Dataset Information

High spatial resolution free-breathing 3D late gadolinium enhancement cardiac magnetic resonance imaging in ischaemic and non-ischaemic cardiomyopathy: quantitative assessment of scar mass and image quality.

ABSTRACT: PURPOSE:To compare breath-hold (BH) with navigated free-breathing (FB) 3D late gadolinium enhancement cardiac MRI (LGE-CMR) MATERIALS AND METHODS: Fifty-one patients were retrospectively included (34 ischaemic cardiomyopathy, 14 non-ischaemic cardiomyopathy, three discarded). BH and FB 3D phase sensitive inversion recovery sequences were performed at 3T. FB datasets were reformatted into normal resolution (FB-NR, 1.46x1.46x10mm) and high resolution (FB-HR, isotropic 0.91-mm voxels). Scar mass, scar edge sharpness (SES), SNR and CNR were compared using paired-samples t-test, Pearson correlation and Bland-Altman analysis. RESULTS:Scar mass was similar in BH and FB-NR (mean ± SD: 15.5±18.0 g vs. 15.5±16.9 g, p=0.997), with good correlation (r=0.953), and no bias (mean difference ± SD: 0.00±5.47 g). FB-NR significantly overestimated scar mass compared with FB-HR (15.5±16.9 g vs 14.4±15.6 g; p=0.007). FB-NR and FB-HR correlated well (r=0.988), but Bland-Altman demonstrated systematic bias (1.15±2.84 g). SES was similar in BH and FB-NR (p=0.947), but significantly higher in FB-HR than FB-NR (p<0.01). SNR and CNR were lower in BH than FB-NR (p<0.01), and lower in FB-HR than FB-NR (p<0.01). CONCLUSION:Navigated free-breathing 3D LGE-CMR allows reliable scar mass quantification comparable to breath-hold. During free-breathing, spatial resolution can be increased resulting in improved sharpness and reduced scar mass. KEY POINTS:• Navigated free-breathing 3D late gadolinium enhancement is reliable for myocardial scar quantification. • High-resolution 3D late gadolinium enhancement increases scar sharpness • Ischaemic and non-ischaemic cardiomyopathy patients can be imaged using free-breathing LGE CMR.

SUBMITTER: Bizino MB

PROVIDER: S-EPMC6096581 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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High spatial resolution free-breathing 3D late gadolinium enhancement cardiac magnetic resonance imaging in ischaemic and non-ischaemic cardiomyopathy: quantitative assessment of scar mass and image quality.

Bizino Maurice B MB Tao Qian Q Amersfoort Jacob J Siebelink Hans-Marc J HJ van den Bogaard Pieter J PJ van der Geest Rob J RJ Lamb Hildo J HJ

European radiology 20180406 9

<h4>Purpose</h4>To compare breath-hold (BH) with navigated free-breathing (FB) 3D late gadolinium enhancement cardiac MRI (LGE-CMR) MATERIALS AND METHODS: Fifty-one patients were retrospectively included (34 ischaemic cardiomyopathy, 14 non-ischaemic cardiomyopathy, three discarded). BH and FB 3D phase sensitive inversion recovery sequences were performed at 3T. FB datasets were reformatted into normal resolution (FB-NR, 1.46x1.46x10mm) and high resolution (FB-HR, isotropic 0.91-mm voxels). Scar ...[more]

PMID: 29626239

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Project description:BackgroundAutomatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.MethodsThirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested.ResultsThe prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions.ConclusionA mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.

Project description:Purpose High-resolution free-breathing late gadolinium enhancement (HR-LGE) was shown valuable for the diagnosis of acute coronary syndromes with non-obstructed coronary arteries. The method may be useful to detect COVID-related myocardial injuries but is hampered by prolonged acquisition times. We aimed to introduce an accelerated HR-LGE technique for the diagnosis of COVID-related myocardial injuries. Method An undersampled navigator-gated HR-LGE (acquired resolution of 1.25mm3) sequence combined with advanced patch-based low-rank reconstruction was developed and validated in a phantom and in 23 patients with structural heart disease (test cohort; 15 men; 55±16 years). Twenty patients with laboratory-confirmed COVID-19 infection associated with troponin rise (COVID cohort; 15 men; 46±24 years) prospectively underwent cardiovascular magnetic resonance (CMR) with the proposed sequence in our center. Image sharpness, quality, signal intensity differences and diagnostic value of free-breathing HR-LGE were compared against conventional breath-held low-resolution LGE (LR-LGE, voxel size 1.8x1.4x6mm). Results Structures sharpness in the phantom showed no differences with the fully sampled image up to an undersampling factor of x3.8 (P>0.5). In patients (N=43), this acceleration allowed for acquisition times of 7min21s±1min12s at 1.25mm3 resolution. Compared with LR-LGE, HR-LGE showed higher image quality (P=0.03) and comparable signal intensity differences (P>0.5). In patients with structural heart disease, all LGE-positive segments on LR-LGE were also detected on HR-LGE (80/391) with 21 additional enhanced segments visible only on HR-LGE (101/391, P<0.001). In 4 patients with COVID-19 history, HR-LGE was definitely positive while LR-LGE was either definitely negative (1 microinfarction and 1 myocarditis) or inconclusive (2 myocarditis). Conclusions Undersampled free-breathing isotropic HR-LGE can detect additional areas of late enhancement as compared to conventional breath-held LR-LGE. In patients with history of COVID-19 infection associated with troponin rise, the method allows for detailed characterization of myocardial injuries in acceptable scan times and without the need for repeated breath holds.

