# Accelerated Computing in Magnetic Resonance Imaging -- Real-Time Imaging Using Non-Linear Inverse Reconstruction

## TL;DR

This paper presents optimized GPU-based methods for real-time dynamic MRI reconstruction using the non-linear inversion algorithm, enabling high frame rate imaging with low latency on multi-GPU systems.

## Contribution

It introduces novel parallel decomposition strategies and an autotuning approach to optimize iterative MRI reconstruction algorithms on multi-GPU hardware.

## Key findings

- Achieved real-time MRI reconstruction at 30 fps.
- Demonstrated effective multi-GPU parallelization of NLINV.
- Provided a general framework for accelerating iterative algorithms in MRI.

## Abstract

Purpose: To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report about our experience with a highly accelerated implementation of the non-linear inversion algorithm (NLINV) for dynamic MRI with high frame rates. Methods: The NLINV algorithm is optimized and ported to run on an 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. Results: 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. Conclusion: Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs it is possible to achieve online image reconstruction with high frame rates.