Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

Purpose: The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI.

Methods: The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data.

Determinants of carotid atherosclerotic plaque burden in a stroke-free population.

Background And Aims: In a large stroke-free population, we sought to identify cardiovascular risk factors and carotid plaque components associated with carotid plaque burden, lumen volume and stenosis.

Methods: The carotid arteries of 1562 stroke-free participants from The Rotterdam Study were imaged on a 1.5-Tesla MRI scanner.

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline.

Purpose: To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images.

Materials And Methods: 3T MRI data acquired with T -weighted gradient-echo black-blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B-spline surface to the boundaries of lumen and outer wall.

Repeatability of in vivo quantification of atherosclerotic carotid artery plaque components by supervised multispectral classification.

Objective: To evaluate the agreement and scan-rescan repeatability of automated and manual plaque segmentation for the quantification of in vivo carotid artery plaque components from multi-contrast MRI.

Materials And Methods: Twenty-three patients with 30-70% stenosis underwent two 3T MR carotid vessel wall exams within a 1 month interval. T1w, T2w, PDw and TOF images were acquired around the region of maximum vessel narrowing.

Multi-Center MRI Carotid Plaque Component Segmentation Using Feature Normalization and Transfer Learning.

Automated segmentation of plaque components in carotid artery magnetic resonance imaging (MRI) is important to enable large studies on plaque vulnerability, and for incorporating plaque composition as an imaging biomarker in clinical practice. Especially supervised classification techniques, which learn from labeled examples, have shown good performance. However, a disadvantage of supervised methods is their reduced performance on data different from the training data, for example on images acquired with different scanners.

Identification of factors associated with the development of knee osteoarthritis in a young to middle-aged cohort of patients with knee complaints.

The objective of this study was to identify risk factors for knee osteoarthritis (OA) development in a young to middle-aged population with sub-acute knee complaints. This, in order to define high risk patients who may benefit from early preventive or future disease modifying therapies. Knee OA development visible on radiographs and MR in 319 patients (mean age 41.

An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed.

Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images.

Purpose: To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery.

Materials And Methods: A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data.

Evaluation of multicontrast MRI including fat suppression and inversion recovery spin echo for identification of intra-plaque hemorrhage and lipid core in human carotid plaque using the mahalanobis distance measure.

Intra-plaque hemorrhage (IPH) and lipid core, characteristics of rupture prone carotid plaques, are often visualized in vivo with MRI using T1 weighted gradient and spin echo, respectively. Increasing magnetic field strength may help to identify IPH and lipid core better. As a proof of concept, automatic segmentation of plaque components was performed with the Mahalanobis distance (MD) measure derived from image contrast from multicontrast MR images including inversion recovery spin echo and T1 weighted gradient echo with fat suppression.

Automatic radiographic quantification of hand osteoarthritis; accuracy and sensitivity to change in joint space width in a phantom and cadaver study.

Objective: To validate a newly developed quantification method that automatically detects and quantifies the joint space width (JSW) in hand radiographs. Repeatability, accuracy and sensitivity to changes in JSW were determined. The influence of joint location and joint shape on the measurements was tested.