Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data
Kerfoot E, Anton EP, Ruijsink B, Clough J, King AP, Schnabel JA (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11040 LNCS
Pages Range: 32-41
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009458
DOI: 10.1007/978-3-030-00946-5_4
Abstract
We present a methodology for reconstructing full-cycle respiratory and cardiac gated short-axis cine MR sequences from real-time MR data. For patients who are too ill or otherwise incapable of consistent breath holds, real-time MR sequences are the preferred means of acquiring cardiac images, but suffer from inferior image quality compared to standard short-axis sequences and lack cardiac ECG gating. To construct a sequence from real-time images which, as close as possible, replicates the characteristics of short-axis series, the phase of the cardiac cycle must be estimated for each image and the left ventricle identified, to be used as a landmark for slice re-alignment. Our method employs CNN-based deep learning to segment the left ventricle in the real-time sequence, which is then used to estimate the pool volume and thus the position of each image in the cardiac cycle. We then use manifold learning to account for the respiratory cycle so as to select images of the best quality at expiration. From these images a selection is made to automatically reconstruct a single cardiac cycle, and the images and segmentations are then aligned. The aligned pool segmentations can then be used to calculate volume over time and thus volume-based biomarkers.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Kerfoot, E., Anton, E.P., Ruijsink, B., Clough, J., King, A.P., & Schnabel, J.A. (2018). Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data. In David Snead, Emanuele Trucco, Danail Stoyanov, Zeike Taylor, Lena Maier-Hein, Nasir Rajpoot, Hrvoje Bogunovic, Francesco Ciompi, Mitko Veta, Mona K. Garvin, Xin Jan Chen, Anne Martel, Jeroen van der Laak, Yanwu Xu, Stephen McKenna (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 32-41). Granada, ESP: Springer Verlag.
MLA:
Kerfoot, Eric, et al. "Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data." Proceedings of the 3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. David Snead, Emanuele Trucco, Danail Stoyanov, Zeike Taylor, Lena Maier-Hein, Nasir Rajpoot, Hrvoje Bogunovic, Francesco Ciompi, Mitko Veta, Mona K. Garvin, Xin Jan Chen, Anne Martel, Jeroen van der Laak, Yanwu Xu, Stephen McKenna, Springer Verlag, 2018. 32-41.
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