour core and oedema) in the case of multimodal MRI method are 86%, 7%, 0.84, for the clinical dataset and 96%, 2% and 0.89 for the BRATS 2013 dataset. The results of the FCN based method show that the application of the RF classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth for the BRATS 2013 dataset is 0.88, 0.80 and 0.73 for complete tumor, core and enhancing tumor, respectively, which is competitive to the state-of-the-art methods. The corresponding results for BRATS 2017 dataset are 0.86, 0.78 and 0.66 respectively. The methods demonstrate promising results in the segmentation of brain tumours. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. In the experiments, texton has demonstrated its advantages of providing significant information to distinguish various patterns in both 2D and 3D spaces. The segmentation accuracy has also been largely increased by fusing information from multimodal MRI images. Moreover, a unified framework is present which complementarily integrates hand-designed features with machine-learned features to produce more accurate segmentation. The hand-designed features from shallow network (with designable filters) encode the prior-knowledge and context while the machine-learned features from a deep network (with trainable filters) learn the intrinsic features. Both global and local information are combined using these two types of networks that improve the segmentation accuracy.
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Daga, P. "Towards efficient neurosurgery : image analysis for interventional MRI." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1449559/.
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Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions.
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Chi, Wenjun. "MRI image analysis for abdominal and pelvic endometriosis." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:27efaa89-85cd-4f8b-ab67-b786986c42e3.
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Endometriosis is an oestrogen-dependent gynaecological condition defined as the presence of endometrial tissue outside the uterus cavity. The condition is predominantly found in women in their reproductive years, and associated with significant pelvic and abdominal chronic pain and infertility. The disease is believed to affect approximately 33% of women by a recent study. Currently, surgical intervention, often laparoscopic surgery, is the gold standard for diagnosing the disease and it remains an effective and common treatment method for all stages of endometriosis. Magnetic resonance imaging (MRI) of the patient is performed before surgery in order to locate any endometriosis lesions and to determine whether a multidisciplinary surgical team meeting is required. In this dissertation, our goal is to use image processing techniques to aid surgical planning. Specifically, we aim to improve quality of the existing images, and to automatically detect bladder endometriosis lesion in MR images as a form of bladder wall thickening. One of the main problems posed by abdominal MRI is the sparse anisotropic frequency sampling process. As a consequence, the resulting images consist of thick slices and have gaps between those slices. We have devised a method to fuse multi-view MRI consisting of axial/transverse, sagittal and coronal scans, in an attempt to restore an isotropic densely sampled frequency plane of the fused image. In addition, the proposed fusion method is steerable and is able to fuse component images in any orientation. To achieve this, we apply the Riesz transform for image decomposition and reconstruction in the frequency domain, and we propose an adaptive fusion rule to fuse multiple Riesz-components of images in different orientations. The adaptive fusion is parameterised and switches between combining frequency components via the mean and maximum rule, which is effectively a trade-off between smoothing the intrinsically noisy images while retaining the sharp delineation of features. We first validate the method using simulated images, and compare it with another fusion scheme using the discrete wavelet transform. The results show that the proposed method is better in both accuracy and computational time. Improvements of fused clinical images against unfused raw images are also illustrated. For the segmentation of the bladder wall, we investigate the level set approach. While the traditional gradient based feature detection is prone to intensity non-uniformity, we present a novel way to compute phase congruency as a reliable feature representation. In order to avoid the phase wrapping problem with inverse trigonometric functions, we devise a mathematically elegant and efficient way to combine multi-scale image features via geometric algebra. As opposed to the original phase congruency, the proposed method is more robust against noise and hence more suitable for clinical data. To address the practical issues in segmenting the bladder wall, we suggest two coupled level set frameworks to utilise information in two different MRI sequences of the same patients - the T2- and T1-weighted image. The results demonstrate a dramatic decrease in the number of failed segmentations done using a single kind of image. The resulting automated segmentations are finally validated by comparing to manual segmentations done in 2D.
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Hagio, Tomoe, and Tomoe Hagio. "Parametric Mapping and Image Analysis in Breast MRI." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621809.
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Breast cancer is the most common and the second most fatal cancer among women in the U.S. Current knowledge indicates that there is a relationship between high breast density (measured by mammography) and increased breast cancer risk. However, the biology behind this relationship is not well understood. This may be due to the limited information provided by mammography which only yields information on the relative amount of fibroglandular to adipose tissue in the breast. In our studies, breast density is assessed using quantitative MRI, in which MRI-based tissue-dependent parameters are derived voxel-wise by mathematically modeling the acquired MRI signals. Specifically, we use data from a radial gradient- and spin-echo imaging technique, previously developed in our group, to assess fat fraction and T₂ of the water component in relation to breast density. In addition, we use diffusion-weighted imaging to obtain another parameter, apparent diffusion coefficient (ADC) of the water component in the breast. Each parametric map provides a different type of information: fat fraction gives the amount of fat present in the voxel, the T₂ of water spin relaxation is sensitive to the water component in the tissue, and the ADC of water yields other type of information, such as tissue cellularity. The challenge in deriving these parameters from breast MRI data is the presence of abundant fat in the breast, which can cause artifacts in the images and can also affect the parameter estimation. We approached this problem by modifying the imaging sequence (as in the case of diffusion-weighted imaging) and by exploring new signal models that describe the MRI signal accounting for the presence of fat. In this work, we present the improvements made in the imaging sequence and in the parametric mapping algorithms using simulation and phantom experiments. We also present preliminary results in vivo in the context of breast density-related tissue characterization.
