430-03-006 ISMRM Abstract

Evaluating Jacobian Approximation for Efficient Joint Optimization of Sampling and Reconstruction for Accelerated MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - AI methods

430-03-006 · Advanced Image Reconstruction · Tuesday, 12 May, 1:40 PM–3:30 PM · Meeting Room 2.60

Keywords: Deep learning reconstruction Sampling pattern optimization Automatic Differentiation NUFFT

Accepted

Idil A Turasi ^1,2, Aiqi Sun², Chenwei Tang^2,3,4, Cagan Alkan⁵, Mahmut Yurt^3,5, John Pauly⁵, Shreyas Vasanawala²

¹Department of Computing + Mathematical Sciences, California Institute of Technology, Pasadena, United States of America

²Department of Radiology, Stanford University, Stanford, United States of America

³Cardiovascular Institute, Stanford University, Stanford, United States of America

⁴Division of Radiology, Veterans Administration Health Care System, Palo Alto, United States of America

⁵Department of Electrical Engineering, Stanford University, Stanford, United States of America

Presenting Author: Idil A Turasi

Synopsis

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References

1. Weiss T, Senouf O, Vedula S, Michailovich O, Zibulevsky M, and Bronstein A. PILOT: Physics-informed learned optimized trajectories for accelerated MRI. arXiv:1909.05773, 2019.

2. Aggarwal HK and Jacob M. J-MoDL: Joint model-based deep learning for optimized sampling and reconstruction. IEEE J. Sel. Top. Signal Process. 2020;14(6):1151–1162. DOI: 10.1109/jstsp.2020.3004094 [doi]

3. Wang G, Luo T, Nielsen JF, Noll DC, and Fessler JA. B-spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI. IEEE Trans Med Imaging. 2022;41(9):2318–2330. DOI: 10.1109/TMI.2022.3161875 [doi]

4. Alkan C, Mardani M, Liao C, Li Z, Vasanawala SS, and Pauly JM. AutoSamp: Autoencoding k-space sampling via variational information maximization for 3D MRI. IEEE Trans Med Imaging. 2024;44(1):270-283. DOI: 10.1109/TMI.2024.3443292 [doi]

5. Wang G, Fessler JA. Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT). IEEE Trans Comput Imaging. 2023;9:43-54. DOI: 10.1109/tci.2023.3240081 [doi]

6. Tang C, Rivera-Rivera LA, Eisenmenger LB, and Johnson KM. Machine learned wave encoded neurovascular 4D flow. Proceedings of 2023 ISMRM & ISMRT Annual Meeting, Toronto, Canada. 2023;p0705.

7. Tang C. Deep learning aided acceleration MR acquisition reconstruction and evaluation. Ph.D. dissertation, University of Wisconsin-Madison. 2024.

8. Tang C, Rivera-Rivera LA, Eisenmenger LB, and Johnson KM. Fast 3D Neuro T2-FLAIR with learned sampling and fully 3D model based deep learning. Proceedings of 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore. 2024;p0277.

9. Ong F, Amin S, Vasanawala SS, and Lustig M. An open archive for sharing MRI raw data. Proceedings of 2018 ISMRM & ESMRMB Joint Annual Meeting, Paris, France. 2018; p3425.

10. Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014;71(3):990-1001. DOI: 10.1002/mrm.24751 [doi]

11. Ong F and Lustig M. SigPy: A Python package for high performance iterative reconstruction. Proceedings of 2020 ISMRM & ISMRT Annual Meeting & Exhibition, Montreal, QC, Canada. 2020;p4819.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/430-03-006