Dissertations / Theses on the topic 'Dynamic contrast'

Angiogenesis is a fundamental driver of tumor biology and many other important aspect of human health. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been shown to be a valuable biomarker for the indirect assessment of angiogenesis. However, DCE-MRI is very specialized technique that has limitations. In this dissertation new models and contrast agents to address some of these limitations are presented. Chapter 1 presents an introduction to DCE-MRI, the rationale to asses tumor biology with this technique, the MRI pulses sequences and the standard pharmacokinetic modeling used for the analysis of DCE- MRI data. Chapter 2 describes the application of DCE-MRI to asses the response to the hypoxia-activated drug TH-302. It is shown that DCE-MRI can detect a response after only 24 hours of initiating therapy. In Chapter 3, a new model for the analysis of DCE-MRI is presented, the so-called Linear Reference Region Model (LRRM). This new model improves upon existing models and it was demonstrated that it is ~620 faster than current algorithms and 5 times less sensitive to noise, and more importantly less sensitive to temporal resolution which enables the analysis of DCE-MRI data obtained in the clinical setting, which opens a new area of study in clinical MRI. Chapter 4 describes the extension of the LRRM to estimate the absolute permeability of two fluorinated contrast agents; we call this approach the Reference Agent Model (RAM). In order to make this new model an experimental reality, a novel pulse sequence and contrast agents (CA) for ¹⁹F MRI were developed. Two contributions to the field of DCE-MRI are presented in this chapter, the first simultaneous ¹⁹F-DCE-MRI detection of two fluorinated CA in a mouse model of breast cancer, and the estimation of their relative permeability. RAM eliminates some of the physiological variables that affect DCE-MRI, which may improve its sensitivity and specificity. Finally, new potential applications of LRRM and RAM are discussed in Chapter 5.

Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities. Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities. Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities. Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.

The purpose of this research is to perform automated analysis of 4D dynamic contrast enhanced MRI datasets (DCE-MRI) of the habd and wrist relating to rheumatoid arthritis (RA) studies. In DCE-MRI, sequences of images are acquired from the joints over time, during which a contrast agent pre-injected into a patient enhances disease affected tissues. Measurement of this enhancement, which is specific to voxels representing particular tissue types, allows assessment of the patient's condition. Currently, analysis of DCE-MRI data is performed using semi-automated or manula techniques, which are time-consuming and subjective. These approaches involve no pre-processing techniques that can compensate for patient motion and hardware instability, or locate the tissue of interest. In this thesis we present a solution for fully automated objective assessment of DCE-MRI data acquired from RA patients. Analysis begins with application of a registration technique that permits compensation for patient motion. Secondly, independent automatic algorithms for accurate segmentation of both bone interiors, joint exteriors, and blood vessels from data volumes of the metacarpophalangeal joints are introduced. Performance of the segmentation algorithms is evaluated with both state-of-the-art and novel techniques developed as a part of this thesis. We have utilised and enhanced a supervised approach and developed a family of unsupervised metrics for automated evaluation of segmentation outputs. Lastly, the datasets are interpreted using a model-based approach, which permits understanding of the behaviour of tissues undergoing the medical procedure, and allows for a robust and accurate extraction of various parameters that quantify the extent of inflammation in RA patients. The algorithms proposed have been demonstrated on datasets acquired with both low and high field scanners, from different joints, using various pulse sequences. They are user-independent, time efficient, and generate easily reproducible and objective results. Expert observers found our results promising for possibly future diagnosis and monitoring of RA patients.

3T MRI provides higher signal-to-noise ratio images compared to lower field machines. However, a major drawback of 3T MRI is a higher B1 transmission-field inhomogeneity across the field-of-view compared to imaging at lower fields. B1-field mapping was performed on volunteers using a Philips 3.0T MR scanner and a typical head-first prone patient positioning technique. The B1-field transmitted in the breasts was found to be reduced towards the right side of the body. In some volunteers, the B1-field was reduced to about one-half of the nominal field in the right breast. To minimize the B1 inhomogeneity artefacts, a saturation recovery snapshot FLASH (SRSF) imaging sequence was proposed. Different saturation techniques were assessed. The best saturation efficiency was produced by Hoffmann’s saturation method. By using Hoffmann’s SRSF sequence, the error in the enhancement ratio (ER) can be reduced to about one half compared to imaging obtained using typical FLASH sequence in the presence of a 50% B1-field reduction. Other techniques i.e. bilateral power optimization and a dedicated patient support system were also tested. Both of these approaches produced substantial reductions of the B1 inhomogeneity seen with the standard technique. To address the effects of the native T1 (T10) of different tissues on DCE-MRI, novel enhancement factor indices calculated using SRSF sequence images were introduced and assessed. Computer simulations and gel phantom experiments showed that less error was observed in the indices calculated compared to the ER calculated using the conventional and widely used FLASH sequence. Furthermore, the effect of B1-field inhomogeneity on the novel indices is also reduced. One of the indices proposed is directly related to the contrast agent concentration. The theory and results presented show that the SRSF pulse sequence and the quantification techniques proposed have the potential to improve the accuracy of breast DCE-MRI at 3T.

In Dynamic Contrast Enhanced MRI there are several steps from the initial signal to obtaining the pharmacokinetic parameters for tumor characterization. The aim of this work was to validate the steps in the flow of data focusing on T1-mapping, Contrast Agent (CA)-quantification and the pharmacokinetical (PK) model, using a digital phantom of a head. In the Digital Phantom tissues are assigned necessary values to obtain both a regular and contrast enhanced (using Parker AIF) representation and simulating an SPGR signal. The data analysis was performed in a software called MICE, as well as the Digital Phantom developed at the department of Radiation Sciences at Umeå University. The method of variable flip angles for the T1-mapping was analyzed with respect to SNR and number of flip angles, finding that the median value in each tissue is correct and stable. A "two point" inversion recovery sequence was tested with optimal combination of inversion times for white matter and CSF and obtaining correct T1-values when the inversion times were close to the tissue T1, otherwise with large deviations seen. Three different methods for CA-quantification were analyzed and a large underestimation was found assuming a linearity between signal and CA-concentration mainly for vessels at about 60%, but also for other tissue such as white matter at about 15%, improving when the assumption was removed. Still there was a noticeable underestimation of 30% and 10% and the quantification was improved further, achieving a near perfect agreement with the reference concentration, taking the T2*-effect into account. Applying Kety-model, discarding the vp-term, Ktrans was found to be stable with respect to noise in the tumor rim but ve noticeably underestimated with about 50%. The effect of different bolus arrival time, shifting the AIF required in the PK-model with respect to the CA-concentration, was tested with values up to 5 s, obtaining up to about 5% difference in Ktrans as well as the effect of a vascular transport function obtained by the means of an effective mean transit time up to 5 s and up to about 5% difference in Ktrans.

This thesis describes the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to study the prognostic role of microvascular physiology and heterogeneity in locally advanced cancers of the cervix, bladder, and head and neck. To increase the utility of DCE-MRI parameters for prognostication and use in heterogeneity analyses, a novel model fitting approach was developed to reduce the error in two-compartment exchange model (2CXM) parameter estimates. Using this method, precision of 2CXM parameters was increased in 35 of 42 experimental conditions (improvements between 4.7% and 50%) and bias reduced in 30 of 42 conditions (reductions between 1.8% and 49%). The prognostic value of plasma flow, permeability surface area product, and contrast agent volume transfer constant were assessed in a cervix cancer dataset. Plasma flow was the most prognostic parameter (HR = 0.25, P = 0.0086), followed by the volume transfer constant (HR = 0.33, P = 0.031), then the permeability surface area product (HR = 0.43, P = 0.090). Inclusion of plasma flow in survival modelling significantly increased the ability to discriminate between patients with short and long disease-free survival, compared to clinicopathologic factors alone (P = 0.043). The universal prognostic value of microvascular heterogeneity was assessed in cervix, bladder, and head and neck datasets. Following estimation of 2CXM parameters for each patient, a selection of previously published heterogeneity biomarkers were computed and entered into a random survival forest variable selection algorithm. Two variables (vvas, Atrans) were identified as universally prognostic and significantly improved discriminative ability of survival models compared to clinicopathologic factors alone (P < 0.001). Gaussian process models were used to decompose statistical and spatial aspects of intratumoural microvascular heterogeneity. When applied to the three cancer datasets described above, statistical variance in plasma flow (P = 0.00025) was universally prognostic and showed greater discriminative ability compared with spatial scale and average microvascular function parameters. The results of this thesis demonstrate that joint fitting reduces error in DCE-MRI parameters. DCE-MRI estimates of plasma flow appear to hold greater prognostic value than the volume transfer constant and permeability surface area product, and microvascular heterogeneity has potential to provide universal prognostic value. The biomarkers vvas, Atrans, and variance in plasma flow, were identified as universally prognostic. Future work should test the reproducibility of these biomarkers for prognostication in independent datasets.