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Wang, Kang. "Image Transfer Between Magnetic Resonance Images and Speech Diagrams." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41533.
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Realtime Magnetic Resonance Imaging (MRI) is a method used for human
anatomical study. MRIs give exceptionally detailed information about soft-tissue
structures, such as tongues, that other current imaging techniques cannot achieve.
However, the process requires special equipment and is expensive. Hence, it is not quite
suitable for all patients.
Speech diagrams show the side view positions of organs like the tongue, throat,
and lip of a speaking or singing person. The process of making a speech diagram is like
the semantic segmentation of an MRI, which focuses on the selected edge structure.
Speech diagrams are easy to understand with a clear speech diagram of the tongue and
inside mouth structure. However, it often requires manual annotation on the MRI
machine by an expert in the field.
By using machine learning methods, we achieved transferring images between
MRI and speech diagrams in two directions. We first matched videos of speech diagram
and tongue MRIs. Then we used various image processing methods and data
augmentation methods to make the paired images easy to train. We built our network
model inspired by different cross-domain image transfer methods and applied
reference-based super-resolution methods—to generate high-resolution images. Thus,
we can do the transferring work through our network instead of manually. Also,
generated speech diagram can work as an intermediary part to be transferred to other
medical images like computerized tomography (CT), since it is simpler in structure
compared to an MRI.
We conducted experiments using both the data from our database and other MRI
video sources. We use multiple methods to do the evaluation and comparisons with
several related methods show the superiority of our approach.
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Dale, Brian M. "Optimal Design of MR Image Acquisition Techniques." Case Western Reserve University School of Graduate Studies / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=case1081556784.
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McGraw, Tim E. "Neuronal fiber tracking in DT-MRI." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000573.
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Thayer, David A. "Imaging Techniques and Hardware for Inhomogeneous MRI." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd535.pdf.
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Stacke, Karin. "Automatic Brain Segmentation into Substructures Using Quantitative MRI." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128900.
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Segmentation of the brain into sub-volumes has many clinical applications. Manyneurological diseases are connected with brain atrophy (tissue loss). By dividingthe brain into smaller compartments, volume comparison between the compartmentscan be made, as well as monitoring local volume changes over time. Theformer is especially interesting for the left and right cerebral hemispheres, dueto their symmetric appearance. By using automatic segmentation, the time consumingstep of manually labelling the brain is removed, allowing for larger scaleresearch.In this thesis, three automatic methods for segmenting the brain from magneticresonance (MR) images are implemented and evaluated. Since neither ofthe evaluated methods resulted in sufficiently good segmentations to be clinicallyrelevant, a novel segmentation method, called SB-GC (shape bottleneck detectionincorporated in graph cuts), is also presented. SB-GC utilizes quantitative MRIdata as input data, together with shape bottleneck detection and graph cuts tosegment the brain into the left and right cerebral hemispheres, the cerebellumand the brain stem. SB-GC shows promises of highly accurate and repeatable resultsfor both healthy, adult brains and more challenging cases such as childrenand brains containing pathologies.
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Lethmate, Ralf. "Novel radial scan strategies and image reconstruction in MRI." Lyon 1, 2001. http://www.theses.fr/2001LYO10272.
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Récemment, un fort regain d'intérêt pour les techniques d'échantillonnage radia en Imagerie de Résonance Magnétique (IRM) est perceptible. Elles permettent d'imager des objets ayant des temps de relaxation transversal très courts et sont peu sensibles aux mouvements. Nous proposons dans ce travail 1) de nouvelles stratégies d'échantillonnage radial 2D/3D et 2) des algorithmes avancés de reconstruction d'image IRM, tels que la techniques de "gridding" utilisant des compensations de densité original, les méthodes Bayésiennes et BURS. Ces algorithmes constituent une avancée considérable dans le monde IRM puisque la reconstruction d'image est possible à partir de tout échantillonnage. Pour augmenter l'intensité du signal, il est avantageux de l'échantillonner dès la montée des gradients, les positions des échantillons diffèrent alors des positions idéales des distributions utilisées (Projection Reconstruction (PR-2D) et linogramme). Or, la reconstruction de l'image nécessite la connaissance précise de ces dernières, qui peuvent être estimées grâce à une expérience préliminaire ou une approche fondée sur la transformation de Gabor. En imagerie 3D, nous proposons cinq équidistributions isotropes que nous comparons à la technique PR-3D, qui souffre d'un sur-échantillonnage excessif sur les pôles. Nous avons mis l'accent sur la qualité d'image, la facilité d'implantation sur l'imageur et els temps d'acquisition qui peuvent ainsi être réduits de 30%. À notre connaissance, ces équidistributions n'ont jamais été appliquées à l'IRM auparavant. Nous proposons également une nouvelle méthode d'imagerie dynamique 3D, prometteuse pour l'angiographie, l'imagerie de perfusion, etc. Elle est fondée sur ces équidistributions et utilise une nouvelle approche "keyhole-sphérique". Tout en ajoutant la dimension temporelle, le temps d'acquisition reste identique à celui d'une acquisition radiale 3D classique. Les résultats sont présentés pour des pseudo-données et des données réelles.
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Williams, Glenda Patricia. "Development and clinical application of techniques for the image processing and registration of serially acquired medical images." Thesis, University of South Wales, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326718.
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Maitree, Rapeepan, Gloria J. Guzman Perez-Carrillo, Joshua S. Shimony, H. Michael Gach, Anupama Chundury, Michael Roach, H. Harold Li, and Deshan Yang. "Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI." SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2017. http://hdl.handle.net/10150/626083.
