Level-Set Based Artery-Vein Separation in Blood Pool Agent CE-MR Angiograms

Blood pool agents (BPAs) for contrast-enhanced (CE) magnetic-resonance angiography (MRA) allow prolonged imaging times for higher contrast and resolution. Imaging is performed during the steady state when the contrast agent is distributed through the complete vascular system. However, simultaneous venous and arterial enhancement in this steady state hampers interpretation. In order to improve visualization of the arteries and veins from steady-state BPA data, a semiautomated method for artery-vein separation is presented. In this method, the central arterial axis and central venous axis are used as initializations for two surfaces that simultaneously evolve in order to capture the arterial and venous parts of the vasculature using the level-set framework. Since arteries and veins can be in close proximity of each other, leakage from the evolving arterial (venous) surface into the venous (arterial) part of the vasculature is inevitable. In these situations, voxels are labeled arterial or venous based on the arrival time of the respective surface. The evolution is steered by external forces related to feature images derived from the image data and by internal forces related to the geometry of the level sets. In this paper, the robustness and accuracy of three external forces (based on image intensity, image gradient, and vessel-enhancement filtering) and combinations of them are investigated and tested on seven patient datasets. To this end, results with the level-set-based segmentation are compared to the reference-standard manually obtained segmentations. Best results are achieved by applying a combination of intensity- and gradient-based forces and a smoothness constraint based on the curvature of the surface. By applying this combination to the seven datasets, it is shown that, with minimal user interaction, artery-vein separation for improved arterial and venous visualization in BPA CE-MRA can be achieved

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

Doctor of Philosophy

dissertationMagnetic resonance guided high intensity focused ultrasound (MRgHIFU) is a promising minimal invasive thermal therapy for the treatment of breast cancer. This study develops techniques for determining the tissue parameters - tissue types and perfusion rate - that influence the local temperature during HIFU thermotherapy procedures. For optimal treatment planning for each individual patient, a 3D volumetric breast tissue segmentation scheme based on the hierarchical support vector machine (SVM) algorithm was developed to automatically segment breast tissues into fat, fibroglandular tissue, skin and lesions. Compared with fuzzy c-mean and conventional SVM algorithm, the presented technique offers tissue classification performance with the highest accuracy. The consistency of the segmentation results along both the sagittal and axial orientations indicates the stability of the proposed segmentation routine. Accurate knowledge of the internal anatomy of the breast can be utilized in the ultrasound beam simulation for the treatment planning of MRgHIFU therapy. Completely noninvasive MRI techniques were developed for visualizing blood vessels and determining perfusion rate to assist in the MRgHIFU therapy. Two-point Dixon fat-water separation was achieved using a 3D dual-echo SSFP sequence for breast vessel imaging. The performances of the fat-water separation with various readout gradient designs were evaluated on a water-oil phantom, ex vivo pork sample and in vivo breast imaging. Results suggested that using a dual-echo SSFP readout with bipolar readout gradient polarity, blood vasculature could be successfully visualized through the thin-slab maximum intensity projection SSFP water-only images. For determining the perfusion rate, we presented a novel imaging pulse sequence design consisting of a single arterial spin labeling (ASL) magnetization preparation followed by Look-Locker-like image readouts. This flow quantification technique was examined through simulation, in vitro and in vivo experiments. Experimental results from a hemodialyzer when fitted with a Bloch-equation-based model provide flow measurements that are consistent with ground truth velocities. With these tissue properties, it is possible to compensate for the dissipative effects of the flowing blood and ultimately improve the efficacy of the MRgHIFU therapies. Complete noninvasiveness of these techniques allows multiple measurements before, during and after the treatment, without the limitation of washout of the injected contrast agent

Improved modelling of the human cerebral vasculature

Ph.DDOCTOR OF PHILOSOPH

Compression of 4D medical image and spatial segmentation using deformable models

Ph.DDOCTOR OF PHILOSOPH

Vessel-based brain-shift compensation using elastic registration driven by a patient-specific finite element model

International audienceDuring brain tumor surgery, planning and guidance are based on pre-operative images which do not account for brain-shift.However, this shift is a major source of error in neuro-navigation systems and affects the accuracy of the procedure. The vascular tree is extracted from pre-operative Magnetic Resonance Angiography and from intra-operative Doppler ultrasound images, which provides sparse information on brain deformations.The pre-operative images are then updated based on an elastic registration of the blood vessels, driven by a patient-specific biomechanical model.This biomechanical model is used to extrapolate the deformation to the surrounding soft tissues.Quantitative results on a single surgical case are provided, with an evaluation of the execution time for each processing step.Our method is proved to be efficient to compensate for brain deformation while being compatible with a surgical process

MRI changes in visceral fat in Crohn’s Disease

Crohn’s Disease (CD) is a chronic inflammatory disease of the gastrointestinal tract affecting 115,000 people in the UK alone. As a chronic illness, CD management requires a stepwise escalation of treatment measures tethered with constant monitoring of the disease activity levels and progression. Hence, non-invasive disease activity assessment methods form an essential part of the treatment process. Endoscopy is considered to be the traditional method for CD diagnosis and disease activity assessment which is invasive and may be uncomfortable for patients. As traditional MRI-based disease assessment methods rely on intravenous gadolinium for contrast enhancement, CD patients typically undergo repeated exposure to gadolinium administration which adds cost and carries the risk of nephrogenic systemic fibrosis, allergic reaction, and long-term brain deposition following repeated use. Hence, the development of contrast-free MRI-based disease activity metrics eliminates the risks associated with gadolinium and allows for a more frequent assessment of the disease progression. However, all developed cross-sectional CD activity metrics so far rely on a visual assessment by radiologists which can be subjective and time-consuming. The aim of this thesis is to examine established radiological hallmarks of CD and employ MRI imaging sequences along with image processing techniques to generate objective and quantitative disease activity measurements. The first part of this thesis investigates visceral fat hypertrophy also known as fat wrapping which refers to an abnormal growth of the mesenteric fat to partially cover the small or large intestine. While fat wrapping has been established as a characteristic of CD, the complex nature of visceral fat hinders detailed analysis of the effect of fat wrapping. Hence, an automated abdominal fat segmentation algorithm was developed to generate an objective measure of abdominal fat volumes which was used to study the differences in visceral fat revealing significant differences between CD patients and healthy volunteers. The second part of the thesis examines mesenteric blood flow in CD patients. CD is known to be associated with hypervascularity of the mesentery, including vascular dilation and wide spacing of the vasa recta. The arteries supply the small bowel branch to a series of intestinal arteries within the mesentery. A second algorithm was developed to automatically trace abdominal vessels on a time-of-flight MRA scans and measure the number of vessels’ branching points which also revealed significant differences between CD patients and HVs. This research has demonstrated the potential for MRI and image processing techniques to provide objective and quantitative measurements of disease activity in CD. The development of automated algorithms for abdominal fat segmentation and vessel tracing allows for a more accurate and efficient assessment of key radiological hallmarks of CD which are often overlooked. These techniques have the potential to improve the management of CD by providing non-invasive and more frequent assessments of disease activity and progression, without the risks associated with traditional contrast-enhanced methods

Brain vasculature segmentation from magnetic resonance angiographic image

Master'sMASTER OF ENGINEERIN