Static Visual Acuity (SVA) has been called into question for some time as a measure of overall visual system function and as a predictor of performance on real-life tasks requiring vision (i.e., operating an automobile). Specifically, it has been pointed out that the targets employed in most SVA testing (high contrast, stationary letters) are an insufficient analog to actual targets encountered in everyday activities, which are often in motion and/or of less-than-perfect contrast. In addition, the size-threshold methodology typically used to measure SVA is incongruent with current theories of a multi-channel visual system. Dynamic Visual Acuity (DVA) and Contrast Sensitivity have been suggested as alternatives to SVA, but while each mitigates specific weaknesses of the SVA measure, neither addresses the shortcomings completely. Traditional DVA measures employ moving targets, but these targets are usually of perfect contrast and a size-threshold methodology is used to specify acuity levels. Contrast Sensitivity employs a contrast-threshold methodology and allows measurement of specific visual channels, but stationary targets are utilized. The present study combined the DVA and Contrast Sensitivity measures in an effort to retain the unique qualities of each while addressing their shortcomings, resulting in a more detailed picture of the human visual system and functioning than has yet been possible. By measuring contrast sensitivity to targets at a set of spatial frequencies spanning the human "window of visibility" and under conditions of motion representative of that encountered in everyday activities, it was hoped that a more powerful predictor of actual visual performance would be created. In addition, normative data was established for two separate age populations, in the hopes of learning more about specific changes that occur to the visual system during the aging process. Indeed, several effects and interactions among the three main variables (spatial frequency, velocity, age) were uncovered, which appears to indicate that the new test may provide more information about the visual system than DVA or contrast sensitivity by themselves. The ramifications of this effort to human factors and visual performance research are discussed along with recommendations for the continuation and application of this line of research.
Ph.D.
Department of Psychology
Arts and Sciences
Psychology

This work is focussed on the analysis of breast tumour physiology by pharmacokinetic modelling of dynamic contrast enhanced MRI (DCE-MRI) data. DCEMRI consists of the intravenous bolus injection of a small molecular weight contrast agent into the patient followed by the rapid acquisition of MR images across both breasts. Due to the leaky nature of the lesion microvasculature there is a greater uptake of contrast agent within the tumour than in the surrounding tissues. The dynamic contrast enhanced MR signal curve can be fitted by compartmental analysis providing information linked to the tumour’s permeability and flow. The effect of the DCE-MRI acquisition parameters on the accuracy of the estimated pharmacokinetic quantities was investigated together with the assumptions lying behind the pharmacokinetic model used for the fitting. Contrast enhanced MRI data were also examined using a fractal measure of tumour heterogeneity with the aim of assessing whether this could be a potential predictor of the tumour response to chemotherapy. Among the factors believed to play an important role in terms of tumour treatment is an increased interstitial fluid pressure (IFP) in the central areas of some large tumours. Here DCE-MRI data were analyzed in a way to see whether it could provide any information related to IFP distribution across tumour volumes. Finally, when performing quantitative DCE-MRI, particular care needs to be taken in the choice of an arterial input function (AIF) which accurately describes the passage of the contrast agent bolus at the lesion location. Here a new approach was proposed and demonstrated for the estimation of a tumour capillary input function together with lesion pharmacokinetic parameters. This was achieved by optimizing the capillary input function with a measure of the patient’s cardiac output, a parameter which is expected to vary depending on the patient’s pathology/physiology.

Dynamic contrast-enhanced (DCE) time series analysis techniques are hard to fully validate quantitatively as ground truth microvascular parameters are difficult to obtain from patient data. This thesis presents a software application for generating synthetic image data from known ground truth tracer kinetic model parameters. As an object oriented design has been employed to maximise flexibility and extensibility, the application can be extended to include different vascular input functions, tracer kinetic models and imaging modalities. Data sets can be generated for different anatomical and motion descriptions as well as different ground truth parameters. The application has been used to generate a synthetic DCE-MRI time series of a liver tumour with non-linear motion of the abdominal organs due to breathing. The utility of the synthetic data has been demonstrated in several applications: in the development of an Akaike model selection technique for assessing the spatially varying characteristics of liver tumours; the robustness of model fitting and model selection to noise, partial volume effects and breathing motion in liver tumours; and the benefit of using model-driven registration to compensate for breathing motion. When applied to synthetic data with appropriate noise levels, the Akaike model selection technique can distinguish between the single-input extended Kety model for tumour and the dual-input Materne model for liver, and is robust to motion. A significant difference between median Akaike probability value in tumour and liver regions is also seen in 5/6 acquired data sets, with the extended Kety model selected for tumour. Knowledge of the ground truth distribution for the synthetic data was used to demonstrate that, whilst median Ktrans does not change significantly due to breathing motion, model-driven registration restored the structure of the Ktrans histogram and so could be beneficial to tumour heterogeneity assessments.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Whitaker College of Health Sciences and Technology, 1995.
Includes bibliographical references (leaves 252-274).
by Jerrold Lee Boxerman.
Ph.D.

Dynamic contrast~enhanced magnetic resonance imaging (DCE-MRI) renography is a desirable kidney assessment methodology owing to the lack of ionizing radiation in MRI and its capability of producing high-resolution anatomical image data as well as physiological data. DCE-MRI renography emerged with the view to provide a minimally invasive framework to quickly and accurately assess kidney function, for example, to measure glomerular filtration rate (GFR). However, despite considerable developments, it is not yet considered a robust technique of renal assessment. This is due to a number of confounding factors ranging from · optimization of data acquisition parameters to data post-processing challenges such as organ motion (mainly due to breathing), segmentation, partial volume (PV) effect (a signal mixing phenomenon) and tracer kinetic modelling. Prior works including registration-based motion correction techniques, semi-automatic segmentation based on similarity measures and a template-based PV correction method have not provided a complete and practical solution. In this work, a blind source separation (BSS) approach based on time-delayed decorrelation and temporal independent component analysis (ICA) was proposed to unmix physiological signals and remove the undesired motion artefacts. To evahtate the technique, test data were constructed using kidney, liver and non- . specific tissue dynamic MR signals. The source signals were correctly identified with small errors and coefficient of determination r2 values of 0.85 - 0.99 between the independent components (ICs) and source signals.

A new optical imaging modality has been developed for small animal in vivo imaging of near-infrared fluorescence resulting from fluorescent contrast agents specifically targeted to molecular markers of cancer. The imaging system is comprised of an intensified charge-coupled device (ICCD) for the detection of ultra-low levels of re-emitted fluorescence following the delivery of an expanded beam of excitation light. The design of the ICCD detection system allows for both continuous wave (CW) and frequency-domain modes of operation. Since the accurate acquisition of frequency-domain photon migration (FDPM) data is important for tomographic imaging, the imaging system was also validated using experimentally obtained FDPM measurements of homogenous turbid media and diffusion theory to obtain estimates of the optical properties characteristic of the media. The experiments demonstrated that the absorption and reduced scattering coefficients are determined least accurately when relative rel measurements of average light intensity IDC are employed either alone or in a rel combination with relative modulation amplitude data IAC and/or relative phase shift data rel . However, when FDPM measurements of are employed either alone or in rel combination with IAC data, the absorption and reduced scattering coefficients may be found accurate to within 15% and 11%, respectively, of the values obtained from standard single-pixel measurements; a result that suggests that FDPM data obtained from an ICCD detection system may in fact be useful in tomographic imaging. Furthermore, intensified-detection allows for sub-second exposure times, permitting the acquisition of dynamic fluorescence images immediately following administration of the contrast agent. Experimental results demonstrate that when coupled with a suitable pharmacokinetic model describing targeted dye distribution throughout the body, dynamic fluorescence imaging may be used to discriminate spontaneous canine adenocarcinoma from normal mammary tissue. A separate experiment demonstrates that pharmacokinetic analysis of dynamic fluorescence images enables one to estimate the rate constant governing Kaposi's sarcoma tumor uptake of an integrin-targeted dye and integrin receptor turnover rate. The rate constant for uptake was calculated to be 0.16-sec-1 while the turnover rate of the integrin receptor was estimated to occur within 24-hours.

