Functional Imaging of Malignant Gliomas with CT Perfusion

The overall survival of patients with malignant gliomas remains dismal despite multimodality treatments. Computed tomography (CT) perfusion is a functional imaging tool for assessing tumour hemodynamics. The goals of this thesis are to 1) improve measurements of various CT perfusion parameters and 2) assess treatment outcomes in a rat glioma model and in patients with malignant gliomas. Chapter 2 addressed the effect of scan duration on the measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS). Measurement errors of these parameters increased with shorter scan duration. A minimum scan duration of 90 s is recommended. Chapter 3 evaluated the improvement in the measurements of these parameters by filtering the CT perfusion images with principal component analysis (PCA). From computer simulation, measurement errors of BF, BV, and PS were found to be reduced. Experiments showed that CT perfusion image contrast-to-noise ratio was improved. Chapter 4 investigated the efficacy of CT perfusion as an early imaging biomarker of response to stereotactic radiosurgery (SRS). Using the C6 glioma model, we showed that responders to SRS (surviving \u3e 15 days) had lower relative BV and PS on day 7 post-SRS when compared to controls and non-responders (P \u3c 0.05). Relative BV and PS on day 7 post-SRS were predictive of survival with 92% accuracy. Chapter 5 examined the use of multiparametric imaging with CT perfusion and 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) to identify tumour sites that are likely to correlate with the eventual location of tumour progression. We developed a method to generate probability maps of tumour progression based on these imaging data. Chapter 6 investigated serial changes in tumour volumetric and CT perfusion parameters and their predictive ability in stratifying patients by overall survival. Pre-surgery BF in the non-enhancing lesion and BV in the contrast-enhancing lesion three months after radiotherapy had the highest combination of sensitivities and specificities of ≥ 80% in predicting 24 months overall survival. iv Optimization and standardization of CT perfusion scans were proposed. This thesis also provided corroborating evidence to support the use of CT perfusion as a biomarker of outcomes in patients with malignant gliomas

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion

Integration of anatomical and hemodynamical information in magnetic resonance angiography

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Preclinical MRI of the Kidney

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Unmixing dynamic PET images with variable specific binding kinetics

To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal component analysis (PCA), independent component analysis (ICA), factor analysis and nonnegative matrix factorization (NMF). Nevertheless, these conventional approaches neglect any possible nonlinear variations in the time activity curves describing the kinetic behavior of tissues with specific binding, which limits their ability to recover a reliable, understandable and interpretable description of the data. This paper proposes an alternative analysis paradigm that accounts for spatial fluctuations in the exchange rate of the tracer between a free compartment and a specifically bound ligand compartment. The method relies on the concept of linear unmixing, usually applied on the hyperspectral domain, which combines NMF with a sum-to-one constraint that ensures an exhaustive description of the mixtures. The spatial variability of the signature corresponding to the specific binding tissue is explicitly modeled through a perturbed component. The performance of the method is assessed on both synthetic and real data and is shown to compete favorably when compared to other conventional analysis methods

Preclinical MRI of the kidney : methods and protocols

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Development of novel methods for obtaining robust dynamic susceptibility contrast magnetic resonance imaging biomarkers from diseased brain in children

Dynamic susceptibility contrast (DSC-) MRI is an important imaging technique from which estimates of perfusion measures including cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) can be calculated. These perfusion measures can be used to indicate health in a range of diseases. However, acquisition protocol varies from centre-to-centre, which leads to variability in data quality between centres and limits the clinical applicability of DSC-MRI. Currently, the recommended process for assessing data quality is by eye, which is very time consuming and subjective between reviewers. In this work an automated processing pipeline for DSC-MRI was produced. Work to develop the pipeline demonstrated that data quality of DSC-MRI data can be assessed using machine learning classifiers, which were trained using metrics calculated from the data and the results of qualitative review. It also showed that it was possible to denoise the data using singular value decomposition (SVD) based methods, which were validated on a simulator and confirmed in patient data. The pipeline created was applied to a multicentre patient dataset where it demonstrated the importance of denoising DSC-MRI data in improving data quality and how data quality can vary with acquisition protocol. It was also applied to a single centre study of patients receiving differing treatments for brain tumours and suggested there are no significant changes in relative CBV (rCBV) in non-tumour brain between differing treatment groups. The pipeline developed during this work has wider applications in other imaging modalities and could be adapted to be applied to other perfusion imaging methods, such as dynamic contrast enhanced (DCE-) MRI, or any other imaging modality that involves analysis of a signal variation with time, such as computed tomography (CT) perfusion imaging or positron emission tomography (PET)

Methods for assisting the automation of Dynamic Susceptibility Contrast Magnetic Resonance Imaging Analysis

Purpose Dynamic susceptibility-contrast magnetic resonance imaging (DSC-MRI) is widely used for cerebral perfusion measurement, but dependence on operator input leads to a time-consuming, subjective, and poorly-reproducible analysis. Although automation can overcome these limitations, investigations are required to further simplify and accelerate the analysis. This research focuses on automating arterial voxel (AV) and brain tissue segmentation, and model-dependent deconvolution steps of DSC-MRI analysis. Methods Several features were extracted from DSC-MRI data; their AV- and tissue voxel- discriminatory powers were evaluated by the area-under-the-receiver-operating-characteristic-curve (AUCROC). Thresholds for discarding non-arterial voxels were identified using ROC cut-offs. The applicability of DSC-MRI time-series data for brain segmentation was explored. Two segmentation approaches that clustered the dimensionality-reduced raw data were compared with two raw−data-based approaches, and an approach using principal component analysis (PCA) for dimension-reduction. Computation time and Dice coefficients (DCs) were compared. For model-dependent deconvolution, four parametric transit time distribution (TTD) models were compared in terms of goodness- and stability-of-fit, consistency of perfusion estimates, and computation time. Results Four criteria were effective in distinguishing AVs, forming the basis of a framework that can determine optimal thresholds for effective criteria to discard tissue voxels with high sensitivity and specificity. Compared to raw−data-based approaches, one of the proposed segmentation approaches identified GM with higher (>0.7, p<0.005), and WM with similar DC. The approach outperformed the PCA-based approach for all tissue regions (p<0.005), and clustered similar regions faster than other approaches (p<0.005). For model-dependent deconvolution, all TTD models gave similar perfusion estimates and goodness-of-fit. The gamma distribution was most suitable for perfusion analysis, showing significantly higher fit stability and lower computation time. Conclusion The proposed methods were able to simplify and accelerate automatic DSC-MRI analysis while maintaining performance. They will particularly help clinicians in rapid diagnosis and characterisation of tumour or stroke lesions, and subsequent treatment planning and monitoring