Dataset Information

A computer-simulation study on the effects of MRI voxel dimensions on carotid plaque lipid-core and fibrous cap segmentation and stress modeling.

ABSTRACT:

Background

The benefits of a decreased slice thickness and/or in-plane voxel size in carotid MRI for atherosclerotic plaque component quantification accuracy and biomechanical peak cap stress analysis have not yet been investigated in detail because of practical limitations.

Methods

In order to provide a methodology that allows such an investigation in detail, numerical simulations of a T1-weighted, contrast-enhanced, 2D MRI sequence were employed. Both the slice thickness (2 mm, 1 mm, and 0.5 mm) and the in plane acquired voxel size (0.62x0.62 mm2 and 0.31x0.31 mm2) were varied. This virtual MRI approach was applied to 8 histology-based 3D patient carotid atherosclerotic plaque models.

Results

A decreased slice thickness did not result in major improvements in lumen, vessel wall, and lipid-rich necrotic core size measurements. At 0.62x0.62 mm2 in-plane, only a 0.5 mm slice thickness resulted in improved minimum fibrous cap thickness measurements (a 2-3 fold reduction in measurement error) and only marginally improved peak cap stress computations. Acquiring voxels of 0.31x0.31 mm2 in-plane, however, led to either similar or significantly larger improvements in plaque component quantification and computed peak cap stress.

Conclusions

This study provides evidence that for currently-used 2D carotid MRI protocols, a decreased slice thickness might not be more beneficial for plaque measurement accuracy than a decreased in-plane voxel size. The MRI simulations performed indicate that not a reduced slice thickness (i.e. more isotropic imaging), but the acquisition of anisotropic voxels with a relatively smaller in-plane voxel size could improve carotid plaque quantification and computed peak cap stress accuracy.

SUBMITTER: Nieuwstadt HA

PROVIDER: S-EPMC4391711 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A computer-simulation study on the effects of MRI voxel dimensions on carotid plaque lipid-core and fibrous cap segmentation and stress modeling.

Nieuwstadt Harm A HA Kassar Zaid A M ZA van der Lugt Aad A Breeuwer Marcel M van der Steen Anton F W AF Wentzel Jolanda J JJ Gijsen Frank J H FJ

PloS one 20150409 4

<h4>Background</h4>The benefits of a decreased slice thickness and/or in-plane voxel size in carotid MRI for atherosclerotic plaque component quantification accuracy and biomechanical peak cap stress analysis have not yet been investigated in detail because of practical limitations.<h4>Methods</h4>In order to provide a methodology that allows such an investigation in detail, numerical simulations of a T1-weighted, contrast-enhanced, 2D MRI sequence were employed. Both the slice thickness (2 mm, ...[more]

PMID: 25856094

Similar Datasets

Project description:ObjectivesThe aim of this study was to investigate whether and what carotid plaque characteristics predict systemic cardiovascular outcomes in patients with clinically established atherosclerotic disease.BackgroundAdvancements in atherosclerosis imaging have allowed assessment of various plaque characteristics, some of which are more directly linked to the pathogenesis of acute cardiovascular events compared to plaque burden.MethodsAs part of the event-driven clinical trial AIM-HIGH (Atherothrombosis Intervention in Metabolic Syndrome with Low HDL/High Triglycerides: Impact on Global Health Outcomes), subjects with clinically established atherosclerotic disease underwent multicontrast carotid magnetic resonance imaging (MRI) to detect plaque tissue composition and high-risk features. Prospective associations between MRI measurements and the AIM-HIGH primary endpoint (fatal and nonfatal myocardial infarction, ischemic stroke, hospitalization for acute coronary syndrome, and symptom-driven revascularization) were analyzed using Cox proportional hazards survival models.ResultsOf the 232 subjects recruited, 214 (92.2%) with diagnostic image quality constituted the study population (82% male, mean age 61 ± 9 years, 94% statin use). During median follow-up of 35.1 months, 18 subjects (8.4%) reached the AIM-HIGH endpoint. High lipid content (hazard ratio [HR] per 1 SD increase in percent lipid core volume: 1.57; p = 0.002) and thin/ruptured fibrous cap (HR: 4.31; p = 0.003) in carotid plaques were strongly associated with the AIM-HIGH endpoint. Intraplaque hemorrhage had a low prevalence (8%) and was marginally associated with the AIM-HIGH endpoint (HR: 3.00; p = 0.053). High calcification content (HR per 1 SD increase in percent calcification volume: 0.66; p = 0.20), plaque burden metrics, and clinical risk factors were not significantly associated with the AIM-HIGH endpoint. The associations between carotid plaque characteristics and the AIM-HIGH endpoint changed little after adjusting for clinical risk factors, plaque burden, or AIM-HIGH randomized treatment assignment.ConclusionsAmong patients with clinically established atherosclerotic disease, carotid plaque lipid content and fibrous cap status were strongly associated with systemic cardiovascular outcomes. Markers of carotid plaque vulnerability may serve as novel surrogate markers for systemic atherothrombotic risk.

Project description:Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Project description:IntroductionA reliable and automated method to segment and classify carotid artery atherosclerotic plaque components is needed to efficiently analyze multi-weighted magnetic resonance (MR) images to allow their integration into patient risk assessment for ischemic stroke. Certain plaque components such as lipid-rich necrotic core (LRNC) with hemorrhage suggest a greater likelihood of plaque rupture and stroke event. Assessment for presence and extent of LRNC could assist in directing treatment with impact upon patient outcomes.MethodsTo address the need to accurately determine the presence and extent of plaque components on carotid plaque MRI, we proposed a two-staged deep-learning-based approach that consists of a convolutional neural network (CNN), followed by a Bayesian neural network (BNN). The rationale for the two-stage network approach is to account for the class imbalance of vessel wall and background by providing an attention mask to the BNN. A unique feature of the network training was to use ground truth defined by both high-resolution ex vivo MRI data and histopathology. More specifically, standard resolution 1.5 T in vivo MR image sets with corresponding high resolution 3.0 T ex vivo MR image sets and histopathology image sets were used to define ground-truth segmentations. Of these, data from 7 patients was used for training and from the remaining two was used for testing the proposed method. Next, to evaluate the generalizability of the method, we tested the method with an additional standard resolution 3.0 T in vivo data set of 23 patients obtained from a different scanner.ResultsOur results show that the proposed method yielded accurate segmentation of carotid atherosclerotic plaque and outperforms not only manual segmentation by trained readers, who did not have access to the ex vivo or histopathology data, but also three state-of-the-art deep-learning-based segmentation methods. Further, the proposed approach outperformed a strategy where the ground truth was generated without access to the high resolution ex vivo MRI and histopathology. The accurate performance of this method was also observed in the additional 23-patient dataset from a different scanner.ConclusionIn conclusion, the proposed method provides a mechanism to perform accurate segmentation of the carotid atherosclerotic plaque in multi-weighted MRI. Further, our study shows the advantages of using high-resolution imaging and histology to define ground truth for training deep-learning-based segmentation methods.

Dataset Information

A computer-simulation study on the effects of MRI voxel dimensions on carotid plaque lipid-core and fibrous cap segmentation and stress modeling.

Background

Methods

Results

Conclusions

Publications

A computer-simulation study on the effects of MRI voxel dimensions on carotid plaque lipid-core and fibrous cap segmentation and stress modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets