Need for speed:Achieving fast image processing in acute stroke care

This thesis aims to investigate the use of high-performance computing (HPC) techniques in developing imaging biomarkers to support the clinical workflow of acute stroke patients. In the first part of this thesis, we evaluate different HPC technologies and how such technologies can be leveraged by different image analysis applications used in the context of acute stroke care. More specifically, we evaluated how computers with multiple computing devices can be used to accelerate medical imaging applications in Chapter 2. Chapter 3 proposes a novel data compression technique that efficiently processes CT perfusion (CTP) images in GPUs. Unfortunately, the size of CTP datasets makes data transfers to computing devices time-consuming and, therefore, unsuitable in acute situations. Chapter 4 further evaluates the algorithm's usefulness proposed in Chapter 3 with two different applications: a double threshold segmentation and a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in CTP scans. Finally, Chapter 5 presents a cloud platform for deploying high-performance medical applications for acute stroke patients. In Part 2 of this thesis, Chapter 6 presents a convolutional neural network (CNN) for detecting and volumetric segmentation of subarachnoid hemorrhages (SAH) in non-contrast CT scans. Chapter 7 proposed another method based on CNNs to quantify the final infarct volumes in follow-up non-contrast CT scans from ischemic stroke patients

A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.

Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging. Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 ± 7.3 years) over a span of 700 days. A novel segmentation algorithm was developed to identify brain vasculature from surrounding tissue. The data was processed to calculate the vascular probability distribution function (PDF); a measure of the vascular diameters in the brain. The initial (day 0) PDF and final (day 700) PDF were used to correlate the changes in cerebral vasculature and BP. Correlation was determined by a mixed effects linear model analysis. Results: The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to BP changes below the circle of Willis (p-value = 0.0007), but not significant (p-value = 0.53) above the circle of Willis, due to smaller blood vessels. Conclusion: Changes in cerebral vasculature and pressure can be non-invasively obtained through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Intelligent Imaging of Perfusion Using Arterial Spin Labelling

Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage

Dynamic Contrast-Enhanced MR Microscopy: Functional Imaging in Preclinical Models of Cancer

<p>Dynamic contrast-enhanced (DCE) MRI has been widely used as a quantitative imaging method for monitoring tumor response to therapy. The pharmacokinetic parameters derived from this technique have been used in more than 100 phase I trials and investigator led studies. The simultaneous challenges of increasing the temporal and spatial resolution, in a setting where the signal from the much smaller voxel is weaker, have made this MR technique difficult to implement in small-animal imaging. Existing preclinical DCE-MRI protocols acquire a limited number of slices resulting in potentially lost information in the third dimension. Furthermore, drug efficacy studies measuring the effect of an anti-angiogenic treatment, often compare the derived biomarkers on manually selected tumor regions or over the entire volume. These measurements include domains where the interpretation of the biomarkers may be unclear (such as in necrotic areas).</p><p>This dissertation describes and compares a family of four-dimensional (3D spatial + time), projection acquisition, keyhole-sampling strategies that support high spatial and temporal resolution. An interleaved 3D radial trajectory with a quasi-uniform distribution of points in k-space was used for sampling temporally resolved datasets. These volumes were reconstructed with three different k-space filters encompassing a range of possible keyhole strategies. The effect of k-space filtering on spatial and temporal resolution was studied in phantoms and in vivo. The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence. Finally, the technique was applied for measuring the extent of the opening of the blood-brain barrier in a mouse model of pediatric glioma and for identifying regions of therapeutic effect in a model of colorectal adenocarcinoma. </p><p>It is shown that 4D radial keyhole imaging does not degrade the system spatial and temporal resolution at a cost of 20-40% decrease in SNR. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted limits. The uncertainty in measuring the pharmacokinetic parameters with the sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time. The histogram of the time-to-peak provides useful knowledge about the spatial distribution of K^trans and microvascular density. Two regions with distinct kinetic parameters were identified when the TTP map from DCE-MRM was thresholded at 1000 sec. The effect of bevacizumab, as measured by a decrease in K^trans, was confined to one of these regions. DCE-MRI studies may contribute unique insights into the response of the tumor microenvironment to therapy.</p>Dissertatio

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus