Dataset Information

Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization.

ABSTRACT:

Purpose

To develop a 3D whole-heart Bright-blood and black-blOOd phase SensiTive (BOOST) inversion recovery sequence for simultaneous noncontrast enhanced coronary lumen and thrombus/hemorrhage visualization.

Methods

The proposed sequence alternates the acquisition of two bright-blood datasets preceded by different preparatory pulses to obtain variations in blood/myocardium contrast, which then are combined in a phase-sensitive inversion recovery (PSIR)-like reconstruction to obtain a third, coregistered, black-blood dataset. The bright-blood datasets are used for both visualization of the coronary lumen and motion estimation, whereas the complementary black-blood dataset potentially allows for thrombus/hemorrhage visualization. Furthermore, integration with 2D image-based navigation enables 100% scan efficiency and predictable scan times. The proposed sequence was compared to conventional coronary MR angiography (CMRA) and PSIR sequences in a standardized phantom and in healthy subjects. Feasibility for thrombus depiction was tested ex vivo.

Results

With BOOST, the coronary lumen is visualized with significantly higher (P?ConclusionA new PSIR sequence for noncontrast enhanced simultaneous coronary lumen and thrombus/hemorrhage detection was developed. The sequence provided improved coronary lumen depiction and showed potential for thrombus visualization. Magn Reson Med 79:1460-1472, 2018. © 2017 International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

SUBMITTER: Ginami G

PROVIDER: S-EPMC5811778 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization.

Ginami Giulia G Neji Radhouene R Phinikaridou Alkystis A Whitaker John J Botnar René M RM Botnar René M RM Prieto Claudia C

Magnetic resonance in medicine 20170719 3

<h4>Purpose</h4>To develop a 3D whole-heart Bright-blood and black-blOOd phase SensiTive (BOOST) inversion recovery sequence for simultaneous noncontrast enhanced coronary lumen and thrombus/hemorrhage visualization.<h4>Methods</h4>The proposed sequence alternates the acquisition of two bright-blood datasets preceded by different preparatory pulses to obtain variations in blood/myocardium contrast, which then are combined in a phase-sensitive inversion recovery (PSIR)-like reconstruction to obta ...[more]

PMID: 28722267

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Project description:BackgroundThe free-breathing 3D whole-heart T2-prepared Bright-blood and black-blOOd phase SensiTive inversion recovery (BOOST) cardiovascular magnetic resonance (CMR) sequence was recently proposed for simultaneous bright-blood coronary CMR angiography and black-blood late gadolinium enhancement (LGE) imaging. This sequence enables simultaneous visualization of cardiac anatomy, coronary arteries and fibrosis. However, high-resolution (< 1.4 × 1.4 × 1.4 mm3) fully-sampled BOOST requires long acquisition times of ~ 20 min.MethodsIn this work, we propose to extend a highly efficient respiratory-resolved motion-corrected reconstruction framework (XD-ORCCA) to T2-prepared BOOST to enable high-resolution 3D whole-heart coronary CMR angiography and black-blood LGE in a clinically feasible scan time. Twelve healthy subjects were imaged without contrast injection (pre-contrast BOOST) and 10 patients with suspected cardiovascular disease were imaged after contrast injection (post-contrast BOOST). A quantitative analysis software was used to compare accelerated pre-contrast BOOST against the fully-sampled counterpart (vessel sharpness and length of the left and right coronary arteries). Moreover, three cardiologists performed diagnostic image quality scoring for clinical 2D LGE and both bright- and black-blood 3D BOOST imaging using a 4-point scale (1-4, non-diagnostic-fully diagnostic). A two one-sided test of equivalence (TOST) was performed to compare the pre-contrast BOOST images. Nonparametric TOST was performed to compare post-contrast BOOST image quality scores.ResultsThe proposed method produces images from 3.8 × accelerated non-contrast-enhanced BOOST acquisitions with comparable vessel length and sharpness to those obtained from fully- sampled scans in healthy subjects. Moreover, in terms of visual grading, the 3D BOOST LGE datasets (median 4) and the clinical 2D counterpart (median 3.5) were found to be statistically equivalent (p < 0.05). In addition, bright-blood BOOST images allowed for visualization of the proximal and middle left anterior descending and right coronary sections with high diagnostic quality (mean score > 3.5).ConclusionsThe proposed framework provides high-resolution 3D whole-heart BOOST images from a single free-breathing acquisition in ~ 7 min.

Project description:BackgroundTo evaluate the predictive value of radiomics features extracted from the thrombus on preoperative computed tomography images to identify successful recanalization after stent retrieve (SR) treatment in patients with acute ischemic stroke (AIS).MethodsTwo hundred fifty-six patients newly diagnosed AIS between March 2017 and September 2020 from two institutes, including the first affiliated hospital of Soochow university (institute I) and Northern Jiangsu People's hospital (institute II), were enrolled continuously and retrospectively. Patients with unsatisfactory image quality were excluded. The remaining patients of institute I were randomly divided into the training and internal validation cohorts at a ratio of 7 to 3, and patients of institute II were collected as the external validation cohort. After extraction and selection of the optimal radiomics features from training cohort, six machine learning (ML) classifiers including naïve Bayes (NB), random forest (RF), logistic regression (LR), linear support vector machine (L.SVM), radial SVM (R.SVM), and an artificial neural network (ANN) were developed to predict successful recanalization with SR treatment and compared. A combined model based on the optimal ML classifier was constructed using the optimal radiomics model and clinical-radiological risk variables. Finally, the performance of the model was selected based on the Matthews correlation coefficient (MCC) and the area under the receiver operating (AUC) and independently evaluated on the internal validation and external validation cohorts.ResultsWe automatically extracted 1,130 radiomics features from the voxel of interest (VOI) using PyRadiomics. The eight most relevant radiomics features were identified using Intraclass coefficient, single-factor logistic regression analysis, and least absolute shrinkage and selection operator algorithm in the training cohort. Among the six ML classifiers, the ANN classifier using thrombus radiomics features achieved the best prediction of early recanalization under SR with MCCs of 0.913, 0.693 and 0.505 in training, internal and external validation cohorts, respectively. Moreover, receiver operating characteristic curves showed that the combined model [AUC =0.860, 95% confidence interval (CI): 0.731-0.936; AUC =0.849, 95% CI: 0.759-0.831] was not significantly better than radiomics model based on the ANN classifier alone (AUC =0.873, 95% CI: 0.803-0.891; AUC =0.805, 95% CI: 0.864-0.971) (P>0.05, Delong test) in internal and external validation cohorts.ConclusionsA radiomics model based on the ANN classifier has the ability to predict successful recanalization after SR in patients with AIS, thus allowing a potentially better selection of mechanical thrombectomy treatment.

Dataset Information

Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization.

Purpose

Methods

Results

Publications

Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization.

Similar Datasets

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