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Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i. e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i. e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
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McGraw, Tim E. "Denoising, segmentation and visualization of diffusion weighted MRI." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011618.
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Sjölund, Jens. "MRI based radiotherapy planning and pulse sequence optimization." Licentiate thesis, Linköpings universitet, Medicinsk informatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115796.
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Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.
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Wilm, Bertram Jakob. "Diffusion-weighted MRI : volume selection, field monitoring and image reconstruction /." [S.l.] : [s.n.], 2009. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=18318.
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Chandrashekara, Raghavendra. "Analysis of cardiac motion using MRI and nonrigid image registration." Thesis, Imperial College London, 2005. http://hdl.handle.net/10044/1/11456.
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Suh, Doug Young. "Knowledge-based boundary detection system : on MRI cardiac image sequences." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13291.
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Callaghan, Martina. "PadeÌ methods for image reconstruction and feature extraction in MRI." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416865.
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Shen, Shan. "MRI brain tumour classification using image processing and data mining." Thesis, University of Strathclyde, 2004. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21543.
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Detecting and diagnosing brain tumour types quickly and accurately is essential to any effective treatment. The general brain tumour diagnosis procedure, biopsy, not only causes a great deal of pain to the patient but also raises operational difficulty to the clinician. In this thesis, a non-invasive brain tumour diagnosis system based on MR images is proposed. The first part is image preprocessing applied to original MR images from the hospital. Non-uniformed intensity scales of MR images are standardized relying on their statistic characteristics without requiring prior or post templates. It is followed by a non-brain region removal process using morphologic operations and a contrast enhancement between white matter and grey matter by means of histogram equalization. The second part is image segmentation applied to preprocessed MR images. A new image segmentation algorithm named IFCM is developed based on the traditional FCM algorithm. Neighbourhood attractions considered in IFCM enable this new algorithm insensitive to noise, while a neural network model is designed to determine optimized degrees of attractions. This extension can also estimate inhomogenities. Brain tissue intensities are acquired from segmentation. The final part of the system is brain tumour classification. It extracts hidden diagnosis information from brain tissue intensities using a fuzzy logic based GP algorithm. This novel method imports a fuzzy membership to implement a multi-class classification directly without converting it into several binary classification problems as with most other methods. Two fitness functions are defined to describe the features of medical data precisely. The superiority of image analysis methods in each part was demonstrated on synthetic images and real MR images. Classification rules of three types and two grades of brain tumours were discovered. The final diagnosis accuracy was very promising. The feasibility and capability of the non-invasive diagnosis system were testified comprehensively.
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Zou, Xin. "Compression and Decompression of Color MRI Image by Huffman Coding." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17029.
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MRI image (Magnetic Resonance Imaging) as a universal body checkup method in modern medicine. It can help doctors to analyze the condition of patients as soon as possible. As the medical images, the MRI images have high quality and a large amount of data, which requires more transmission time and larger storage capacity. To reduce transmission time and storage capacity, the compression and decompression technology is applied. Now most MRI images are colour, but most theses still use gray MRI images to research. Compressed color MRI images is a new research area. In this thesis, some basic theories of the compression technoloy and medical technology were firstly introduced, then basic strcture and kernel algorithm of Huffman coding were explained in detail. Finally, Huffman coding was implemented in MATLAB to compress and decompress the colour MRI images.The result of the experiment shows that the Huffman coding in colour MRI image compression can get high compression ratio and coding efficient.
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Song, Yang. "Semi-Automatic Registration Utility for MR Brain Imaging of Small Animals." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/148.
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The advancements in medical technologies have allowed more accurate diagnosis and quantitative assessments. Magnetic Resonance Imaging is one of the most effective and critical technologies in modern diagnosis. However, preprocessing tasks are required to perform various research topics basing on MR image. Registration is one of the those preprocessing tasks. In this research, a semi-automatic utility was developed for doing MRI registration of small animals. It focuses on 2D rigid body registration. The test results show that this developed utility can perform registration well for MRI of small animals in both intra-subject and inter-subjects.
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Mahbod, Amirreza. "Structural Brain MRI Segmentation Using Machine Learning Technique." Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189985.
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Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.
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Larsson, Jonatan. "Implementation and evaluation of motion correction for quantitative MRI." Thesis, Linköpings universitet, Medicinsk informatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-61331.
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Image registration is the process of aligning two images such that their mutual features overlap. This is of great importance in several medical applications. In 2008 a novel method for simultaneous T1, T2 and proton density quantification was suggested. The method is in the field of quantitative Magnetic Resonance Imaging or qMRI. In qMRI parameters are quantified by a pixel-to-pixel fit of the image intensity as a function of different MR scanner settings. The quantification depends on several volumes of different intensities to be aligned. If a patient moves during the data aquisition the datasets will not be aligned and the results are degraded due to this. Since the quantification takes several minutes there is a considerable risk of patient movements. In this master thesis three image registration methods are presented and a comparison in robustness and speed was made. The phase based algorithm was suited for this problem and limited to finding rigid motion. The other two registration algorithms, originating from the Statistical Parametrical Mapping, SPM, package, were used as references. The result shows that the pixel-to-pixel fit is greatly improved in the datasets with found motion. In the comparison between the different methods the phase based algorithm turned out to be both the fastest and the most robust method.
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Ming, Kevin. "Towards in vitro MRI based analysis of spinal cord injury." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2290.
