Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively

From the macro- to the microvasculature : temporal and spatial visualization using arterial spin labeling

For many cerebrovascular diseases, visualization of blood flow through the large vasculature, as well as quantitative information on tissue perfusion, is very important. Arterial Spin labelling (ASL) magnetic resonance (MR) imaging enables the visualization of arterial flow by labelling the magnetization of arterial blood using radiofrequency pulses. The labelled arterial blood acts as an endogenous tracer and allows, which can avoid the reliance on the use of contrast agents. In this doctoral thesis, several new techniques for dynamic MR angiography and perfusion imaging were developed based on ASL techniques, which include pulsed ASL, pseudo-continuous ASL (pCASL), vessel-encoded pCASL, time-encoded pCASL as well as simultaneous multi-slice pCASL. The underlying motivation of these development is to reduce the burden on patients by employing non-invasive ASL techniques as potential alternatives to X-ray digital subtraction angiography, contrast-enhanced MR angiography and perfusion imaging. In each study, the optimum ASL techniques was carefully chosen by considering the pros and cons of the technique to achieve better clinical usability, while improving robustness against potential artifacts.LUMC / Geneeskund

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

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

Imaging cerebrovascular health using 7T MRI

Magnetic resonance imaging is a valuable clinical tool for the visualization of intracranial vasculature. Without exposing patients to ionizing radiation or intravenous contrasts, it can provide multi-modal structural information about the shape, structure, and function of the various vessels involved in stroke and dementia. However, imaging methods are limited by the achieved contrasts and resolutions, as well as the required scan times. Ultra-high field 7T MRI offers increased signal-to-noise ratio and desirable changes in relaxation parameters, therefore promising substantial improvements to existing neurovascular MRI approaches such as MR angiography (MRA) and MR vessel wall imaging (VWI). However, 7T MRI also introduces increased specific absorption rates and reduced homogeneity and extent of the transmit B1 field. Because of the latter, the first research chapter in this thesis (Chapter 3) studies the possibility to increase the extent of this 7T B1+ field into the feeding arteries in the neck using parallel transmission (pTx). The second research chapter (Chapter 4) aims to improve the accelerated acquisition of high-resolution MRA using compressed sensing reconstruction. This facilitates the visualization of the small intracranial arteries which are involved in lacunar infarcts and vascular dementia, which can be achieved within clinical scan times. The final parts of this thesis (Chapters 5-7) focus on a specific intracranial VWI sequence called DANTE-SPACE. A simulation framework for the sequence is first presented in Chapter 5. This framework includes various additional processes such as (pulsatile) tissue motion and B1+ variations to accurately represent the intra- and extra-vascular contrast mechanisms. The simulations are then used for the optimization and comparison of the T2-weighted DANTE-SPACE sequence at 3T, 7T without pTx, and 7T with pTx. The optimizations aim to maximize the contrast between both the blood within and the cerebrospinal fluid surrounding intracranial vessel walls, and the comparison between different field strengths provides a first quantitative indication of the added value of ultra- high field MRI for the DANTE-SPACE sequence

The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment

BackgroundArteriovenous malformations (AVMs) are rare vascular anomalies involving a disorganization of arteries and veins with no intervening capillaries. In the past 10 years, radiomics and machine learning (ML) models became increasingly popular for analyzing diagnostic medical images. The goal of this review was to provide a comprehensive summary of current radiomic models being employed for the diagnostic, therapeutic, prognostic, and predictive outcomes in AVM management.MethodsA systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms: (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computer-aided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies.ResultsThirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range: 10–18).ConclusionWe demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management