mritc: A Package for MRI Tissue Classification

This paper presents an R package for magnetic resonance imaging (MRI) tissue classification. The methods include using normal mixture models, hidden Markov normal mixture models, and a higher resolution hidden Markov normal mixture model fitted by various optimization algorithms and by a Bayesian Markov chain Monte Carlo (MCMC) method. Functions to obtain initial values of parameters of normal mixture models and spatial parameters are provided. Supported input formats are ANALYZE, NIfTI, and a raw byte format. The function slices3d in misc3d is used for visualizing data and results. Various performance evaluation indices are provided to evaluate classification results. To improve performance, table lookup methods are used in several places, and vectorized computation taking advantage of conditional independence properties are used. Some computations are performed by C code, and OpenMP is used to parallelize key loops in the C code

Image Fusion and Axial Labeling of the Spine

In order to improve radiological diagnosis of back pain and spine disease, two new algorithms have been developed to aid the 75% of Canadians who will suffer from back pain in a given year. With the associated medical imaging required for many of these patients, there is a potential for improvement in both patient care and healthcare economics by increasing the accuracy and efficiency of spine diagnosis. A real-time spine image fusion system and an automatic vertebra/disc labeling system have been developed to address this. Both magnetic resonance (MR) images and computed tomography (CT) images are often acquired for patients. The MR image highlights soft tissue detail while the CT image highlights bone detail. It is desirable to present both modalities on a single fused image containing the clinically relevant detail. The fusion problem was encoded in an energy functional balancing three competing goals for the fused image: 1) similarity to the MR image, 2) similarity to the CT image and 3) smoothness (containing natural transitions). Graph-Cut and convex solutions have been developed. They have similar performance to each other and outperform other fusion methods from recent literature. The convex solution has real-time performance on modern graphics processing units, allowing for interactive control of the fused image. Clinical validation has been conducted on the convex solution based on 15 patient images. The fused images have been shown to increase confidence of diagnosis compared to unregistered MR and CT images, with no change in time for diagnosis based on readings from 5 radiologists. Spinal vertebrae serve as a reference for the location of surrounding tissues, but vertebrae have a very similar appearance to each other, making it time consume for radiologist to keep track of their locations. To automate this, an axial MR labeling algorithm was developed that runs in near real-time. Probability product kernels and fast integral images combined with simple geometric rules were used to classify pixels, slices and vertebrae. Evaluation was conducted on 32 lumbar spine images and 24 cervical spine images. The algorithm demonstrated 99% and 79% accuracy on the lumbar and cervical spine respectively

Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography

Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted

Boundary aware reconstruction of scalar fields

pre-printIn visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials' local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user's understanding of discrete features within the studied object

Doctor of Philosophy

dissertationHigh arterial tortuosity, or twistedness, is a sign of many vascular diseases. Some ocular diseases are clinically diagnosed in part by assessment of increased tortuosity of ocular blood vessels. Increased arterial tortuosity is seen in other vascular diseases but is not commonly used for clinical diagnosis. This study develops the use of existing magnetic resonance angiography (MRA) image data to study arterial tortuosity in a range of arteries of hypertensive and intracranial aneurysm patients. The accuracy of several centerline extraction algorithms based on Dijkstra's algorithm was measured in numeric phantoms. The stability of the algorithms was measured in brain arteries. A centerline extraction algorithm was selected based on its accuracy. A centerline tortuosity metric was developed using a curve of tortuosity scores. This tortuosity metric was tested on phantoms and compared to observer-based tortuosity rankings on a test data set. The tortuosity metric was then used to measure and compare with negative controls the tortuosity of brain arteries from intracranial aneurysm and hypertension patients. A Dijkstra based centerline extraction algorithm employing a distance-from-edge weighted center of mass (DFE-COM) cost function of the segmented arteries was selected based on generating 15/16 anatomically correct centerlines in a looping artery iv compared to 15/16 for the center of mass (COM) cost function and 7/16 for the inverse modified distance from edge cost function. The DFE-COM cost function had a lower root mean square error in a lopsided phantom (0.413) than the COM cost function (0.879). The tortuosity metric successfully ordered electronic phantoms of arteries by tortuosity. The tortuosity metric detected an increase in arterial tortuosity in hypertensive patients in 13/13 (10/13 significant at α = 0.05). The metric detected increased tortuosity in a subset of the aneurysm patients with Loeys-Dietz syndrome (LDS) in 7/7 (three significant at α = 0.001). The tortuosity measurement combination of the centerline algorithm and the distance factor metric tortuosity curve was able to detect increases in arterial tortuosity in hypertensives and LDS patients. Therefore the methods validated here can be used to study arterial tortuosity in other hypertensive population samples and in genetic subsets related to LDS

Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray-white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1-weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between-group differences and group-by-sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between-group differences and group-by-sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray-white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

Multiview microscopy of single cells through microstructure-based indirect optical manipulation

Fluorescent observation of cells generally suffers from the limited axial resolution due to the elongated point spread function of the microscope optics. Consequently, three-dimensional imaging results in axial resolution that is several times worse than the transversal. The optical solutions to this problem usually require complicated optics and extreme spatial stability. A straightforward way to eliminate anisotropic resolution is to fuse images recorded from multiple viewing directions achieved mostly by the mechanical rotation of the entire sample. In the presented approach, multiview imaging of single cells is implemented by rotating them around an axis perpendicular to the optical axis by means of holographic optical tweezers. For this, the cells are indirectly trapped and manipulated with special microtools made with two-photon polymerization. The cell is firmly attached to the microtool and is precisely manipulated with 6 degrees of freedom. The total control over the cells' position allows for its multiview fluorescence imaging from arbitrarily selected directions. The image stacks obtained this way are combined into one 3D image array with a multiview image processing pipeline resulting in isotropic optical resolution that approaches the lateral diffraction limit. The presented tool and manipulation scheme can be readily applied in various microscope platforms