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A novel approach for the analysis of spinal cord deformation based on a combined technique of non-invasive imaging and medical image processing is presented. A sopposed to traditional approaches where animal spinal cords are exposed and directly subjected to mechanical impact in order to be examined, this approach can be used to quantify deformities of the spinal cord in vivo, so that deformations — specifically those of myelopathy-related sustained compression — of the spinal cord can be computed in its original physiological environment. This, then, allows for a more accurate understanding of spinal cord deformations and injuries.
Images of rat spinal cord deformations, acquired using magnetic resonance imaging (MRI), were analyzed using a combination of various image processing methods, including image segmentation, a versor-based rigid registration technique, and a B-spline-based non-rigid registration technique. To verify the validity and assess the accuracy of this approach, several validation schemes were implemented to compare the deformation fields computed by the proposed algorithm against known deformation fields. First, validation was performed on a synthetically-generated spinal cord model data warped using synthetic deformations; error levels achieved were consistently below 6% with respect to cord width, even for large degrees of deformation up to half of the dorsal-ventral width of the cord (50% deflection). Then, accuracy was established using in vivo rat spinal cord images warped using those same synthetic deformations; error levels achieved were also consistently below 6% with respect to cord width, in this case for large degrees of deformation up to the entire dorsal-ventral width of the cord (100% deflection). Finally, the accuracy was assessed using data from the Visible Human Project (VHP) warped using simulated deformations obtained from finite element (FE) analysis of the spinal cord; error levels achieved were as low as 3.9% with respect to cord width.
This in vivo, non-invasive semi-automated analysis tool provides a new framework through which the causes, mechanisms, and tolerance parameters of myelopathy-related sustained spinal cord compression, as well as the measures used in neuroprotection and regeneration of spinal cord tissue, can be prospectively derived in a manner that ensures the bio-fidelity of the cord.
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Joos, Louis. "Deformable 3D Brain MRI Registration with Deep Learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262852.
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Traditional deformable registration methods have achieved impressive performances but are computationally time-consuming since they have to optimize an objective function for each new pair of images. Very recently some learning-based approaches have been proposed to enable fast registration by learning to estimate the spatial transformation parameters directly from the input images. Here we present a method for 3D fast pairwise registration of brain MR images. We model the deformation function with B-splines and learn the optimal control points using a U-Net like CNN architecture. An inverse-consistency loss has been used to enforce diffeomorphicity of the deformation. The proposed algorithm does not require supervised information such as segmented labels but some can be used to help the registration process. We also implemented several strategies to account for the multi-resolution nature of the problem. The method has been evaluated on MICCAI 2012 brain MRI datasets, and evaluated on both similarity and invertibility of the computed transformation.
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Jonsson, Joakim. "Integration of MRI into the radiotherapy workflow." Doctoral thesis, Umeå universitet, Radiofysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-68959.
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The modern day radiotherapy treatments are almost exclusively based on computed tomography (CT) images. The CT images are acquired using x-rays, and therefore reflect the radiation interaction properties of the material. This information is used to perform accurate dose calculation by the treatment planning system, and the data is also well suited for creating digitally reconstructed radiographs for comparing patient set up at the treatment machine where x-ray images are routinely acquired for this purpose. The magnetic resonance (MR) scanner has many attractive features for radiotherapy purposes. The soft tissue contrast as compared to CT is far superior, and it is possible to vary the sequences in order to visualize different anatomical and physiological properties of an organ. Both of these properties may contribute to an increase in accuracy of radiotherapy treatment. Using the MR images by themselves for treatment planning is, however, problematic. MR data reflects the magnetic properties of protons, and thus have no connection to the radiointeraction properties of the material. MRI also has inherent difficulty in imaging bone, which will appear in images as areas of no signal similar to air. This makes both dose calculation and patient positioning at the treatment machine troublesome. There are several clinics that use MR images together with CT images to perform treatment planning. The images are registered to a common coordinate system, a process often described as image fusion. In these cases, the MR images are primarily used for target definition and the CT images are used for dose calculations. This method is now not ideal, however, since the image fusion may introduce systematic uncertainties into the treatment due to the fact that the tumor is often able to move relatively freely with respect to the patients’ bony anatomy and outer contour, especially when the image registration algorithms take the entire patient anatomy in the volume of interest into account. The work presented in the thesis “Integration of MRI into the radiotherapy workflow” aim towards investigating the possibilities of workflows based entirely on MRI without using image registration, as well as workflows using image registration methods that are better suited for targets that can move with respect to surrounding bony anatomy, such as the prostate.Modern strålterapi av cancer baseras nästan helt på datortomografiska (CT) bilder. CT bilder tas med hjälp av röntgenfotoner, och återger därför hur det avbildade materialet växelverkar med strålning. Denna information används för att utföra noggranna dosberäkningar i ett dosplaneringssystem, och data från CT bilder lämpar sig också väl för att skapa digitalt rekonstruerade röntgenbilder vilka kan användas för att verifiera patientens position vid behandling. Bildgivande magnetresonanstomografi (MRI) har många egenskaper som är intressanta för radioterapi. Mjukdelskontrasten i MR bilder är överlägsen CT, och det är möjligt att i stor utstäckning variera sekvensparametrar för att synliggöra olika anatomiska och funktionella attribut hos ett organ. Dessa bägge egenskaper kan bidra till ökad noggrannhet i strålbehandling av cancer. Att använda enbart MR bilder som planeringsunderlag för radioterapi är dock problematiskt. MR data reflekterar magnetiska attribut hos protoner, och har därför ingen koppling till materialets egenskaper då det gäller strålningsväxelverkan. Dessutom är det komplicerat att avbilda ben med MR; ben uppträder som områden av signalförlust i bilderna, på samma sätt som luft gör. Detta gör det svårt att utföra noggranna dosberäkningar och positionera patienten vid behandling. Många moderna kliniker använder redan idag MR tillsammans med CT under dosplanering. Bilderna registreras till ett gemensamt koordinatsystem i en process som kallas bildfusion. I dessa fall används MR bilderna primärt som underlag för utlinjering av tumör, eller target, och CT bilderna används som grund för dosberäkningar. Denna metod är dock inte ideal, då bildregistreringen kan införa systematiska geometriska fel i behandlingen. Detta på grund av att tumörer ofta är fria att röra sig relativt patientens skelett och yttre kontur, och många bildregistreringsalgoritmer tar hänsyn till hela bildvolymen. Arbetet som presenteras i denna avhandling syftar till att undersöka möjligheterna med arbetsflöden som baseras helt på MR data utan bildregistrering, samt arbetsflöden som använder bildregistrerings-algoritmer som är bättre anpassade för tumörer som kan röra sig i förhållande till patientens övriga anatomi, som till exempel prostatacancer.
