A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction
Huang Z, Bae J, Johnson PM, Sood T, Heacock L, Fogarty J, Moy L, Kim SG, Knoll F (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12964 LNCS
Pages Range: 45-53
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030885519
DOI: 10.1007/978-3-030-88552-6_5
Abstract
Dynamic contrast enhancement (DCE) MRI has been increasingly utilized in clinical practice. While machine learning (ML) applications are gaining momentum in MRI reconstruction, the dynamic nature of image acquisition for DCE-MRI limits access to a simultaneously high spatial and temporal resolution ground truth image for supervised ML applications. In this study, we introduced a pipeline to simulate the ground truth DCE-MRI k-space data from real breast perfusion images. Based on physical model and the clinical images, we estimate the perfusion parameters. Treating those as ground truth, we simulated the signal. Using our simulated images, we trained ML reconstruction models. We demonstrate the utility of our simulation pipeline using two ML models and one conventional reconstruction method. Our results suggest that, even though the image quality of the ML reconstructions seem to be very close to the simulated ground truth, the temporal pattern and its kinetic parameters may not be close to the ground truth data.
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United States (USA) (US)How to cite
APA:
Huang, Z., Bae, J., Johnson, P.M., Sood, T., Heacock, L., Fogarty, J.,... Knoll, F. (2021). A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction. In Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 45-53). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Huang, Zhengnan, et al. "A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction." Proceedings of the 4th International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 held in Conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo, Springer Science and Business Media Deutschland GmbH, 2021. 45-53.
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