Dataset Information

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

ABSTRACT: Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targeted myocardial segments. Thirdly, when there was disagreement between the human operator and machine, a second human validator reviewed and rescored the cases for adjudication and to identify the source of disagreement. The multi-stream neural networks demonstrated 89.8% agreement, 87.4% ROC-AUC on motion artefact detection with the human operator in the 2568 T1 maps. Trained with additional supervision on attention, agreements and AUC significantly improved to 91.5% and 89.1%, respectively (p < 0.001). Rescoring of disagreed cases by the second human validator revealed that human operator error was the primary cause of disagreement. Deep learning with attention supervision provides a quick and high-quality assurance of clinical images, and outperforms human operators.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC7718111 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Zhang Qiang Q Hann Evan E Werys Konrad K Wu Cody C Popescu Iulia I Lukaschuk Elena E Barutcu Ahmet A Ferreira Vanessa M VM Piechnik Stefan K SK

Artificial intelligence in medicine 20200907

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated m ...[more]

PMID: 33250143

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Project description:Stress and rest T1-mapping may assess for myocardial ischemia and extracellular volume (ECV). However, the stress T1 response is method-dependent, and underestimation may lead to misdiagnosis. Further, ECV quantification may be affected by time, as well as the number and dosage of gadolinium (Gd) contrast administered. We compared two commonly available T1-mapping approaches in their stress T1 response and ECV measurement stability. Healthy subjects (n = 10, 50% female, 35 ± 8 years) underwent regadenoson stress CMR (1.5 T) on two separate days. Prototype ShMOLLI 5(1)1(1)1 sequence was used to acquire consecutive mid-ventricular T1-maps at rest, stress and post-Gd contrast to track the T1 time evolution. For comparison, standard MOLLI sequences were used: MOLLI 5(3)3 Low (256 matrix) & High (192 matrix) Heart Rate (HR) to acquire rest and stress T1-maps, and MOLLI 4(1)3(1)2 Low & High HR for post-contrast T1-maps. Stress and rest myocardial blood flow (MBF) maps were acquired after IV Gd contrast (0.05 mmol/kg each). Stress T1 reactivity (delta T1) was defined as the relative percentage increase in native T1 between rest and stress. Myocardial T1 values for delta T1 (dT1) and ECV were calculated. Residuals from the identified time dependencies were used to assess intra-method variability. ShMOLLI achieved a greater stress T1 response compared to MOLLI Low and High HR (peak dT1 = 6.4 ± 1.7% vs. 4.8 ± 1.3% vs. 3.8 ± 1.0%, respectively; both p < 0.0001). ShMOLLI dT1 correlated strongly with stress MBF (r = 0.77, p < 0.001), compared to MOLLI Low HR (r = 0.65, p < 0.01) and MOLLI High HR (r = 0.43, p = 0.07). ShMOLLI ECV was more stable to gadolinium dose with less time drift (0.006-0.04% per minute) than MOLLI variants. Overall, ShMOLLI demonstrated less intra-individual variability than MOLLI variants for stress T1 and ECV quantification. Power calculations indicate up to a fourfold (stress T1) and 7.5-fold (ECV) advantage in sample-size reduction using ShMOLLI. Our results indicate that ShMOLLI correlates strongly with increased MBF during regadenoson stress and achieves a significantly higher stress T1 response, greater effect size, and greater ECV measurement stability compared with the MOLLI variants tested.

Project description:BACKGROUND:Cardiovascular magnetic resonance (CMR) T1? mapping can be used to detect ischemic or non-ischemic cardiomyopathy without the need of exogenous contrast agents. Current 2D myocardial T1? mapping requires multiple breath-holds and provides limited coverage. Respiratory gating by diaphragmatic navigation has recently been exploited to enable free-breathing 3D T1? mapping, which, however, has low acquisition efficiency and may result in unpredictable and long scan times. This study aims to develop a fast respiratory motion-compensated 3D whole-heart myocardial T1? mapping technique with high spatial resolution and predictable scan time. METHODS:The proposed electrocardiogram (ECG)-triggered T1? mapping sequence is performed under free-breathing using an undersampled variable-density 3D Cartesian sampling with spiral-like order. Preparation pulses with different T1? spin-lock times are employed to acquire multiple T1?-weighted images. A saturation prepulse is played at the start of each heartbeat to reset the magnetization before T1? preparation. Image navigators are employed to enable beat-to-beat 2D translational respiratory motion correction of the heart for each T1?-weighted dataset, after which, 3D translational registration is performed to align all T1?-weighted volumes. Undersampled reconstruction is performed using a multi-contrast 3D patch-based low-rank algorithm. The accuracy of the proposed technique was tested in phantoms and in vivo in 11 healthy subjects in comparison with 2D T1? mapping. The feasibility of the proposed technique was further investigated in 3 patients with suspected cardiovascular disease. Breath-hold late-gadolinium enhanced (LGE) images were acquired in patients as reference for scar detection. RESULTS:Phantoms results revealed that the proposed technique provided accurate T1? values over a wide range of simulated heart rates in comparison to a 2D T1? mapping reference. Homogeneous 3D T1? maps were obtained for healthy subjects, with septal T1? of 58.0 ± 4.1?ms which was comparable to 2D breath-hold measurements (57.6 ± 4.7?ms, P?=?0.83). Myocardial scar was detected in 1 of the 3 patients, and increased T1? values (87.4 ± 5.7?ms) were observed in the infarcted region. CONCLUSIONS:An accelerated free-breathing 3D whole-heart T1? mapping technique was developed with high respiratory scan efficiency and near-isotropic spatial resolution (1.7 × 1.7 × 2?mm3) in a clinically feasible scan time of ~?6 mins. Preliminary patient results suggest that the proposed technique may find applications in non-contrast myocardial tissue characterization.

Project description:The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm.

Dataset Information

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

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Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

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