Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study

TL;DR

This paper proposes an FPGA-optimized physics-driven AI method for MRI reconstruction that reduces computational demands and data transfer, enabling real-time, high-resolution imaging on edge devices.

Contribution

It introduces a novel FPGA-friendly PD-AI MRI reconstruction approach using 8-bit quantization and FFT bypassing, enhancing efficiency without sacrificing quality.

Findings

Achieves comparable image quality to traditional methods

Reduces computational complexity significantly

Outperforms standard clinical MRI reconstruction methods

Abstract

Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of these scans generates massive data volumes, leading to challenges in transmission, storage, and real-time processing. This is particularly pronounced in functional MRI, where hundreds of volumetric acquisitions further exacerbate these demands. Edge computing with FPGAs presents a promising solution for enabling PD-AI reconstruction near the MRI sensors, reducing data transfer and storage bottlenecks. However, this requires optimization of PD-AI models for hardware efficiency through quantization and bypassing traditional FFT-based approaches, which can be a limitation due to their computational demands. In this work, we propose a novel PD-AI computational MRI approach optimized for FPGA-based edge computing devices, leveraging 8-bit complex data quantization and eliminating redundant FFT/IFFT operations. Our results show that this strategy improves computational efficiency while maintaining reconstruction quality comparable to conventional PD-AI methods, and outperforms standard clinical methods. Our approach presents an opportunity for high-resolution MRI reconstruction on resource-constrained devices, highlighting its potential for real-world deployment.