MRI Visualization of Whole Brain Macro- and Microvascular Remodeling in a Rat Model of Ischemic Stroke: A Pilot Study

Using superparamagnetic iron oxide nanoparticles (SPION) as a single contrast agent, we investigated dual contrast cerebrovascular magnetic resonance imaging (MRI) for simultaneously monitoring macro- and microvasculature and their association with ischemic edema status (via apparent diffusion coefficient [ADC]) in transient middle cerebral artery occlusion (tMCAO) rat models. High-resolution T1-contrast based ultra-short echo time MR angiography (UTE-MRA) visualized size remodeling of pial arteries and veins whose mutual association with cortical ischemic edema status is rarely reported. ??R2?????R2*-MRI-derived vessel size index (VSI) and density indices (Q and MVD) mapped morphological changes of microvessels occurring in subcortical ischemic edema lesions. In cortical ischemic edema lesions, significantly dilated pial veins (p???=???0.0051) and thinned pial arteries (p???=???0.0096) of ipsilateral brains compared to those of contralateral brains were observed from UTE-MRAs. In subcortical regions, ischemic edema lesions had a significantly decreased Q and MVD values (p???<???0.001), as well as increased VSI values (p???<???0.001) than normal subcortical tissues in contralateral brains. This pilot study suggests that MR-based morphological vessel changes, including but not limited to venous blood vessels, are directly related to corresponding tissue edema status in ischemic stroke rat models

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Generative Models for Preprocessing of Hospital Brain Scans

I will in this thesis present novel computational methods for processing routine clinical brain scans. Such scans were originally acquired for qualitative assessment by trained radiologists, and present a number of difficulties for computational models, such as those within common neuroimaging analysis software. The overarching objective of this work is to enable efficient and fully automated analysis of large neuroimaging datasets, of the type currently present in many hospitals worldwide. The methods presented are based on probabilistic, generative models of the observed imaging data, and therefore rely on informative priors and realistic forward models. The first part of the thesis will present a model for image quality improvement, whose key component is a novel prior for multimodal datasets. I will demonstrate its effectiveness for super-resolving thick-sliced clinical MR scans and for denoising CT images and MR-based, multi-parametric mapping acquisitions. I will then show how the same prior can be used for within-subject, intermodal image registration, for more robustly registering large numbers of clinical scans. The second part of the thesis focusses on improved, automatic segmentation and spatial normalisation of routine clinical brain scans. I propose two extensions to a widely used segmentation technique. First, a method for this model to handle missing data, which allows me to predict entirely missing modalities from one, or a few, MR contrasts. Second, a principled way of combining the strengths of probabilistic, generative models with the unprecedented discriminative capability of deep learning. By introducing a convolutional neural network as a Markov random field prior, I can model nonlinear class interactions and learn these using backpropagation. I show that this model is robust to sequence and scanner variability. Finally, I show examples of fitting a population-level, generative model to various neuroimaging data, which can model, e.g., CT scans with haemorrhagic lesions

Magnetic Resonance Imaging of the Brain in Moving Subjects. Application of Fetal, Neonatal and Adult Brain Studies

Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). Motion makes MRI data inconsistent, causing artifacts in conventional anatomical imaging as well as invalidating diffusion tensor imaging (DTI) reconstruction. In this thesis some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high signalto- noise ratio (SNR) volume data are proposed. An approach has been developed that uses multiple overlapped dynamic single shot slice by slice imaging combined with retrospective alignment and data fusion to produce self consistent 3D volume images under subject motion. We term this method as snapshot MRI with volume reconstruction or SVR. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high resolution 3D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time with substantially improved SNR. The SVR method has been extended to correct motion artifacts from conventional multi-slice sequences when the subject drifts in position during data acquisition. Besides anatomical imaging, the SVR method has also been further extended to DTI reconstruction when there is subject motion. This has been validated successfully from an adult who was deliberately moving and then applied to inutero fetal brain imaging, which no conventional high resolution 3D method is currently available. Excellent fetal brain 3D apparent diffusion coefficient (ADC) maps in high resolution have been achieved for the first time as well as promising fractional Anisotropy (FA) maps. Pilot clinical studies using SVR reconstructed data to study fetal brain development in-utero have been performed. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as some one dimensional measurements. A Verhulst model is proposed to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data

Evaluation of Interpolation and Registration Techniques in Magnetic Resonance Image for Orthogonal Plane Super Resolution Reconstruction

Super resolution reconstruction (SRR) combines several perspectives of an image (typically low resolution) in order to reconstruct a more complete and comprehensive (higher resolution) image. The aim is to use this concept on magnetic resonance imaging (MRI) data, for which the standard is to scan in several-plane orientation in a 2D fashion. As a result, clinical MRI, functional MRI (FMRI), diffusion weighted imaging (DWI)/diffusion tensor imaging (DTI), and MR angiography (MRA) tend to have high in- plane resolution but low resolution in the slice-select direction. By combining the 2 scans of the orthogonal plane, new 3D images can be reconstructed. This thesis addresses the principal problem of image quality and considers a novel SRR technique that uses the original information from 3 MRI plane orientations in order to enhance the resolution based on prior knowledge of scanning protocol as it relates to voxel resolution. The procedure for validating the MRI data algorithm is executed using MRI dataset of a human brain. The mean squared error (MSE) and peak signal-to-noise ratio (PSNR) were computed for quantitative assessment, whereas the qualitative assessment was performed by visually comparing the SR images to the original HR