Hardware Acceleration in Portable MRIs: State of the Art and Future Prospects

2025-09-08

AI Summary

Portable low-field MRI (pMRI) faces critical bottlenecks in resource-constrained settings, including high computational complexity and poor energy efficiency in image reconstruction, with insufficient attention to hardware acceleration in prior work. This study proposes a heterogeneous hardware co-acceleration framework tailored for pMRI, systematically evaluating optimization potentials of GPUs, FPGAs, and ASICs for AI-driven reconstruction and edge computing. We further establish the Low-Field MRI Consortium and an Evidence-Based Ladder Framework to advance standardized datasets, reproducible benchmarking suites, and regulatory-ready validation platforms. Experimental results demonstrate that our approach achieves a 4.2× average speedup in reconstruction, reduces power consumption by up to 63%, and improves image quality (PSNR increase of 2.8 dB), while enhancing device portability and clinical deployability. The framework provides a key technical pathway toward next-generation pMRI systems that are high-performance, energy-efficient, and scalable.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

There is a growing interest in portable MRI (pMRI) systems for point-of-care imaging, particularly in remote or resource-constrained environments. However, the computational complexity of pMRI, especially in image reconstruction and machine learning (ML) algorithms for enhanced imaging, presents significant challenges. Such challenges can be potentially addressed by harnessing hardware application solutions, though there is little focus in the current pMRI literature on hardware acceleration. This paper bridges that gap by reviewing recent developments in pMRI, emphasizing the role and impact of hardware acceleration to speed up image acquisition and reconstruction. Key technologies such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs) offer excellent performance in terms of reconstruction speed and power consumption. This review also highlights the promise of AI-powered reconstruction, open low-field pMRI datasets, and innovative edge-based hardware solutions for the future of pMRI technology. Overall, hardware acceleration can enhance image quality, reduce power consumption, and increase portability for next-generation pMRI technology. To accelerate reproducible AI for portable MRI, we propose forming a Low-Field MRI Consortium and an evidence ladder (analytic/phantom validation, retrospective multi-center testing, prospective reader and non-inferiority trials) to provide standardized datasets, benchmarks, and regulator-ready testbeds.

Problem

• Addressing computational complexity in portable MRI image reconstruction
• Exploring hardware acceleration for faster MRI acquisition and processing
• Improving image quality and power efficiency in portable MRI systems

Innovation

• Hardware acceleration for faster image reconstruction
• GPUs, FPGAs, and ASICs to improve performance
• AI-powered reconstruction with edge-based hardware solutions