The assessment of myocardial perfusion using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool for diagnosing myocardial ischaemia due to coronary heart disease, which affects nearly 2.7 million people in the UK and for which there is an effective treatment. Although visual analysis of DCE-MRI data performs well diagnostically, a quantitative estimate of myocardial blood flow (MBF) makes the diagnosis objective and could increase diagnostic performance. Obtaining MBF estimates from DCE-MRI data is a multi-step process requiring: - the localisation of the myocardium and arterial input function (AIF) to generate signal intensity vs. time curves; - the conversion of signal intensity data to contrast agent concentration values; - the application of a perfusion model to generate a quantitative MBF estimate; - the interpretation of MBF estimates to make a diagnostic assessment of myocardial ischaemia. There are a range of approaches for solving each of these problems. The aim of the work presented in this thesis has been to provide clinically relevant evidence for choosing between these approaches. Myocardial localisation contour error tolerance levels are suggested based on simulations using a volunteer dataset. A non-linear signal intensity to contrast agent concentration conversion method is presented and tested using simulations and phantom data. An investigation into the best way to interpret quantitative MBF estimates is then presented. Finally a comparison of four, widely applied, perfusion models is conducted. Where possible, methods have been compared on a sizeable patient dataset in terms of diagnostic performance rather than MBF estimate accuracy. This provides evidence suitable for informing clinical decisions on the best methodology for quantitative perfusion. Such evidence could contribute to a standard methodology for quantitative cardiac MR perfusion. This is necessary for large clinical trials, which are essential before quantitative MBF estimates can be accepted into routine clinical practice.

The neovasculature formed as a lesion outgrows the local blood supply is incompletely formed.  This makes these vessels prone to leak macromolecular contrast agents, and it is this property of soft tissue lesions which allows the accumulation of a contrast agent within a lesion and hence alters the magnetic resonance (MR) signal. Since it is thought that the amount, and rate, of signal enhancement due to the accumulation of a contrast agent is an important aid in diagnosis, the accuracy and reliability of the quantitative phase of the image analysis are important issues in need of evaluation.  This work has investigated several methodological issues. As the quantitation of signal enhancement on a T1 weighted image due to the accumulation of a contrast agent is dependent upon the pre-contrast T1 of the tissue that is imaged, T1 measurements are made before the dynamic phase of an imaging protocol. A commonly used method of T1 measurement makes use of 2 images acquired at different flip angles.  Two novel methods of T1 measurement were investigated: the first based upon the 2 point method relies upon a linear regression; the second method uses an non linear least squares fit (NLLS).  The NLLS method is recommended. Fitting a mathematical model to a set of real data points involves minimising the sum of the squares of the differences between the real points and the points predicted by the model.  Two models commonly used in DCE-MRI were investigated with respect to their ability to find the true minimum in the presence of noise.  Furthermore, we show that the use of a single start point for all minimisations leads to unexpected fitting failures, and as such we propose a solution to this. Contrast media are cleared from the body by the kidneys.  Hence the effects of renal function on measurements made with the same tow models were investigated.  We show that  this significantly affects results.

This thesis explores issues relating to the quantitation of both signal enhancement and contrast agent uptake, along with problems associated with such quantitation, with the aim of improving the specificity of dynamic, contrast enhanced breast MRI. A variable flip angle technique for measuring T1 in vivo was implemented using 2D and 3D FLASH sequences, in order to monitor the differential relaxation rate following injection of contrast agent. Experiments (with phantom objects) investigating sources of error in these techniques found that (i) the rf transmit power calibration automatically performed by the imaging system was 13.5% in error, (ii) significant non-uniformity in the rf transmit field existed over the breast coil volume and (iii) a 2D FLASH sequence developed locally from an editable scout sequence was significantly more accurate at measuring T1 than a commercially supplied 2D FLASH sequence. Since the in vivo measurement of T1 requires complicated imaging protocols and data analysis, two simple indices commonly used to quantitate signal enhancement were evaluated by computer simulation and comparison in a group of patients. The postulate that the index least influenced by pre-contrast tissue T1 (when using a contrast enhanced gradient echo imaging protocol) would be better able to correctly classify an undiagnosed lesion as either benign or malignant was used to evaluate which index was the most appropriate for quantitating signal enhancement in breast MRI. An index which normalised the difference between pre- and post-contrast signal to the fat signal intensity proved to be the better of the two indices. One problem with this index, however, is that it is sensitive to variations in fat signal through the breast. A simple uniformity correction scheme was implemented to reduce this problem and tested on both phantom and patient image data sets.

Applying mathematical models to dynamic contrast enhanced MRI (DCE MRI) data to perform quantitative tracer kinetic analysis enables the estimation of tissue characteristics such as vascular permeability and the fractional volume of plasma in a tissue. However, it is unclear to what extent modeling assumptions, particularly regarding water exchange between tissue compartments, impacts parameter estimates derived from clinical DCE MRI data. In this work, a new model is developed which includes water exchange effects, termed the water exchange modified two compartment exchange model (WX-2CXM). Two boundaries of this model (the fast and no exchange limits) were used to analyse a clinical DCE MRI bladder cancer dataset. Comparisons with DCE CT, which is not affected by water exchange, suggested that water exchange may have affected estimates of vp, the fractional volume of plasma. Further investigation and simulations led to the development of a DCE MRI protocol which was sensitised to water exchange, in order to further evaluate the water exchange effects found in the bladder cancer dataset. This protocol was tested by imaging the parotid glands in eight healthy volunteers, and confirmed evidence of water exchange effects on vp, as well as flow Fp and the fractional volume of extravascular extracellular space ve. This protocol also enabled preliminary estimates of the water residence times in parotid tissue, however, these estimates had a large variability and require further validation. The work presented in this thesis suggests that, although water exchange effects do not have a large effect on clinical data, the effect is measurable, and may lead to the ability to estimate of tissue water residence times. Results do not support a change in the current practise of neglecting water exchange effects in clinical DCEMRI acquisitions.