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Axberg, Elin, and Ida Klerstad. "Similarity models for atlas-based segmentation of whole-body MRI volumes." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172792.
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In order to analyse body composition of MRI (Magnetic Resonance Imaging) volumes, atlas-based segmentation is often used to retrieve information from specific organs or anatomical regions. The method behind this technique is to use an already segmented image volume, an atlas, to segment a target image volume by registering the volumes to each other. During this registration a deformation field will be calculated, which is applied to a segmented part of the atlas, resulting in the same anatomical segmentation in the target. The drawback with this method is that the quality of the segmentation is highly dependent on the similarity between the target and the atlas, which means that many atlases are needed to obtain good segmentation results in large sets of MRI volumes. One potential solution to overcome this problem is to create the deformation field between a target and an atlas as a sequence of small deformations between more similar bodies. In this master thesis a new method for atlas-based segmentation has been developed, with the anticipation of obtaining good segmentation results regardless of the level of similarity between the target and the atlas. In order to do so, 4000 MRI volumes were used to create a manifold of human bodies, which represented a large variety of different body types. These MRI volumes were compared to each other and the calculated similarities were saved in matrices called similarity models. Three different similarity measures were used to create the models which resulted in three different versions of the model. In order to test the hypothesis of achieving good segmentation results when the deformation field was constructed as a sequence of small deformations, the similarity models were used to find the shortest path (the path with the least dissimilarity) between a target and an atlas in the manifold. In order to evaluate the constructed similarity models, three MRI volumes were chosen as atlases and 100 MRI volumes were randomly picked to be used as targets. The shortest paths between these volumes were used to create the deformation fields as a sequence of small deformations. The created fields were then used to segment the anatomical regions ASAT (abdominal subcutaneous adipose tissue), LPT (left posterior thigh) and VAT (visceral adipose tissue). The segmentation performance was measured with Dice Index, where segmentations constructed at AMRA Medical AB were used as ground truth. In order to put the results in relation to another segmentation method, direct deformation fields between the targets and the atlases were also created and the segmentation results were compared to the ground truth with the Dice Index. Two different types of transformation methods, one non-parametric and one affine transformation, were used to create the deformation fields in this master thesis. The evaluation showed that good segmentation results can be achieved for the segmentation of VAT for one of the constructed similarity models. These results were obtained when a non-parametric registration method was used to create the deformation fields. In order to achieve similar results for an affine registration and to improve the segmentation of other anatomical regions, further investigations are needed.
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Saputra, Michael Wijaya. "Water and Fat Image Reconstruction from MRI Raw Multi Coil Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372138.
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n MRI, water and fat signal separation with robust techniques are often helpful in the diagnosis using MRI. Reliable separation of water and fat will help the doctor to get accurate diagnoses such as the size of a tumour. Moreover, fat images can also help in diagnosing the liver and heart condition. To perform water and fat separation, multiple echoes, i.e. measurements of the raw MR signal at different time points, are required. By utilizing the knowledge of the expected signal evolution, it is possible to perform the separation. A main magnetic field is used in MRI. This field is not perfectly homogeneous. Estimating the non-homogeneities is crucial for correcting the separation signal. This thesis used the method of "Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation" (IDEAL). The aims of the thesis are developed a method which reconstruct fat or water MRI images from raw multi-coil image data and evaluate the method’s accuracy and speed by comparing with an available, implemented reconstruction method. In particular, the stability to so called swap artefacts will be analysed. Estimated field maps or inhomogeneity fields are one important and essential step, but there exist multiple local minima. To avoid choosing the incorrect minima, the initial estimation of the field map had to be close to the actual field map value. Neighbouring pixels would have a similar field map values, since the inhomogeneity field was smoothly varying. As such, we carried out the combination of IDEAL algorithms with a region growing method. We implemented the method to do the water and fat separation from a raw image consisting of multi-coil data and multi- echo. The proposed method was tested and the region growing method shows a significantly improved separation of water and fat, when compared to the traditional method without region growing.
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Järrendahl, Hannes. "Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130944.
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Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.
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Heinrich, Mattias Paul. "Deformable lung registration for pulmonary image analysis of MRI and CT scans." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:570112d9-c995-46df-86c0-15ce4ab928ff.
