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Assessment of carotid atherosclerotic disease using three-dimensional cardiovascular magnetic resonance vessel wall imaging: comparison with digital subtraction angiography.
BACKGROUND:A three-dimensional (3D) cardiovascular magnetic resonance (CMR) vessel wall imaging (VWI) technique based on 3D T1 weighted (T1w) Sampling Perfection with Application-optimized Contrast using different flip angle Evolutions (SPACE) has recently been used as a promising CMR imaging modality for evaluating extra-cranial and intra-cranial vessel walls. However, this technique is yet to be validated against the current diagnostic imaging standard. We therefore aimed to evaluate the diagnostic performance of 3D CMR VWI in characterizing carotid disease using intra-arterial digital subtraction angiography (DSA) as a reference. METHODS:Consecutive patients with at least unilateral > 50% carotid stenosis on ultrasound were scheduled to undergo interventional therapy were invited to participate. The following metrics were measured using 3D CMR VWI and DSA: lumen diameter of the common carotid artery (CCA) and segments C1-C7, stenosis diameter, reference diameter, lesion length, stenosis degree, and ulceration. We assessed the diagnostic sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve of 3D CMR VWI, and used Cohen's kappa, the intraclass correlation coefficient (ICC), and Bland-Altman analyses to assess the diagnostic agreement between 3D CMR VWI and DSA. RESULTS:The ICC (all ICCs ≥0.96) and Bland-Altman plots indicated excellent inter-reader agreement in all individual morphologic measurements by 3D CMR VWI. Excellent agreement in all individual morphologic measurements were also found between 3D CMR VWI and DSA. In addition, 3D CMR VWI had high sensitivity (98.4, 97.4, 80.0, 100.0%), specificity (100.0, 94.5, 99.1, 98.0%), and Cohen's kappa (0.99, 0.89, 0.84, 0.96) for detecting stenosis > 50%, stenosis > 70%, ulceration, and total occlusion, respectively, using DSA as the standard. The AUC of 3D CMR VWI for predicting stenosis > 50 and > 70% were 0.998 and 0.999, respectively. CONCLUSIONS:The 3D CMR VWI technique enables accurate diagnosis and luminal feature assessment of carotid artery atherosclerosis, suggesting that this imaging modality may be useful for routine imaging workups and provide comprehensive information for both the vessel wall and lumen
Contrast-free detection of myocardial fibrosis in hypertrophic cardiomyopathy patients with diffusion-weighted cardiovascular magnetic resonance.
BackgroundsPrevious studies have shown that diffusion-weighted cardiovascular magnetic resonance (DW-CMR) is highly sensitive to replacement fibrosis of chronic myocardial infarction. Despite this sensitivity to myocardial infarction, DW-CMR has not been established as a method to detect diffuse myocardial fibrosis. We propose the application of a recently developed DW-CMR technique to detect diffuse myocardial fibrosis in hypertrophic cardiomyopathy (HCM) patients and compare its performance with established CMR techniques.MethodsHCM patients (N = 23) were recruited and scanned with the following protocol: standard morphological localizers, DW-CMR, extracellular volume (ECV) CMR, and late gadolinium enhanced (LGE) imaging for reference. Apparent diffusion coefficient (ADC) and ECV maps were segmented into 6 American Heart Association (AHA) segments. Positive regions for myocardial fibrosis were defined as: ADC > 2.0 μm(2)/ms and ECV > 30%. Fibrotic and non-fibrotic mean ADC and ECV values were compared as well as ADC-derived and ECV-derived fibrosis burden. In addition, fibrosis regional detection was compared between ADC and ECV calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) using ECV as the gold-standard reference.ResultsADC (2.4 ± 0.2 μm(2)/ms) of fibrotic regions (ADC > 2.0 μm(2)/ms) was significantly (p < 0.01) higher than ADC (1.5 ± 0.2 μm(2)/ms) of non-fibrotic regions. Similarly, ECV (35 ± 4%) of fibrotic regions (ECV > 30%) was significantly (p < 0.01) higher than ECV (26 ± 2%) of non-fibrotic regions. In fibrotic regions defined by ECV, ADC (2.2 ± 0.3 μm(2)/ms) was again significantly (p < 0.05) higher than ADC (1.6 ± 0.3 μm(2)/ms) of non-fibrotic regions. In fibrotic regions defined by ADC criterion, ECV (34 ± 5%) was significantly (p < 0.01) higher than ECV (28 ± 3%) in non-fibrotic regions. ADC-derived and ECV-derived fibrosis burdens were in substantial agreement (intra-class correlation = 0.83). Regional detection between ADC and ECV of diffuse fibrosis yielded substantial agreement (κ = 0.66) with high sensitivity, specificity, PPV, NPV, and accuracy (0.80, 0.85, 0.81, 0.85, and 0.83, respectively).ConclusionDW-CMR is sensitive to diffuse myocardial fibrosis and is capable of characterizing the extent of fibrosis in HCM patients
Prospective swallowing motion self-gating: a feasibility study in carotid artery wall MRI using 3D variable-flip-angle TSE
Improved black-blood imaging using DANTE-SPACE for combined carotid and intracranial vessel wall evaluation
In vivo contrast free chronic myocardial infarction characterization using diffusion-weighted cardiovascular magnetic resonance.
