PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI

Tomer Weiss10000-0002-5039-0858, Ortal Senouf1, Sanketh Vedula1, Oleg Michailovich2, Michael Zibulevsky1, Alex Bronstein1
1: Computer Science, Technion, Haifa, Israel, 2: Electrical and Computer Engineering, Waterloo University, Canada
Publication date: 2021/04/16
PDF · Code · arXiv


Magnetic Resonance Imaging (MRI) has long been considered to be among “the gold standards” of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of concurrent learning-based design of data acquisition and image reconstruction schemes. In this work, we propose a novel approach to the learning of optimal schemes for conjoint acquisition and reconstruction of MRI scans, with the optimization carried out simultaneously with respect to the time-efficiency of data acquisition and the quality of resulting reconstructions. To be of a practical value, the schemes are encoded in the form of general k-space trajectories, whose associated magnetic gradients are constrained to obey a set of predefined hardware requirements (as defined in terms of, e.g., peak currents and maximum slew rates of magnetic gradients). With this proviso in mind, we propose a novel algorithm for the end-to-end training of a combined acquisition-reconstruction pipeline using a deep neural network with differentiable forward- and back-propagation operators. We also demonstrate the effectiveness of the proposed solution in application to both image reconstruction and image segmentation, reporting substantial improvements in terms of acceleration factors as well as the quality of these end tasks.



Bibtex @article{melba:2021:006:weiss, title = "PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI", author = "Weiss, Tomer and Senouf, Ortal and Vedula, Sanketh and Michailovich, Oleg and Zibulevsky, Michael and Bronstein, Alex", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "April 2021 issue", year = "2021", pages = "1--23", issn = "2766-905X", doi = "https://doi.org/10.59275/j.melba.2021-1a1f", url = "https://melba-journal.org/2021:006" }
RISTY - JOUR AU - Weiss, Tomer AU - Senouf, Ortal AU - Vedula, Sanketh AU - Michailovich, Oleg AU - Zibulevsky, Michael AU - Bronstein, Alex PY - 2021 TI - PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI T2 - Machine Learning for Biomedical Imaging VL - 1 IS - April 2021 issue SP - 1 EP - 23 SN - 2766-905X DO - https://doi.org/10.59275/j.melba.2021-1a1f UR - https://melba-journal.org/2021:006 ER -

2021:006 cover