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Medical imaging has seen a rapid development in its clinical use in assessment of treatment outcome, disease monitoring and diagnosis over the last few decades. Yet, the vast amount of available image data limits the practical use of this potentially very valuable source of information for radiologists and physicians. Therefore, the design of computer-aided medical image analysis is of great importance to imaging in clinical practice. This thesis deals with the problem of deformable image registration in the context of lung imaging, and addresses three of the major challenges involved in this challenging application, namely: designing an image similarity for multi-modal scans or scans of locally changing contrast, modelling of complex lung motion, which includes sliding motion, and approximately globally optimal mathematical optimisation to deal with large motion of small anatomical features. The two most important contributions made in this thesis are: the formulation of a multi-dimensional structural image representation, which is independent of modality, robust to intensity distortions and very discriminative for different image features, and a discrete optimisation framework, based on an image-adaptive graph structure, which enables a very efficient optimisation of large dense displacement spaces and deals well with sliding motion. The derived methods are applied to two different clinical applications in pulmonary image analysis: motion correction for breathing-cycle computed tomography (CT) volumes, and deformable multi-modal fusion of CT and magnetic resonance imaging chest scans. The experimental validation demonstrates improved registration accuracy, a high quality of the estimated deformations, and much lower computational complexity, all compared to several state-of-the-art deformable registration techniques.
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Lai, Matteo. "Conditional MR image synthesis with Auxiliary Progressive Growing GANs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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L'addestramento di algotritmi di deep learning (DL) richiede una grande quantità di dati, che però spesso non sono disponibili in ambito medico. In questa tesi viene proposto un modello per la generazione di dataset sintetici etichettati nell'ambito dell'imaging medico ad alta risoluzione. Dopo aver presentato vantaggi e limiti dell'uso delle tecniche di DL in radiologia, vengono proposte le Generative Adversarial Networks (GANs) come possibile soluzione per superare tali limiti. Illustrando lo stato dell'arte relativo alle GAN, viene focalizzata l'attenzione sulle Progressive Growing GAN, capaci di generare immagini ad alta risoluzione, e sulle Auxiliary Classifier GAN (ACGAN), capaci di generare immagini target.
Sulla base di questi modelli, vengono proposte le innovative Progressive ACGAN (PACGAN), progettate per generare immagini target ad elevata risoluzione. L'obiettivo di questo lavoro di tesi è sfruttare la capacità delle GAN di creare una rappresentazione nello spazio latente dei dati del training set, sia per generare immagini target ad alta risoluzione (256 x 256), che per effettuare una classificazione. Il modello proposto viene testato su un dataset contenente 200 immagini di risonanza magnetica (RM) cerebrale di soggetti sani e pazienti con malattia di Alzheimer.
I risultati del modello sono molto promettenti.
La qualità delle immagini generate è stata valutata sia visivamente che quantitativamente, tramite FID (Fréchet Inception Distance) e MS-SSIM (Multi-Scale Structural Similarity Index), evidenziando una maggiore capacità delle PACGAN di rappresentare immagini target ad alta risoluzione rispetto alle ACGAN. Le performance di classificazione risultano ottime nel training set, con discreta capacità di generalizzare su nuovi dati.
Il modello proposto consente quindi di generare immagini target ad alta risoluzione che possono essere usate per ottenere dataset sintetici.
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Bhat, Danish. "Image Registration and Analysis within quantitative MRI to improve estimation of brain parenchymal fraction." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132973.
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In certain neuro-degenerative diseases likemultiple sclerosis (MS), the rate of brain atrophy can be measured by monitoring the brain parenchymal fraction (BPF) in such patients. The BPF is defined as the ratio of brain parenchymal volume (BPV, defined as the total volume of gray matter tissue, white matter tissue and other unidentified tissue) and intracranial volume (ICV, the total volume of the skull). It can be represented by the formula in equation 1:
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Graff, Christian George. "Parameter Estimation in Magnetic Resonance Imaging." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/195912.
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This work concerns practical quantitative magnetic resonance (MR) imaging techniques and their implementation and use in clinical MR systems. First, background information on MR imaging is given, including the physics of the magnetic resonance, relaxation effects and how imaging is accomplished.Subsequently, the first part of this work describes the estimation of the T2 relaxation parameter from fast spin-echo (FSE) data. Various complications are considered, including partial volume and data from multiple receiver coils along with the effects of the timing parameters on the accuracy of T2 estimates. Next, the problem of classifying small (1 cm diameter) liver lesions using T2 estimates obtained from radially-acquired FSE data collected in a single breath-hold is considered. Several algorithms are proposed for obtaining lesion T2 estimates, and these algorithms are evaluated with a task-based metric, their ability to separate two classes of lesions, benign and malignant. A novel computer-generated phantom is developed for the generation of the data used in this evaluation.The second part of this work describes techniques that perform the separation of water and lipid signals while simultaneously estimating relaxation parameters that have clinical relevance. The acquisition sequences used here are Cartesian and radial versions of Gradient and Spin-Echo (GRASE). The radial GRASE technique is post-processed with a novel algorithm that estimates the T2 of the water signal independent of the lipid signal. The accuracy of this algorithm is evaluated in phantom and its potential use for detecting inflammation of the liver is evaluated using clinical data. Cartesian GRASE data is processed to obtain T2-dagger and lipid fraction estimates in bone which can be used to assess bone quality. The algorithm is tested in phantom and in vivo, and preliminary results are given.In the concluding chapter results are summarized and directions for future work are indicated.
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Usta, Fatma. "Image Processing Methods for Myocardial Scar Analysis from 3D Late-Gadolinium Enhanced Cardiac Magnetic Resonance Images." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37920.
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Myocardial scar, a non-viable tissue which occurs on the myocardium due to the insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Analysis of myocardial scar is important for predicting the risk of arrhythmia and locations of re-entrant circuits in patients’ hearts. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, reconstruction of the intact geometry of scar is required.
