Accelerated Computing in Magnetic Resonance Imaging: Real-Time Imaging Using Nonlinear Inverse Reconstruction

Computational and Mathematical Methods in Medicine
Sebastian SchaetzMartin Uecker

Abstract

To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV) algorithm for dynamic MRI with high frame rates. The NLINV algorithm is optimized and ported to run on a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV alg...Continue Reading

References

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Citations

Jul 22, 2018·Dento Maxillo Facial Radiology·Sebastian KrohnJens Frahm
Apr 6, 2019·Fluids and Barriers of the CNS·Gökmen AktasSteffi Dreha-Kulaczewski
May 26, 2021·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Olivier JaubertVivek Muthurangu
Dec 18, 2020··Jian ZhuangYiyu Shi

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Software Mentioned

Supermicro
FFTW
NLINV
CUDA
cuFFT
clFFT

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