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

• URL: http://arxiv.org/abs/2506.03183v1
• Date: Fri, 30 May 2025 02:35:43 GMT
• Title: Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
• Authors: Yaşar Utku Alçalar, Yu Cao, Mehmet Akçakaya,
• Abstract summary: We propose a novel PD-AI computational MRI approach optimized for FPGA-based edge computing devices.<n>Our results show that this strategy improves computational efficiency while maintaining reconstruction quality comparable to conventional PD-AI methods.<n>Our approach presents an opportunity for high-resolution MRI reconstruction on resource-constrained devices, highlighting its potential for real-world deployment.
• Score: 6.098295105240952
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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