Currently, 2D multi-slice late gadolinium-enhanced magnetic resonance imaging (LGEMRI) is widely used to detect and quantify myocardial scar regions of the heart. However, due to the anisotropic spatial dimensions in 2D LGE-MR images, creating scar geometry from these images results in substantial reconstruction errors. For applications requiring reconstructing the intact geometry of scar surfaces, 3D LGE-MR images are more suited as they are isotropic in voxel dimensions and have a higher resolution.
While many techniques have been reported for segmentation of scar using 2D LGEMR images, the equivalent studies for 3D LGE-MRI are limited. Most of these 2D and
3D techniques are basic intensity threshold-based methods. However, due to the lack of optimum threshold (Th) value, these intensity threshold-based methods are not robust in dealing with complex scar segmentation problems. In this study, we propose an algorithm for segmentation of myocardial scar from 3D LGE-MR images based on Markov random field based continuous max-flow (CMF) method. We utilize the segmented myocardium as the region of interest for our algorithm.
We evaluated our CMF method for accuracy by comparing its results to manual delineations using 3D LGE-MR images of 34 patients. We also compared the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yields a Dice similarity coefficient (DSC) of 71 +- 8.7% and an absolute volume error (|VE|) of 7.56 +- 7 cm3. Overall, the CMF method outperformed the conventional methods for almost all reported metrics in scar segmentation. We present a comparison study for scar geometries obtained from 2D vs 3D LGE-MRI.
As the myocardial scar geometry greatly influences the sensitivity of risk prediction in
patients, we compare and understand the differences in reconstructed geometry of scar generated using 2D versus 3D LGE-MR images beside providing a scar segmentation study. We use a retrospectively acquired dataset of 24 patients with a myocardial scar who underwent both 2D and 3D LGE-MR imaging. We use manually segmented scar volumes from 2D and 3D LGE-MRI. We then reconstruct the 2D scar segmentation boundaries to 3D surfaces using a LogOdds-based interpolation method. We use numerous metrics to quantify and analyze the scar geometry including fractal dimensions, the number-of-connected-components, and mean volume difference. The higher 3D fractal dimension results indicate that the 3D LGE-MRI produces a more complex surface geometry by better capturing the sparse nature of the scar. Finally, 3D LGE-MRI produces a larger scar surface volume (27.49 +- 20.38 cm3) than 2D-reconstructed LGE-MRI (25.07 +- 16.54 cm3).
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Smith, Norman Ronald. "Fast and automatic techniques for 3D visualization of MRI data." Thesis, Imperial College London, 1998. http://hdl.handle.net/10044/1/11916.
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Fan, Mingdong. "THREE INITIATIVES ADDRESSING MRI PROBLEMS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1585863940821908.
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Ali, Syed Farooq. "Comparative Studies of Contouring Algorithms for Cardiac Image Segmentation." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325183438.
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Davis, Warren B. "The Claustrum in Autism and Typically Developing Male Children: A Quantitative MRI Study." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2628.pdf.
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Shang, Weijian. "Teleoperation of MRI-Compatible Robots with Hybrid Actuation and Haptic Feedback." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/49.
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Image guided surgery (IGS), which has been developing fast recently, benefits significantly from the superior accuracy of robots and magnetic resonance imaging (MRI) which is a great soft tissue imaging modality. Teleoperation is especially desired in the MRI because of the highly constrained space inside the closed-bore MRI and the lack of haptic feedback with the fully autonomous robotic systems. It also very well maintains the human in the loop that significantly enhances safety. This dissertation describes the development of teleoperation approaches and implementation on an example system for MRI with details of different key components. The dissertation firstly describes the general teleoperation architecture with modular software and hardware components. The MRI-compatible robot controller, driving technology as well as the robot navigation and control software are introduced. As a crucial step to determine the robot location inside the MRI, two methods of registration and tracking are discussed. The first method utilizes the existing Z shaped fiducial frame design but with a newly developed multi-image registration method which has higher accuracy with a smaller fiducial frame. The second method is a new fiducial design with a cylindrical shaped frame which is especially suitable for registration and tracking for needles. Alongside, a single-image based algorithm is developed to not only reach higher accuracy but also run faster. In addition, performance enhanced fiducial frame is also studied by integrating self-resonant coils. A surgical master-slave teleoperation system for the application of percutaneous interventional procedures under continuous MRI guidance is presented. The slave robot is a piezoelectric-actuated needle insertion robot with fiber optic force sensor integrated. The master robot is a pneumatic-driven haptic device which not only controls the position of the slave robot, but also renders the force associated with needle placement interventions to the surgeon. Both of master and slave robots mechanical design, kinematics, force sensing and feedback technologies are discussed. Force and position tracking results of the master-slave robot are demonstrated to validate the tracking performance of the integrated system. MRI compatibility is evaluated extensively. Teleoperated needle steering is also demonstrated under live MR imaging. A control system of a clinical grade MRI-compatible parallel 4-DOF surgical manipulator for minimally invasive in-bore prostate percutaneous interventions through the patient’s perineum is discussed in the end. The proposed manipulator takes advantage of four sliders actuated by piezoelectric motors and incremental rotary encoders, which are compatible with the MRI environment. Two generations of optical limit switches are designed to provide better safety features for real clinical use. The performance of both generations of the limit switch is tested. MRI guided accuracy and MRI-compatibility of whole robotic system is also evaluated. Two clinical prostate biopsy cases have been conducted with this assistive robot.
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Ribolla, Marco. "Development of an Image Registration Procedure : Matching of Brain MRI Data Sets." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81457.