This manuscript presents an investigation and application of the medical radiographic technique of Dynamic Contrast-enhanced Computed Tomography with an emphasis on its application to the measurement of tissue perfusion using the techniques of CT Perfusion. CT Perfusion was used in association with Fluoro- Deoxy Glucose Positron Emission Tomography (FDG PET) to investigate altered blood flow due to the angiogenic effects of tumour in the clinical setting of medical imaging for cancer diagnosis and staging. CT perfusion, CT enhancement and Doppler ultrasound studies were compared in a series of patient studies performed for the assessment of metastatic liver disease. There was good correlation between all techniques for the arterial phase but not between Doppler measurements of the portal phase and any CT measurement. A new method was developed for quantifying CT perfusion and enhancement values, the Standardised Perfusion Value (SPV) and the Standardised Enhancement Value (SEV). The SPV was shown to correlate with FDG uptake in a series of 16 patient studies of lung nodules, an unexpected and potentially important finding that if confirmed in a larger study may provide an additional diagnostic role for CT in the assessment of lung nodules. Investigation of a commercially available package for the determination of CT Perfusion, CT Perfusion GE Medical Systems, was undertaken in a small series of brain studies for assessment of acute stroke. This data set showed the technique to positively identify patients with non-hemorrhagic stroke in the presence of a normal conventional CT, to select those cases where thrombolysis is appropriate, and to provide an indication for prognosis. An investigation of the accuracy and cost-effectiveness of FDG PET in solitary pulmonary nodules using Australian data was carried out. FDG PET was found to be accurate, cost saving and cost effective for the characterisation of indeterminate solitary pulmonary nodules in Australia. This work was expanded to include the impact of quantitative contrast enhancement CT (QECT) on the cost-effectiveness of FDG PET. The addition of QECT is a cost effective approach, however whether QECT is used alone or in combination with FDG PET will depend on local availability of PET, the cost of PET with respect to surgery and the prior probability of malignancy. A published review of CT perfusion, clinical applications and techniques, is included in the body of the work. Dynamic contrast-enhanced CT and FDG PET were used to investigate blood flow, expressed as SPV, and metabolic relationships in non-small cell lung cancers (NSCLC) of varying size and stage. A significant correlation between SPV and FDG uptake was only found for tumours smaller than 4.5 cm2. Blood flow-metabolic relationships are not consistent in NSCLC but depend on tumour size and stage. Dynamic contrast-enhanced CT as an adjunct to an FDG study undertaken using integrated PET-CT offers an efficient way to augment the assessment of tumour biology with possible future application as part of clinical care. In summary the work has developed a method for standardizing the results of dynamic contrast-enhanced CT and investigated its potential when applied with FDG PET to improve the diagnosis and staging of cancers.

This manuscript presents an investigation and application of the medical radiographic technique of Dynamic Contrast-enhanced Computed Tomography with an emphasis on its application to the measurement of tissue perfusion using the techniques of CT Perfusion. CT Perfusion was used in association with Fluoro- Deoxy Glucose Positron Emission Tomography (FDG PET) to investigate altered blood flow due to the angiogenic effects of tumour in the clinical setting of medical imaging for cancer diagnosis and staging. CT perfusion, CT enhancement and Doppler ultrasound studies were compared in a series of patient studies performed for the assessment of metastatic liver disease. There was good correlation between all techniques for the arterial phase but not between Doppler measurements of the portal phase and any CT measurement. A new method was developed for quantifying CT perfusion and enhancement values, the Standardised Perfusion Value (SPV) and the Standardised Enhancement Value (SEV). The SPV was shown to correlate with FDG uptake in a series of 16 patient studies of lung nodules, an unexpected and potentially important finding that if confirmed in a larger study may provide an additional diagnostic role for CT in the assessment of lung nodules. Investigation of a commercially available package for the determination of CT Perfusion, CT Perfusion GE Medical Systems, was undertaken in a small series of brain studies for assessment of acute stroke. This data set showed the technique to positively identify patients with non-hemorrhagic stroke in the presence of a normal conventional CT, to select those cases where thrombolysis is appropriate, and to provide an indication for prognosis. An investigation of the accuracy and cost-effectiveness of FDG PET in solitary pulmonary nodules using Australian data was carried out. FDG PET was found to be accurate, cost saving and cost effective for the characterisation of indeterminate solitary pulmonary nodules in Australia. This work was expanded to include the impact of quantitative contrast enhancement CT (QECT) on the cost-effectiveness of FDG PET. The addition of QECT is a cost effective approach, however whether QECT is used alone or in combination with FDG PET will depend on local availability of PET, the cost of PET with respect to surgery and the prior probability of malignancy. A published review of CT perfusion, clinical applications and techniques, is included in the body of the work. Dynamic contrast-enhanced CT and FDG PET were used to investigate blood flow, expressed as SPV, and metabolic relationships in non-small cell lung cancers (NSCLC) of varying size and stage. A significant correlation between SPV and FDG uptake was only found for tumours smaller than 4.5 cm2. Blood flow-metabolic relationships are not consistent in NSCLC but depend on tumour size and stage. Dynamic contrast-enhanced CT as an adjunct to an FDG study undertaken using integrated PET-CT offers an efficient way to augment the assessment of tumour biology with possible future application as part of clinical care. In summary the work has developed a method for standardizing the results of dynamic contrast-enhanced CT and investigated its potential when applied with FDG PET to improve the diagnosis and staging of cancers.

