Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction

Uten Yarach; Itthi Chatnuntawech; Congyu Liao; Surat Teerapittayanon; Siddharth Srinivasan Iyer; Tae Hyung Kim; Justin Haldar; Jaejin Cho; Berkin Bilgic; Yuxin Hu; Brian Hargreaves; Kawin Setsompop

doi:10.1016/j.mri.2024.110277

Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction

Magn Reson Imaging. 2025 Jan:115:110277. doi: 10.1016/j.mri.2024.110277. Epub 2024 Nov 19.

Authors

Affiliations

¹ Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
² National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand.
³ Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁴ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Department of Computer Engineering, Hongik University, Seoul, South Korea.
⁶ Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
⁷ Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
⁸ Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
⁹ Department of Bioengineering, Stanford University, Stanford, California, USA.
¹⁰ Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, California, USA.
¹¹ Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA. Electronic address: [email protected].

PMID: 39566835
DOI: 10.1016/j.mri.2024.110277

Abstract

Purpose: BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications. The proposed reconstruction includes the development of ML-based unrolled reconstruction as well as rapid ML-based B0 and eddy current estimations that are needed. The architecture of the unroll network was designed so that it can mimic S-LORAKS regularization well, with the addition of virtual coil channels.

Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also introduced into the reconstruction to effectively take advantage of the smooth phase prior and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.

Results: The introduction of the Virtual Coil concept into the unrolled network was shown to be key to achieving high-quality reconstruction for BUDA-cEPI. With the inclusion of an additional non-diffusion image (b-value = 0 s/mm²), a slight improvement was observed, with the normalized root mean square error further reduced by approximately 5 %. The reconstruction times for S-LORAKS and the proposed unrolled networks were approximately 225 and 3 s per slice, respectively.

Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ∼88× when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.

Keywords: BUDA-cEPI; Deep learning; Diffusion MRI; Eddy current; Geometric distortion; Off-resonance; Unrolled network.

MeSH terms

Algorithms
Brain* / diagnostic imaging
Deep Learning*
Diffusion Magnetic Resonance Imaging / methods
Echo-Planar Imaging / methods
Humans
Image Processing, Computer-Assisted* / methods
Neural Networks, Computer