Fast GPU Implementation of a Scan-Specific Deep Learning Reconstruction for Accelerated Magnetic Resonance Imaging

Chi Zhang, Sebastian Weingartner, Steen Moeller, Kamil Ugurbil, Mehmet Akcakaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Parallel Imaging is a technique commonly used in most clinical Magnetic Resonance Imaging (MRI) scans to mitigate the problem of long scan-times. In parallel imaging, information from multiple receiver antennas with different spatial sensitivities is combined to allow reconstruction from undersampled image information. Robust Artificial-neural-networks for k-space Interpolation (RAKI) has been recently proposed enabling parallel imaging reconstruction in MRI using convolutional neural networks (CNN) trained solely on a calibration signal corresponding to that image. While RAKI has demonstrated improved reconstruction performance compared to established techniques, its reconstruction time is prolonged due to the repeated application of the CNN, and the necessity of a training-phase for each receiver image. In this study, we propose an optimized RAKI implementation based on GPU parallel programming. The training phase duration is substantially shortened by optimizing the number of iterations and allowing for adaptively updated learning rates without compromising visual reconstruction quality. Efficient use of GPU resources was facilitated by a parallelized implementation of the training of multiple networks using CPU multiprocessing. The proposed implementation demonstrates more than 60-fold reduction in the reconstruction speed of clinical sample data compared with conventional sequential implementation, thus, easing the integration of RAKI in clinical applications.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Electro/Information Technology, EIT 2018
PublisherIEEE Computer Society
Pages399-403
Number of pages5
ISBN (Electronic)9781538653982
DOIs
StatePublished - Oct 18 2018
Event2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States
Duration: May 3 2018May 5 2018

Publication series

NameIEEE International Conference on Electro Information Technology
Volume2018-May
ISSN (Print)2154-0357
ISSN (Electronic)2154-0373

Other

Other2018 IEEE International Conference on Electro/Information Technology, EIT 2018
CountryUnited States
CityRochester
Period5/3/185/5/18

Bibliographical note

Funding Information:
This work was partially supported by NIH R00HL111410, NIH P41EB015894, NIH U01EB025144 and NSF CAREER CCF-1651825.

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