VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Accelerated MRI Reconstruction
Abstract
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require large amounts of fully-sampled ground truth data, which are difficult to curate and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics for improved data efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX (1) demonstrates strong improvements over supervised baselines with and without augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art data augmentation techniques that are purely image-based on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven augmentations.
Additional Information
This work was supported by R01 AR063643, R01 EB002524, R01 EB009690, R01 EB026136, K24 AR062068, and P41 EB015891 from the NIH; the Precision Health and Integrated Diagnostics Seed Grant from Stanford University; DOD – National Science and Engineering Graduate Fellowship (ARO); National Science Foundation (GRFP-DGE 1656518, CCF1763315, CCF1563078); Stanford Artificial Intelligence in Medicine and Imaging GCP grant; Stanford Human-Centered Artificial Intelligence GCP grant; GE Healthcare and Philips.Attached Files
Submitted - 2111.02549.pdf
Files
Name | Size | Download all |
---|---|---|
md5:ed3764b9750008d9af957c03884c4985
|
7.1 MB | Preview Download |
Additional details
- Eprint ID
- 111784
- Resolver ID
- CaltechAUTHORS:20211108-011022956
- R01 AR063643
- NIH
- R01 EB002524
- NIH
- R01 EB009690
- NIH
- R01 EB026136
- NIH
- K24 AR062068
- NIH
- P41 EB015891
- NIH
- Stanford University
- National Defense Science and Engineering Graduate (NDSEG) Fellowship
- DGE-1656518
- NSF Graduate Research Fellowship
- CCF-1763315
- NSF
- CCF-1563078
- NSF
- GE Healthcare
- Philips
- Created
-
2021-11-09Created from EPrint's datestamp field
- Updated
-
2023-06-02Created from EPrint's last_modified field