Project description:BackgroundQuantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders.PurposeTo develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification.Study typeRetrospective.PopulationA total of 191 hypertrophic cardiomyopathy patients: 1) 162 patients from two sites randomly split into training (50%; 81 patients), validation (25%, 40 patients), and testing (25%; 41 patients); and 2) an external testing dataset (29 patients) from a third site.Field strength/sequence1.5T, inversion-recovery segmented gradient-echo LGE and balanced steady-state free-precession cine sequences ASSESSMENT: Two convolutional neural networks (CNN) were trained for myocardium and scar segmentation, one with and one without LGE-Cine fusion. For CNN with fusion, the input was two aligned LGE and cine images at matched cardiac phase and anatomical location. For CNN without fusion, only LGE images were used as input. Manual segmentation of the datasets was used as reference standard.Statistical testsManual and CNN-based quantifications of LGE scar burden and of myocardial volume were assessed using Pearson linear correlation coefficients (r) and Bland-Altman analysis.ResultsBoth CNN models showed strong agreement with manual quantification of LGE scar burden and myocardium volume. CNN with LGE-Cine fusion was more robust than CNN without LGE-Cine fusion, allowing for successful segmentation of significantly more slices (603 [95%] vs. 562 (89%) of 635 slices; P < 0.001). Also, CNN with LGE-Cine fusion showed better agreement with manual quantification of LGE scar burden than CNN without LGE-Cine fusion (%ScarLGE-cine = 0.82 × %Scarmanual , r = 0.84 vs. %ScarLGE = 0.47 × %Scarmanual , r = 0.81) and myocardium volume (VolumeLGE-cine = 1.03 × Volumemanual , r = 0.96 vs. VolumeLGE = 0.91 × Volumemanual , r = 0.91).Data conclusionCNN based LGE-Cine fusion can improve the robustness and accuracy of automated scar quantification.Level of evidence3 TECHNICAL EFFICACY: 1.

Project description:BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans.MethodsA CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement, and myocardial lesion burden quantification. Image quality was compared using a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC.ResultsA total of 1348 hypertrophic cardiomyopathy patients provided 4093 triplets of matched T1 maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development and 345 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets; P<0.001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions (r=0.77-0.79; ICC=0.77-0.87; P<0.001) and intermediate-intensity lesions (r=0.70-0.76; ICC=0.82-0.85; P<0.001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second.ConclusionsVNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Project description:ObjectiveMotion-corrected averaging with a single-shot technique was introduced for faster acquisition of late-gadolinium-enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging while free-breathing. We aimed to evaluate the image quality (IQ) of free-breathing motion-corrected single-shot LGE (moco-ss-LGE) in patients with hypertrophic cardiomyopathy (HCM).Materials and methodsBetween April and December 2019, 30 patients (23 men; median age, 48.5; interquartile range [IQR], 36.5-61.3) with HCM were prospectively enrolled. Breath-held single-shot LGE (bh-ss-LGE) and free-breathing moco-ss-LGE images were acquired in random order on a 3T MR system. Semi-quantitative IQ scores, contrast-to-noise ratios (CNRs), and quantitative size of myocardial scar were assessed on pairs of bh-ss-LGE and moco-ss-LGE. The mean ± standard deviation of the parameters was obtained. The results were compared using the Wilcoxon signed-rank test.ResultsThe moco-ss-LGE images had better IQ scores than the bh-ss-LGE images (4.55 ± 0.55 vs. 3.68 ± 0.45, p < 0.001). The CNR of the scar to the remote myocardium (34.46 ± 11.85 vs. 26.13 ± 10.04, p < 0.001), scar to left ventricle (LV) cavity (13.09 ± 7.95 vs. 9.84 ± 6.65, p = 0.030), and LV cavity to remote myocardium (33.12 ± 15.53 vs. 22.69 ± 11.27, p < 0.001) were consistently greater for moco-ss-LGE images than for bh-ss-LGE images. Measurements of scar size did not differ significantly between LGE pairs using the following three different quantification methods: 1) full width at half-maximum method; 23.84 ± 12.88% vs. 24.05 ± 12.81% (p = 0.820), 2) 6-standard deviation method, 15.14 ± 10.78% vs. 15.99 ± 10.99% (p = 0.186), and 3) 3-standard deviation method; 36.51 ± 17.60% vs. 37.50 ± 17.90% (p = 0.785).ConclusionMotion-corrected averaging may allow for superior IQ and CNRs with free-breathing in single-shot LGE imaging, with a herald of free-breathing moco-ss-LGE as the scar imaging technique of choice for clinical practice.

Dataset Information

High spatial resolution free-breathing 3D late gadolinium enhancement cardiac magnetic resonance imaging in ischaemic and non-ischaemic cardiomyopathy: quantitative assessment of scar mass and image quality.

Publications

High spatial resolution free-breathing 3D late gadolinium enhancement cardiac magnetic resonance imaging in ischaemic and non-ischaemic cardiomyopathy: quantitative assessment of scar mass and image quality.

Similar Datasets

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