Learning-based quality control for cardiac MR images

Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, De Marvao A, O'Regan DP, Cook S, Glocker B, Matthews PM, Rueckert D (2019)

Publication Type: Journal article

Publication year: 2019

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 38

Pages Range: 1127-1138

Article Number: 8519790

Journal Issue: 5

DOI: 10.1109/TMI.2018.2878509

Abstract

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method-integrating both regression and structured classification models-to extract landmarks and probabilistic segmentation maps from both long- A nd short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Tarroni, G., Oktay, O., Bai, W., Schuh, A., Suzuki, H., Passerat-Palmbach, J.,... Rueckert, D. (2019). Learning-based quality control for cardiac MR images. IEEE Transactions on Medical Imaging, 38(5), 1127-1138. https://doi.org/10.1109/TMI.2018.2878509

MLA:

Tarroni, Giacomo, et al. "Learning-based quality control for cardiac MR images." IEEE Transactions on Medical Imaging 38.5 (2019): 1127-1138.

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