0273 ISMRM Abstract

eRAKI: Fast Robust Artificial neural networks for K‐space Interpolation (RAKI) with Coil Combination and Joint Reconstruction

0273 · Machine Learning for Image Reconstruction · Tuesday, 18 May, 2:00 PM–4:00 PM · Concurrent 1

Accepted

Heng Yu ¹, Zijing Dong^2,3, Yamin Arefeen², Congyu Liao⁴, Kawin Setsompop^4,5, Berkin Bilgic^3,6,7

¹Department of Automation, Tsinghua University, Beijing, China

²Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States of America

³Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America

⁴Radiological Sciences Laboratory, Stanford University, Stanford, United States of America

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

⁶Harvard Medical School, Boston, United States of America

⁷Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States of America

Presenting Author: Heng Yu

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References

1. Knoll F, Hammernik K, Zhang C, et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues[J]. IEEE Signal Processing Magazine, 2020, 37(1): 128-140.

2. Akçakaya M, Moeller S, Weingärtner S, et al. Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging[J]. Magnetic resonance in medicine, 2019, 81(1): 439-453.

3. Hosseini S A H, Zhang C, Weingärtner S, et al. Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling[J]. Plos one, 2020, 15(2): e0229418.

4. Hosseini S A H, Moeller S, Weingärtner S, et al. Accelerated coronary MRI using 3D SPIRiT-RAKI with sparsity regularization[C]//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 1692-1695.

5. Uecker M, Lai P, Murphy M J, et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA[J]. Magnetic resonance in medicine, 2014, 71(3): 990-1001.

6. van der Kouwe A J W, Benner T, Salat D H, et al. Brain morphometry with multiecho MPRAGE[J]. Neuroimage, 2008, 40(2): 559-569.

7. Mugler III J P, Brookeman J R. Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE)[J]. Magnetic resonance in medicine, 1990, 15(1): 152-157.

8. Griswold M A, Jakob P M, Heidemann R M, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA)[J]. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2002, 47(6): 1202-1210.

9. Wang F, Dong Z, Reese T G, et al. Echo planar time‐resolved imaging (EPTI)[J]. Magnetic resonance in medicine, 2019, 81(6): 3599-3615.

10. Breuer F A, Blaimer M, Heidemann R M, et al. Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi‐slice imaging[J]. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2005, 53(3): 684-691.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2021/0273