MRSaiFE: Tissue Heating Prediction for MRI: a Feasibility Study

TL;DR

This study demonstrates a proof of concept for an AI-based software, MRSaiFE, capable of real-time local SAR prediction during MRI scans, potentially enhancing safety by monitoring tissue heating at ultra-high field strengths.

Contribution

The paper introduces MRSaiFE, an AI tool for real-time SAR prediction in MRI, addressing a critical safety challenge with initial promising results.

Findings

Achieved SAR prediction with residual RMSE <11%

Attained SSIM >84% for SAR pattern accuracy

Validated feasibility at 3T and 7T MRI field strengths

Abstract

A to-date unsolved and highly limiting safety concern for Ultra High-Field (UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF) power in the body, quantified by the specific absorption rate (SAR), leading to dangerous tissue heating/damage in the form of local SAR hotspots that cannot currently be measured/monitored, thereby severely limiting the applicability of the technology for clinical practice and in regulatory approval. The goal of this study has been to show proof of concept of an artificial intelligence (AI) based exam-integrated real-time MRI safety prediction software (MRSaiFE) facilitating the safe generation of 3T and 7T images by means of accurate local SAR-monitoring at sub-W/kg levels. We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths. SAR patterns were predicted with a residual root mean squared error (RSME) of <11% along with a structural similarity (SSIM) level of >84% for both field strengths (3T and 7T).