Abstract Magnetic Resonance Imaging (MRI) is a medical imaging technique used in radiology to visualize the anatomical structures and the functions of the body. Thanks to its fine spatial resolution and to the great contrast between the different soft tissues, MRI has become the most used method for the anatomical image generation. During the last two decades, MRI was widely studied and developed, so high performance devices and new analysis protocols are now available. As an outcome, MR can now be used also to perform functional analysis. Currently, the Positron Emission Tomography (PET) is the gold standard technique in functional imaging. However, MRI is becoming a valid alternative to PET in functional analysis because of its greater spatial resolution, its wide diffusion and the absence of ionizing radiations. Currently, perfusion magnetic resonance using an exogenous tracer, such as gadolinium, is the most interesting technique for the quantitative study of the hemodynamic. The Dynamic Susceptibility Contrast - Magnetic Resonance Imaging (DSC-MRI) allows to quantify important hemodynamic parameters that play an important role in the study of several pathologies, such as cerebral neoplasia, ischemia or infarction, epilepsy, dementia and schizophrenia. The commonly used model for describing the DSC-MRI signal is based on the non diffusible tracer theory, also called dilution theory. It assumes that the tracer remains intravascular, the blood-brain-barrier (BBB) is intact and there is no tracer recirculation. Under these assumptions, the model allows to estimate the Cerebral Blood Volume (CBV), the Cerebral Blood Flow (CBF) and the Mean Transit Time (MTT). The most crucial step in the DSC-MRI image quantification is the residue function estimate that presents some limitations. The most important ones, that are considered in this work, are: • the necessity to know the Arterial Input Function (AIF), which is the concentration time curve in the vessels feeding the tissue; • the assessment of the residue function requiring to perform a deconvolution operation, which is a well-known difficult mathematical problem. Currently, AIF is measured directly on the MR images, by selecting a small number of pixels containing one of the principal arterial vessels. The pixel selection can be made either manually, by a physician, or by means of automatic algorithms. During the past years, several automatic and semiautomatic methods for the AIF extraction have been proposed, but a standard has not been achieved, yet. In this work, the AIF selection and deconvolution problems are discussed in depth. A new selection method, combining anatomical information with MR-signal analysis in presented. It is compared to the other AIF selection algorithms proposed in literature on a simulated data set. Then, a comparison with the manual selection method on a clinical data set is performed and the AIF selection impact on CBF, CBV and MTT estimate is investigated. The proposed method has been shown to reliably reconstruct the true AIF, providing accurate estimates and very narrow confidence bands. Moreover, it is robust against the different noise levels, thus increasing the reproducibility level in DSC-MRI image quantification. Furthermore, AIFs obtained with the new method have been shown to lead to a more accurate diagnosis than the manual ones. Another critical step in DSC-MRI data analysis is the deconvolution operation, that allows to estimate the residue function. Problems in this step are due to the deconvolution intrinsic problems (e.g. the ill-posedness and the ill-conditioning) and to the physiological system specific problems (e.g. non negative constrains). Moreover, another important source of error in the residue function estimate is the possible presence of delay and/or dispersion in AIF. Currently, the most used deconvolution methods are the Singular Value Decomposition (SVD) and the block-Circulant Singular Value Decomposition (cSVD). SVD is historically the first and the most important deconvolution method proposed in the DSC-MRI context and it is currently the reference method. The cSVD method is the natural evolution of SVD and it has been proposed to overcome the problem of delay in the AIF. Several other deconvolution methods have been proposed in literature. Among them all, we focus on a recently proposed method, the Nonlinear Stochastic Regularization (NSR), that accounts for both the smoothness and the non-negativity constraint of the residue function. In this work, a new deconvolution method is presented. The Population Deconvolution (PD) method exploits a population approach to analyse a large set of similar voxels at the same time, thus improving the data quality in the deconvolution operation. PD has been validated on simulated data and compared to SVD and cSVD. PD can reconstruct reliable and physiological residue functions. The residue functions obtained using PD present very small and damped oscillations compared to SVD and cSVD ones. Furthermore, PD has been shown to accurately estimate the CBF both in presence and in absence of dispersion, providing better results than SVD and cSVD. SVD, cSVD and PD have been compared also to NSR on clinical data. CBF and MTT maps provided by PD present a greater contrast level than SVD and cSVD ones, as they emphasize the flow and transit time differences. Also NSR maps are extremely contrasted, but they appear noisier than the PD ones. A new physiological indicator, the Laterality Index, has also been introduced. It provides a graphical representation of the CBF and MTT map information, integrating all the information provided by the different parameters. NSR provides very large laterality indices, thus emphasizing the disease affected regions. Nevertheless, the detection of the pathological areas is not easy because of the large LI variability also in the healthy regions. On the contrary, SVD and cSVD laterality indices make the disease detection difficult because they do not emphasize the pathological areas. PD meets the need to underline the pathologic areas without showing false positive results, providing larger LIs than the SVD and cSVD ones, but smaller than the NSR ones. Therefore, PD has been shown to lead to a more accurate diagnosis than the other methods. Finally, another promising deconvolution method, called DNP, is presented. Differently from PD, that has to be applied to large data sets because of its population approach, DNP is a voxel based method, thus it can be applied also to a small number of voxels. The most interesting DNP feature is that it accounts for both the residue function continuity and the system BIBO-stability. Moreover, it can estimate the AIF delay, thus improving the accuracy in the R(t) estimation. Since it is still under development, only the DNP preliminary results are presented in this work. DNP has been shown to provide more accurate CBF estimates than SVD and cSVD, both in presence and absence of delay and dispersion. Furthermore, the DNP reconstructed residue functions show neither the negative values nor the spurious oscillations usually present in the SVD and cSVD ones. However, DNP bears some limitations too. Currently, the most important DNP limitation is the delay estimation. DNP usually overestimates the delay, above all in presence of dispersion, thus providing a non accurate characterization of the residue function. Another DNP problem is that the hyper-parameter quantification requires a non-linear step, which increases the computation time of the algorithm. In conclusion, although they present some limitations in the post-processing analysis, DSC-MRI techniques are becoming an important tool in medical research and in clinical practice. The development of a fully automatic algorithm for the AIF selection and of a deconvolution method based on a population approach would improve the clinical and scientific information provided by DSC-MRI analysis.
Abstract La Risonanza Magnetica (RM) è una tecnica di imaging medico che viene utilizzata in radiologia sia per le strutture anatomiche sia per le funzionalità del corpo umano. Grazie all’elevata risoluzione spaziale di cui dispone e al notevole livello di contrasto tra le differenti tipologie di tessuto, la RM è diventata lo strumento per la generazione di immagini anatomiche più diffuso. Negli ultimi decenni, la RM è stata oggetto di studi approfonditi e notevoli sviluppi, tanto che oggi sono disponibili macchinari ad elevate prestazioni e un ampio numero di protocolli d’acquisizione differenti. Di conseguenza, la RM ha cominciato a essere utilizzata anche per studi funzionali. Attualmente, la Tomografia ad Emissione di Positroni (PET) è la tecnica di riferimento per gli studi funzionali, ma la RM sta diventando una valida alternativa grazie alla sua maggiore risoluzione spaziale, alla sua maggiore diffusione e al fatto che non utilizza radiazioni ionizzanti nocive. Attualmente, la risonanza magnetica di perfusione che ricorre all’uso di un agente di contrasto esogeno, come il gadolinio, è la tecnica più interessante per lo studio quantitativo dell’emodinamica. La Dynamic Susceptibility Contrast - Magnetic Resonance Imaging (DSC-MRI) permette di ricavare importanti parametri emodinamici che ricoprono un ruolo chiave nello studio di svariate patologie, quali la neoplasia cerebrale, l’ischemia, l’infarto, l’epilessia, la demenza e la schizofrenia. Per caratterizzare il segnale ottenuto con la DSC-MRI viene generalmente utilizzato un modello basato sulla teoria dei traccianti non diffusibili (la teoria della diluizione). Basandosi sulle ipotesi che il tracciante sia intravascolare, che la barriera emato-encefalica sia intatta e che non ci sia ricircolo di tracciante, il modello permette di ricavare il Volume Ematico Cerebrale (CBV), il Flusso Ematico Cerebrale (CBF) e il Tempo Medio di Transito (MTT). I passaggio chiave per la stima di tali parametri è la quantificazione della funzione residuo, che presenta tuttavia alcuni problemi. In questa tesi saranno trattati i più importanti tra essi: • la necessità di ricavare la Funzione d’Ingresso Arteriale (AIF), che rappresenta l’andamento nel tempo della concentrazione di tracciante nei vasi che irrorano il tessuto; • la necessità di ricorrere ad un’operazione di deconvoluzione per ricavare la funzione residuo. La AIF è solitamente ricavata selezionando alcuni pixel che rappresentano i vasi arteriali principali direttamente sulle immagini di RM. La selezione dei pixel può essere fatta sia manualmente da un radiologo sia tramite un algoritmo di selezione automatica. Recentemente sono stati proposti numerosi algoritmi per svolgere tale compito, ma non si è ancora raggiunto uno standard. In questo lavoro il problema relativo all’estrazione della AIF viene discusso approfonditamente. Si propone un nuovo metodo per la selezione dei pixel arteriali che combina le informazioni anatomiche con l’analisi del segnale DSC-MRI. L’algoritmo è testato su dati simulati e confrontato con i più interessanti algoritmi proposti in letteratura. Successivamente viene applicato anche su dati reali e confrontato con la AIF ottenuta tramite selezione manuale al fine di valutare l’impatto che la scelta della AIF ha sulla stima dei parametri CBF, CBV e MTT. Il metodo proposto ha dimostrato di essere in grado di ricostruire la AIF originale, fornendo sia stime accurate che intervalli di confidenza molto stretti. Inoltre ha dimostrato di essere robusto nei confronti di diversi livelli di rumorosità nei dati, contribuendo quindi all’aumento della riproducibilità nello studio dell’emodinamica cerebrale. Infine, le AIF ottenute tramite il nuovo algoritmo hanno permesso di effettuare diagnosi più accurate rispetto a quelle ottenute tramite selezione manuale. Un altro passaggio critico per l’analisi dei dati DSC-MRI è rappresentato dall’operazione di deconvoluzione necessaria per la stima della funzione residuo. I problemi in quest’ambito sono legati sia ai problemi intrinseci della deconvoluzione (ad esempio il fatto che è un problema matematico mal condizionato e mal posto), sia ad aspetti dovuti al fatto che si tratta di un sistema fisiologico (ad esempio vincoli di non negatività). Inoltre, la possibile presenza di dispersione e ritardo nella AIF costituisce un’altra importante fonte di errore per la stima della funzione residuo. Ad oggi, i metodi di deconvoluzione più diffusi sono la Singular Value Decomposition (SVD) e la block-Circulant Singular Value Decomposition (cSVD). La SVD è storicamente il primo metodo proposto per lo studio dei dati DSC-MRI e rappresenta ancora il metodo di riferimento in quest’ambito. La cSVD è invece la naturale evoluzione della SVD, proposta per eliminare i problemi dovuti al ritardo nella AIF che caratterizzano la SVD. Numerosi metodi sono stati proposti negli anni in letteratura. Tra i vari, citiamo la Nonlinear Stochastic Regularization (NSR), che permette di tener conto sia dei vincoli di non negatività sia della regolarità della funzione residuo. In questo lavoro si presenta un nuovo metodo di deconvoluzione. La Population Deconvolution (PD) che analizza contemporaneamente un ampio numero di voxel simili sfruttando un approccio di popolazione, quindi migliorando la qualità dei dati utilizzati per l’operazione di deconvoluzione. Il metodo PD è stato validato su dati simulati e confrontato sia con la SVD che con la cSVD. PD riesce a ricostruire funzioni residuo che risultato credibili e fisiologiche in quanto presentano oscillazioni poco ampie e più smorzate rispetto a quelle presenti nelle funzioni residuo ottenuto con la SVD e la cSVD. PD permette inoltre di ricavare stime accurate di CBF, sia in presenza che in assenza di dispersione nella AIF, fornendo risultati migliori rispetto alla SVD e alla cSVD. PD, SVD e cSVD sono stati inoltre utilizzati per l’analisi di dati reali e sono stati confrontati anche con NSR. Le mappe di CBF e MTT ottenute tramite PD presentano un livello di contrasto migliore rispetto a quelle ottenute con SVD e cSVD, enfatizzando maggiormente le aree caratterizzate da un diverso flusso ematico. Anche le mappe ottenute tramite NSR presentano un alto livello di contrasto, risultando però più rumorose rispetto a quelle ottenute tramite PD. Si è inoltre introdotto un nuovo indicatore fisiologico, l’indice di lateralità, che permette di fornire una rappresentazione grafica e di integrare le informazioni contenute nelle mappe di CBF e MTT. NSR fornisce valori di lateralità molto ampi, evidenziando notevolmente le zone caratterizzate da diversi flussi ematici. Tuttavia, l’individuazione delle aree colpite dalla patologia è resa difficoltosa dal fatto che anche le aree sane sono caratterizzate da ampi indici di lateralità. L’opposto si verifica considerando gli indici di lateralità ottenuti tramite SVD o cSVD; in questo caso l’individuazione delle aree malate è resa difficile dal fatto che gli indici forniti sono molto piccoli. PD invece permette di ottenere degli indici di lateralità che evidenziano le aree malate più di quanto non facciano SVD o cSVD, ma con valori meno ampi rispetto a NSR, soprattutto nelle regioni sane. In questo modo, PD permette di ottenere diagnosi più accurate. Infine, in questo lavoro viene presentato un ulteriore promettente metodo di deconvoluzione, chiamato DNP. A differenza di PD, che deve essere utilizzato per l’analisi di un elevato numero di voxel a causa dell’approccio di popolazione, DNP è un metodo di deconvoluzione di singoli voxel, quindi può essere applicato anche all’analisi di regioni contenenti pochi voxel. L’aspetto più interessante del metodo DNP è che permette di tenere conto sia del fatto che la funzione residuo deve essere continua, sia del fatto che un sistema fisiologico è, naturalmente, BIBO stabile. Inoltre, tale metodo permette di stimare anche il ritardo normalmente presente nella AIF, migliorando la precisione nella stima della funzione residuo. Dato che il metodo è ancora in fase di sviluppo, nella tesi sono riportati solo dei risultati preliminari. Tali risultati mostrano che DNP è in grado di fornire stime di CBF più accurate rispetto a SVD e cSVD, sia in presenza che in assenza di dispersione e ritardo. Inoltre, le funzioni residuo ottenute tramite DNP non presentano valori negativi e le oscillazioni non fisiologiche generalmente presenti nei risultati forniti da SVD e cSVD. D’altro canto, DNP presenta ancora dei problemi, il più importante dei quali è il calcolo del ritardo nella AIF, poco preciso e generalmente sovrastimato, soprattutto in presenza di dispersione. Inoltre, DNP non riesce ancora a caratterizzare bene l’andamento della funzione residuo. Un altro problema non ancora risolto è legato alla stima degli iper-parametri. Infatti questo aspetto richiede alcuni passaggi non lineari che incrementano notevolmente i tempi di calcolo necessari all’algoritmo. In conclusione, anche se presenta ancora numerosi limiti nella fase di analisi del segnale, la DSC-MRI sta diventando uno strumento molto importante sia nella pratica clinica che nella fase di ricerca medica. Gli algoritmi di selezione della AIF e di deconvoluzione che sono stati proposti in questa tesi permettono di migliorare l’informazione clinica e scientifica che si può ottenere dall’analisi dei dati ottenuti tramite DSC-MRI.