BackgroundDespite the established role of late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) in characterizing chronic myocardial infarction (MI), a significant portion of chronic MI patients are contraindicative for the use of contrast agents. One promising alternative contrast free technique is diffusion weighted CMR (dwCMR), which has been shown ex vivo to be sensitive to myocardial fibrosis. We used a recently developed in vivo dwCMR in chronic MI pigs to compare apparent diffusion coefficient (ADC) maps with LGE imaging for infarct characterization.MethodsIn eleven mini pigs, chronic MI was induced by complete occlusion of the left anterior descending artery for 150 minutes. LGE, cine, and dwCMR imaging was performed 8 weeks post MI. ADC maps were derived from three orthogonal diffusion directions (b = 400 s/mm2) and one non-diffusion weighted image. Two semi-automatic infarct classification methods, threshold and full width half max (FWHM), were performed in both LGE and ADC maps. Regional wall motion (RWM) analysis was performed and compared to ADC maps to determine if any observed ADC change was significantly influenced by bulk motion.ResultsADC of chronic MI territories was significantly increased (threshold: 2.4 ± 0.3 μm2/ms, FWHM: 2.4 ± 0.2 μm2/ms) compared to remote myocardium (1.4 ± 0.3 μm2/ms). RWM was significantly reduced (threshold: 1.0 ± 0.4 mm, FWHM: 0.9 ± 0.4 mm) in infarcted regions delineated by ADC compared to remote myocardium (8.3 ± 0.1 mm). ADC-derived infarct volume and location had excellent agreement with LGE. Both LGE and ADC were in complete agreement when identifying transmural infarcts. Additionally, ADC was able to detect LGE-delineated infarcted segments with high sensitivity, specificity, PPV, and NPV. (threshold: 0.88, 0.93, 0.87, and 0.94, FWHM: 0.98, 0.97, 0.93, and 0.99, respectively).ConclusionsIn vivo diffusion weighted CMR has potential as a contrast free alternative for LGE in characterizing chronic MI
Linear Gaussian Bounding Box Representation and Ring-Shaped Rotated Convolution for Oriented Object Detection
In oriented object detection, current representations of oriented bounding
boxes (OBBs) often suffer from boundary discontinuity problem. Methods of
designing continuous regression losses do not essentially solve this problem.
Although Gaussian bounding box (GBB) representation avoids this problem,
directly regressing GBB is susceptible to numerical instability. We propose
linear GBB (LGBB), a novel OBB representation. By linearly transforming the
elements of GBB, LGBB avoids the boundary discontinuity problem and has high
numerical stability. In addition, existing convolution-based rotation-sensitive
feature extraction methods only have local receptive fields, resulting in slow
feature aggregation. We propose ring-shaped rotated convolution (RRC), which
adaptively rotates feature maps to arbitrary orientations to extract
rotation-sensitive features under a ring-shaped receptive field, rapidly
aggregating features and contextual information. Experimental results
demonstrate that LGBB and RRC achieve state-of-the-art performance.
Furthermore, integrating LGBB and RRC into various models effectively improves
detection accuracy
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