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Background: Registration is a key process in comparing different image sets. The registered images are used by surgeons for reasons of evaluation and surveillance (e.g.post-operative position control and particular health state evaluation). To compare images at an appropriate level of quality, it requires an understanding of how the images are related to each other and which registration basis and transformation should be chosen, to achieve the best possible registration result. The intention of this thesis is, to develop and evaluate a registration method for comparing the amount of cerebrospinal fluid, in order to apply it as a basis for the deep brain stimulation. Since the cerebrospinal fluid has an influence on the electrical current within the brain it is important, to know how much cerebrospinal fluid exists. Material and Methods: This thesis presents a straightforward approach, to register magnetic resonance (MR) image sets by subtracting the normalized intensities and by calculating the particular rigid transformations. Two T2 weighted image sets and two spoiled gradient recalled echo (SPGR) image sets were used for the registration process. Furthermore the T2 images were used for the validation of the whole registration method. Results: Both image set modalities, the T2 as well as the SPGR were successfully registered using the developed registration method. Therefore a translation correction of 54pixels in x-direction, respectively 65 pixels in y-direction (T2) and 7 pixels in x-direction, respectively 6 pixels in y-direction (SPGR) was necessary. The detected rotation of 1.5 °in the T2 matching set was adjusted too. The SPGR matching set showed no rotation. The median sum of squares of intensity differences resulted in a value of 6438 (T2) and25.86 (SPGR). The validation procedure constitutes an indication that the developed registration process is reliable and stable. Conclusion: The implemented registration procedure constitutes a straightforward, timeconsuming approach, which is useful to gain results within the same image modality. If there is any need for an inter-modality registration, the approach must be changed.
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Freeborough, Peter Anthony. "Image analysis tools and texture classification and their applications in clinical MRI." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/12021.
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Lindahl, Jens. "Correlation between PET/MRI image features andpathological subtypes for localized prostate cancer." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184885.
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Prostate cancer is the most common cancer in Sweden. Patients with the condition have a good prognosis in general and most cases can be treated. Localized prostate cancer is primarily treated via surgery or radiation therapy and is diagnosed with the help of different imaging modalities, such as magnetic resonance imaging, MRI, and positron emission tomography, PET. The diagnosis is confirmed and the aggressiveness of the cancer is determined through biopsies. Samples from a small part of the prostate are extracted and then examined. This could mean that parts of higher aggressiveness may be missed, which in turn could lead to under-treatment of the cancer. The aggressiveness of a lesion can be described by Gleason Score, GS, which is determined by an visual assessment of the shape, size and arrangement of the cells. The aim of this study was to correlate GS with in-vivo images using MRI and PET. This was accomplished by investigating image data from PSMA PET, Acetate PET, Ktrans MRI and T2-weighted MRI from a cohort of 26 prostate cancer patients containing 74 lesions. Regions of interests, ROI:s, were created and applied on all images. Statistics such as median and max value were extracted from each ROI. The statistics were combined to get a wide range of descriptive variables for each respective imaging modality. These were normalised against a certain zone of the prostate or only the absolute value. The results indicated that PSMA PET, Acetate PET and Ktrans MRI were correlated to GS, while T2-weighted MRI was not. Data also indicated that PSMA PET, Acetate PET and Ktrans MRI give complementary information to each other, which could indicate that a combination of the modalities would better predict GS. The implications of these findings could affect both the diagnostics and the treatment of prostate cancer.
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Shi, Wenzhe. "An image segmentation and registration approach to cardiac function analysis using MRI." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10548.
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Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent years, significant progress has been made in the care and treatment of patients with such diseases. A crucial factor for this progress has been the development of magnetic resonance (MR) imaging which makes it possible to diagnose and assess the cardiovascular function of the patient. The ability to obtain high-resolution, cine volume images easily and safely has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during the image acquisition process. With the development of advanced MR imaging acquisition technologies, 3D MR imaging is more and more clinically feasible. This recent development has allowed new potentially 3D image analysis technologies to be deployed. However, quantitative analysis of cardiovascular system from the images remains a challenging topic. The work presented in this thesis describes the development of segmentation and motion analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic resonance (CMR) images. The first main contribution of the thesis is the development of a fully automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art techniques. The proposed segmentation technique is capable of generating an accurate 3D segmentation from multiple image sequences. The proposed segmentation technique is robust even in the presence of pathological changes, large anatomical shape variations and locally varying contrast in the images. Another main contribution of this thesis is the development of motion tracking techniques that can integrate motion information from different sources. For example, the radial motion of the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of both longitudinal and circumferential motion. We propose a novel technique based on non-rigid image registration for the myocardial motion estimation using both untagged and 3D tagged MR images. The novel aspect of our technique is its simultaneous use of complementary information from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted to maximise the utility of information from both images. The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard FFD approach, a significant increase in registration accuracy can be observed in datasets with discontinuous motion patterns. Both the segmentation and motion tracking techniques presented in this thesis have been applied to clinical studies. We focus on two important clinical applications that can be addressed by the techniques proposed in this thesis. The first clinical application aims at measuring longitudinal changes in cardiac morphology and function during the cardiac remodelling process. The second clinical application aims at selecting patients that positively respond to cardiac resynchronization therapy (CRT). The final chapter of this thesis summarises the main conclusions that can be drawn from the work presented here and also discusses possible avenues for future research.
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Schwarz, Jolanda M. [Verfasser]. "Advanced Image Reconstruction Methods for Ultra-High Field MRI / Jolanda M. Schwarz." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/1218474947/34.
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