Tumour tissue is highly heterogeneous with disordered vasculature that is characteristically highly permeable relative to other normal tissue blood vessels. Non-invasive investigation of tumour vasculature may be achieved using Dynamic Contrast Enhanced MRI (DCE-MRI). Pharmacokinetic modelling of contrast agent uptake can provide information about blood flow and vessel permeability, but modelling is limited due to the ability of typical contrast agents such as Gd-DTPA to extravasate and accumulate in tumour tissue. The hypothesis motivating this work is that DCE-MRI measurements with both high and low molecular weight contrast agent uptake will allow for improved interpretation of the tumour micro-environment. A new high molecular weight contrast agent comprised of hyperbranched polyglycerol (HPG) molecules doubly labelled with gadolinium and a fluorescent marker is characterized, and used along side a standard low molecular weight contrast agent, Gadovist (Bayer Healthcare). Histological data reveals that HPG extravasates slowly from vasculature, and remains near blood vessels over the time-frame of a DCE-MRI experiment. HPG was also found to accumulate in tumour tissue over days, peaking at 2-4 days. HPG was found to be inappropriate for pharmacokinetic modelling, due to relatively low enhancement in the DCE-MRI data. Parameter maps showing bolus arrival time of HPG throughout the tumour show increased sensitivity to necrosis relative to Gadovist. Initial area under the HPG-concentration time curve was found to be correlated with vascular density. Modelling of DCE-MRI data should be performed with a model appropriate to the tissue, contrast agent, and data available. While simpler models are not able to distinguish blood flow from permeability, data quality is not necessarily sufficient to justify the use of a more complex model. This problem is addressed in this work by modelling contrast agent uptake with system of increasingly complex models, and the Akaike information criterion was used to determine that a general two compartment exchange model was more appropriate than the extended Tofts model for pharmacokinetic modelling of DCE-MRI with a standard contrast agent.

Renal phase-contrast magnetic resonance imaging (PC-MRI) is an MRI modality where the phase component of the MR signal is made sensitive to the velocity of water molecules in the kidneys. PC-MRI is able to assess the Renal Blood Flow (RBF), which is an important biomarker in the development of kidney disease. RBF is analyzed with the manual or semi-automatic delineation by experts of the renal arteries in PC-MRI. This is a time-consuming and operator-dependent process. We have therefore trained, validated and tested a fully-automated deep learning model for faster and more objective renal artery segmentation. The PC-MRI data used in model training, validation and testing come from four studies (N=131 subjects). Images were acquired from three manufacturers with different imaging parameters. The best deep learning model found consists of a deeply-supervised 2D attention U-Net with residual skip connections. The output of this model was re-introduced as an extra channel in a second iteration to refine the segmentation result. The flow values in the segmented regions were integrated to provide a quantification of the mean arterial flow in the segmented renal arteries. The automated segmentation was evaluated in all the images that had manual segmentation ground-truths that come from a single operator. The evaluation was completed in terms of a segmentation accuracy metric called Dice Coefficient. The mean arterial flow values that were quantified from the auto-mated segmentation were also evaluated against ground-truth flow values from semi-automatic software. The deep learning model was trained and validated on images with segmentation ground-truths with 4-fold cross-validation. A Dice segmentation accuracy of 0.71±0.21 was achieved (N=73 subjects). Although segmentation results were accurate for most arteries, the algorithm failed in ten out of 144arteries. The flow quantification from the segmentation was highly correlated without significant bias in comparison to the ground-truth flow measurements. This method shows promise for supporting RBF measurements from PC-MRI and can likely be used to save analysis time in future studies. More training data has to be used for further improvement, both in terms of accuracy and generalizability.

Microvascular permeability is a pharmacologic indicator of tumor response to therapy, and it is expected that this biomarker will evolve into a clinical surrogate endpoint and be integrated into protocols for determining patient response to antiangiogenic or antivascular therapies. The goal of this research is to develop a method by which microvascular permeability (Ktrans) and vascular volume (vp) as measured by DCE-MRI were directly compared to the same parameters measured by intravital fluorescence microscopy in an MRI-compatible window chamber model. Dynamic contrast enhanced-MRI (DCE-MRI) is a non-invasive, clinically useful imaging approach that has been used extensively to measure active changes in tumor microvascular hemodynamics. However, uncertainties exist in DCE-MRI as it does not interrogate the contrast reagent (CR) itself, but the effect of the CR on tissue water relaxivity. Thus, direct comparison of DCE-MRI with a more quantitative measure would help better define the derived parameters. The combined imaging system was able to obtain both dynamic contrast-enhanced MRI data high spatio-termporal resolution fluorescence data following injection of fluorescent and gadolinium co-labeled albumin. This approach allowed for the cross-validation of vascular permeability data, in relation tumor growth, angiogenesis and response to therapy in both imaging systems.

