Journal of magnetic resonance imaging : JMRI · 2020
Towards Automated Quantification of Vessel Wall Composition Using MRI.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ziegler Magnus, Good Elin, Engvall Jan, Warntjes Marcel, de Muinck Ebo, Dyverfeldt Petter
Аннотация
BACKGROUND: MRI can be used to generate fat fraction (FF) and R2* data, which have been previously shown to characterize the plaque compositional features lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in the carotid arteries (CAs). Previously, these data were extracted from CA plaques using time-consuming manual analyses.
PURPOSE: To design and demonstrate a method for segmenting the CA and extracting data describing the composition of the vessel wall.
STUDY TYPE: Prospective.
SUBJECTS: 31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS).
FIELD STRENGTH/SEQUENCES: T1 -weighted (T1 W) quadruple inversion recovery, contrast-enhanced MR angiography (CE-MRA), and 4-point Dixon data were acquired at 3T.
ASSESSMENT: The vessel lumen of the CA was automatically segmented using support vector machines (SVM) with CE-MRA data, and the vessel wall region was subsequently delineated. Automatically generated segmentations were quantitatively measured and three observers visually compared the segmentations to manual segmentations performed on T1 w images. Dixon data were used to generate FF and R2* maps. Both manually and automatically generated segmentations of the CA and vessel wall were used to extract compositional data.
STATISTICAL TESTS: Two-tailed t-tests were used to examine differences between results generated using manual and automated analyses, and among different configurations of the automated method. Interobserver agreement was assessed with Fleiss' kappa.
RESULTS: Automated segmentation of the CA using SVM had a Dice score of 0.89 ± 0.02 and true-positive ratio 0.93 ± 0.03 when compared against ground truth, and median qualitative score of 4/5 when assessed visually by multiple observers. Vessel wall regions of 0.5 and 1 mm yielded compositional information similar to that gained from manual analyses. Using the 0.5 mm vessel wall region, the mean difference was 0.1 ± 2.5% considering FF and 1.1 ± 5.7[1/s] for R2*.
LEVEL OF EVIDENCE: 1.
TECHNICAL EFFICACY STAGE: 1. J. Magn. Reson. Imaging 2020;52:710-719.
PURPOSE: To design and demonstrate a method for segmenting the CA and extracting data describing the composition of the vessel wall.
STUDY TYPE: Prospective.
SUBJECTS: 31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS).
FIELD STRENGTH/SEQUENCES: T1 -weighted (T1 W) quadruple inversion recovery, contrast-enhanced MR angiography (CE-MRA), and 4-point Dixon data were acquired at 3T.
ASSESSMENT: The vessel lumen of the CA was automatically segmented using support vector machines (SVM) with CE-MRA data, and the vessel wall region was subsequently delineated. Automatically generated segmentations were quantitatively measured and three observers visually compared the segmentations to manual segmentations performed on T1 w images. Dixon data were used to generate FF and R2* maps. Both manually and automatically generated segmentations of the CA and vessel wall were used to extract compositional data.
STATISTICAL TESTS: Two-tailed t-tests were used to examine differences between results generated using manual and automated analyses, and among different configurations of the automated method. Interobserver agreement was assessed with Fleiss' kappa.
RESULTS: Automated segmentation of the CA using SVM had a Dice score of 0.89 ± 0.02 and true-positive ratio 0.93 ± 0.03 when compared against ground truth, and median qualitative score of 4/5 when assessed visually by multiple observers. Vessel wall regions of 0.5 and 1 mm yielded compositional information similar to that gained from manual analyses. Using the 0.5 mm vessel wall region, the mean difference was 0.1 ± 2.5% considering FF and 1.1 ± 5.7[1/s] for R2*.
LEVEL OF EVIDENCE: 1.
TECHNICAL EFFICACY STAGE: 1. J. Magn. Reson. Imaging 2020;52:710-719.