Today, infrared cameras are used especially for target tracking and surveillance operations. However, they have a high dynamic range output, and the standard display devices cannot handle them. In order to show them on common devices, the dynamic range is cropped. Thus, the contrast of the image is reduced. This is called as the High Dynamic Range (HDR) Compression. Although several algorithms have been proposed for preserving details during the HDR compression process, it cannot be used to enhance the local contrasts of image contents. In this thesis, we compare the performances of contrast enhancement techniques, which are suitable for real time applications. The methods experimented are generally histogram based methods. Some modifications are also proposed in order to reduce computational complexity of the process. Performances of these methods are compared with common objective quality metrics on different image sets.

 

Acquired data from dynamic contrast enhanced MRI measurements can be used to non-invasively assess tumour vascular characteristics through pharmacokinetic modelling. The modelling requires an arterial input function which is the concentration of contrast agent in the blood reaching the volume of interest as a function of time. The aim of this work is testing and optimizing a turboFLASH sequence to appraise its suitability for measuring the arterial input function by measuring phase.

Contrast concentration measurements in a phantom were done with both phase and relaxivity techniques. The results were compared to simulations of the experiment conditions to compare the conformance. The results using the phase technique were promising, and the method was carried on to in-vivo testing. The in-vivo data displayed a large signal loss which motivated a new phantom experiment to examine the cause of this signal reduction. Dynamic measurements were made in a phantom with pulsatile flow to mimic a blood vessel with a somewhat modified turboFLASH sequence. The conclusions drawn from analyzing the data were used to further improve the sequence and this modified turboFLASH sequence was tested in an in-vivo experiment. The obtained concentration curve showed significant improvement and was deemed to be a good representation of the true blood concentration.

The conclusion is that phase measurements can be recommended over relaxivity based measurements. This recommendation holds for using a slice selective saturation recovery turboFLASH sequence and measuring the arterial input function in the neck. Other areas of application need more thorough testing.

 

L'objectif de ce travail était de développer des méthodes pour permettre une évaluation in vivo plus robuste du réseau vasculaire dans la tumeur par imagerie de contraste ultrasonore. Trois aspects de l'analyse de donnée ont été abordé: 1) la régression des modèles paramétriques de flux sur les données de puissance linéaire, 2) la compensation du mouvement 3) l’évaluation d’une méthode de clustering pour identifier les hétérogénéités dans les tumeurs. Un modèle multiplicatif est proposé pour décrire le signal DCE-US. Une méthode de régression en est dérivée. La caractérisation du signal permet la mise au point d’une méthode de simulation de séquences 2D+T. La méthode de régression permet une diminution de la variabilité des paramètres de flux fonctionnels extraits, sur données simulées expérimentales. La méthode de simulation est appliquée pour évaluer une méthode combinant estimation du mouvement et estimations des paramètres micro-vasculaires dans un unique problème mathématique d'optimisation. Cette nouvelle méthode présente en plus l'avantage d'être indépendante de l'opérateur. Il est montré que dans une large majorité des cas l'estimation du mouvement est meilleure avec la nouvelle méthode qu'avec une méthode de références. Une méthode de clustering est adaptée et évaluée sur données DCE-US simulées et in-vivo. Elle permet de détecter des hétérogénéités dans la structure vasculaire des tumeurs. Les méthodes développées permettent d'améliorer l’évaluation du réseau microvasculaire par DCE-US grâce à une description rigoureuse du signal, à la mise au point d'outil diminuant l'intervention de l'opérateur et la prise en compte de l'hétérogénéité du réseau vasculaire
This work aimed to develop methods to robustly evaluate in vivo functional flow within the tumor vascular network with Dynamic contrast-enhanced ultrasound (DCE-US). Three aspects of data analysis were addressed: 1) insuring best fit between parametric flow models and the experimentally acquired echo-power curves, 2) compensating sequences for motion and 3) evaluating a method to discriminate between tissues with different functional flow. A multiplicative model is proposed to describe the DCE-US signal. Based on this model, a new parametric regression method of the signal is derived. Characterization of the statistical properties of the noise and signal is also used to develop a new method simulating contrast-enhanced ultrasound 2D+t sequences. A significant decrease in the variability of the functional flow parameters extracted according to the new multiplicative-noise fitting method is demonstrated using both simulated and experimentally-acquired sequences. The new sequence simulations are applied to test a method combining motion estimation and flow-parameter estimation within a single mathematical framework. Because this new method does not require the selection of a reference image, it reduces operator intervention. Tests of the method on both simulations and clinical data and demonstrate in a majority of sequences a more accurate motion estimation than the commonly used image registration method. Finally, a non-parametric method for perfusion curve clustering is evaluated on 2D+t sequences. The aim of this method is to regroup similar filling patterns without a priori knowledge about the patterns. The method is tested on simulated and on pre-clinical data

L'imagerie de perfusion permet un accès non invasif à la micro-vascularisation tissulaire. Elle apparaît comme un outil prometteur pour la construction de biomarqueurs d'imagerie pour le diagnostic, le pronostic ou le suivi de traitement anti-angiogénique du cancer. Cependant, l'analyse quantitative des séries dynamiques de perfusion souffre d'un faible rapport signal sur bruit (SNR). Le SNR peut être amélioré en faisant la moyenne de l'information fonctionnelle dans de grandes régions d'intérêt, qui doivent néanmoins être fonctionnellement homogènes. Pour ce faire, nous proposons une nouvelle méthode pour la segmentation automatique des séries dynamiques de perfusion en régions fonctionnellement homogènes, appelée DCE-HiSET. Au coeur de cette méthode, HiSET (Hierarchical Segmentation using Equivalence Test ou Segmentation hiérarchique par test d'équivalence) propose de segmenter des caractéristiques fonctionnelles ou signaux (indexées par le temps par exemple) observées discrètement et de façon bruité sur un espace métrique fini, considéré comme un paysage, avec un bruit sur les observations indépendant Gaussien de variance connue. HiSET est un algorithme de clustering hiérarchique qui utilise la p-valeur d'un test d'équivalence multiple comme mesure de dissimilarité et se compose de deux étapes. La première exploite la structure de voisinage spatial pour préserver les propriétés locales de l'espace métrique, et la seconde récupère les structures homogènes spatialement déconnectées à une échelle globale plus grande. Etant donné un écart d'homogénéité $\delta$ attendu pour le test d'équivalence multiple, les deux étapes s'arrêtent automatiquement par un contrôle de l'erreur de type I, fournissant un choix adaptatif du nombre de régions. Le paramètre $\delta$ apparaît alors comme paramètre de réglage contrôlant la taille et la complexité de la segmentation. Théoriquement, nous prouvons que, si le paysage est fonctionnellement constant par morceaux avec des caractéristiques fonctionnelles bien séparées entre les morceaux, HiSET est capable de retrouver la partition exacte avec grande probabilité quand le nombre de temps d'observation est assez grand. Pour les séries dynamiques de perfusion, les hypothèses, dont dépend HiSET, sont obtenues à l'aide d'une modélisation des intensités (signaux) et une stabilisation de la variance qui dépend d'un paramètre supplémentaire $a$ et est justifiée a posteriori. Ainsi, DCE-HiSET est la combinaison d'une modélisation adaptée des séries dynamiques de perfusion avec l'algorithme HiSET. A l'aide de séries dynamiques de perfusion synthétiques en deux dimensions, nous avons montré que DCE-HiSET se révèle plus performant que de nombreuses méthodes de pointe de clustering. En terme d'application clinique de DCE-HiSET, nous avons proposé une stratégie pour affiner une région d'intérêt grossièrement délimitée par un clinicien sur une série dynamique de perfusion, afin d'améliorer la précision de la frontière des régions d'intérêt et la robustesse de l'analyse basée sur ces régions tout en diminuant le temps de délimitation. La stratégie de raffinement automatique proposée est basée sur une segmentation par DCE-HiSET suivie d'une série d'opérations de type érosion et dilatation. Sa robustesse et son efficacité sont vérifiées grâce à la comparaison des résultats de classification, réalisée sur la base des séries dynamiques associées, de 99 tumeurs ovariennes et avec les résultats de l'anapathologie sur biopsie utilisés comme référence. Finalement, dans le contexte des séries d'images 3D, nous avons étudié deux stratégies, utilisant des structures de voisinage des coupes transversales différentes, basée sur DCE-HiSET pour obtenir la segmentation de séries dynamiques de perfusion en trois dimensions. (...)
Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarker for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in large regions of interest, which however need to be functionally homogeneous. To achieve SNR improvement, we propose a novel method for automatic segmentation of DCE image sequence into functionally homogeneous regions, called DCE-HiSET. As the core of the proposed method, HiSET (Hierarchical Segmentation using Equivalence Test) aims to cluster functional (e.g. with respect to time) features or signals discretely observed with noise on a finite metric space considered to be a landscape. HiSET assumes independent Gaussian noise with known constant level on the observations. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to preserve the local property of the metric space, and the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given an expected homogeneity discrepancy $\delta$ for the multiple equivalence test, both steps stop automatically through a control of the type I error, providing an adaptive choice of the number of clusters. Parameter $\delta$ appears as the tuning parameter controlling the size and the complexity of the segmentation. Assuming that the landscape is functionally piecewise constant with well separated functional features, we prove that HiSET will retrieve the exact partition with high probability when the number of observation times is large enough. In the application for DCE image sequence, the assumption is achieved by the modeling of the observed intensity in the sequence through a proper variance stabilization, which depends only on one additional parameter $a$. Therefore, DCE-HiSET is the combination of this DCE imaging modeling step with our statistical core, HiSET. Through a comparison on synthetic 2D DCE image sequence, DCE-HiSET has been proven to outperform other state-of-the-art clustering-based methods. As a clinical application of DCE-HiSET, we proposed a strategy to refine a roughly manually delineated ROI on DCE image sequence, in order to improve the precision at the border of ROIs and the robustness of DCE analysis based on ROIs, while decreasing the delineation time. The automatic refinement strategy is based on the segmentation through DCE-HiSET and a series of erosion-dilation operations. The robustness and efficiency of the proposed strategy are verified by the comparison of the classification of 99 ovarian tumors based on their associated DCE-MR image sequences with the results of biopsy anapathology used as benchmark. Furthermore, DCE-HiSET is also adapted to the segmentation of 3D DCE image sequence through two different strategies with distinct considerations regarding the neighborhood structure cross slices. This PhD thesis has been supported by contract CIFRE of the ANRT (Association Nationale de la Recherche et de la Technologie) with a french company INTRASENSE, which designs, develops and markets medical imaging visualization and analysis solutions including Myrian®. DCE-HiSET has been integrated into Myrian® and tested to be fully functional

Cette thèse cherche à étudier les liens existants entre les contrats formels et les comportements coopératifs. Les contrats formels étant censés faciliter la collaboration et éviter des incompréhensions coûteuses entre les parties, nous nous intéressons dans un premier temps à la manière dont le contrat formel impacte sur la capacité des parties à coopérer. Nous analysons ensuite, dans un même ordre d’idée, la coopération à travers le prisme des renégociations contractuelles. L’objectif est alors d’étudier la façon dont les contrats s’adaptent à un environnement changeant à travers le temps. Enfin, l’étude se porte sur l’impact de l’existence de rapports informels, considérés par la littérature antérieure comme, de manière alternative, substitut ou complément aux contrats formels. Plus précisément, nous étudions comment l’existence de mécanismes relationnels influe sur les choix contractuels. Ainsi, l’objectif est d’améliorer la compréhension du rôle joué par les contrats formels et la coopération informelle dans les relations et d’enrichir la théorie sur les déterminants de l’incomplétude contractuelle. Nos résultats suggèrent que le rôle du contrat formel dans les relations dépend fortement du contexte et de l’identité des parties concernées. Nos résultats permettent également d’identifier la capacité des parties à soutenir un accord relationnel comme une nouvelle source endogène d’incomplétude contractuelle. Enfin, nous obtenons également que les adaptations contractuelles, par le biais des renégociations, ne sont pas nécessairement nocives pour les parties. Au final, nous pensons que cette thèse contribue à la littérature sur le débat entre complémentarité et / ou substituabilité des modes de gouvernance formels et informels ainsi qu’à la littérature sur le lien entre contrat relationnel et l’incomplétude contractuelle endogène. Par conséquent, l’implication majeure de ce travail de thèse est illustrée par la nécessité pour les parties de réfléchir attentivement au contrat initial et aux efforts consentis pour sa rédaction. En premier lieu car le contrat formel impacte sur leur capacité à coopérer ex post et, en second lieu, car le contrat formel peut se révéler être trop complet par rapport au niveau de complétude optimal
This Ph.D. dissertation seeks to investigate the existing links between cooperative behavior and formal contracts. First, because formal agreements are supposed to facilitate smooth collaboration and avoid costly misunderstandings, we are interested in how formal contracts impact on the ability of parties to cooperate. Following the same intuition, we also analyze cooperation through the lens of renegotiations in order to investigate how contracts adapt themselves through time in a changing environment. Second, we also study the impact of the existence of informal dealings, alternatively considered in previous literature as substitute or complement to formal contracting. More precisely, we aim to investigate how the existence of relational mechanisms may impact on contractual choices. Our goal is thus to improve the understandings of the role played by formal contract and informal cooperation in relationships and to enrich the theory of the determinants of incomplete contract. Our results suggest that the role of formal contract in relationships strongly depends on the context and the identity of parties. Our results also identify the ability of the parties to sustain a relational agreement as a new source of endogenous contractual incompleteness. Finally, we also find that adaptations through contractual renegotiations are not necessarily harmful for the contracting parties. We believe that this Ph.D. dissertation contributes to the literature on the debate of complementarity and/or the substitutability of formal and informal governance and to the literature on the link between relational contract and endogenous contractual completeness. In the end, the overall implication is the necessity for parties to carefully think about the initial contract they draft. Because it has an impact on their ability to cooperate ex post and also because contracts can be over-complete compared to the efficient (i.e. socially optimal) level of completeness

Background: Use of contrast enhanced cardiac magnetic resonance imaging (MRI) for identification of focal pathology (perfusion deficit and scar) is widespread. Quantitative analysis of dynamic contrast enhanced (DCE) MRI data may allow objective assessment of focal and diffuse disease. However it is a complex process and not widely adopted outside the research domain. For accurate quantification temporal variation in relative contrast agent concentration in the myocardium and feeding blood supply must be measured. While MRI signal intensity can be used as a probe of contrast agent concentration its response is non-linear. Aims: In this thesis non-linearity correction methods for quantitative myocardial DCE-MRI are compared, the feasibility of a novel bookend T1 based correction is tested and the method is used in clinical studies to assess myocardial characteristics in health and ischaemic disease. Methods: Signal non-linearity correction methods were compared using simulation, phantom experiments and a volunteer study. Methods compared were independent sampling strategies (dual-bolus and dual-sequence), previously proposed model based correction (native T1 or proton density weighted image based) and bookend T1 based correction which is proposed as a method to account for imperfect magnetisation preparation. The feasibility of the bookend T1 method was tested and characteristics of heathy and diseased myocardium were assessed in clinical studies of ischaemia and infarction. Conclusions: Native T1 based correction has been found to be highly sensitive to imperfect magnetisation preparation, and is thus recommended against. Model based correction using proton density weighted images or bookend T1 data have been found to be more accurate and precise than dual-sampling methods. The clinical studies have demonstrated the feasibility of the bookend T1 based method and have yielded insights into myocardial characteristics in a range of conditions.