arXiv 2509.071932025-09-08eess.IVcs.AIcs.CV

机器学习重建技术在加速脑部MRI扫描中的评估

Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans

Humber River Hospital（亨伯河医院）；Toronto Western Hospital, University of Toronto（多伦多大学多伦多西区医院）；Foqus Technologies Inc.（Foqus 技术公司）；Robarts Research Institute, Western University（西安大略大学罗巴茨研究所）

Jonathan I. Mandel, Shivaprakash Hiremath, Hedyeh Keshtgar, Timothy Scholl, Sadegh Raeisi

备注：: This work has been submitted to Radiology: Artificial Intelligence for possible publication

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AI总结：

本研究评估了深度学习重建算法DeepFoqus-Accelerate在四倍加速脑部MRI中的诊断质量，结果显示图像质量保持良好，扫描时间减少75%，支持临床工作流程改进。

摘要：

这项回顾性-前瞻性研究评估了基于深度学习的MRI重建算法在高达四倍加速的脑部MRI扫描中能否保持诊断质量，使用了公开和前瞻性临床数据。研究包括18名健康志愿者（扫描在3T下进行，时间为2024年1月至2025年3月），以及选定的包含多种病理的fastMRI公开数据集。采用相位编码欠采样的2D/3D T1、T2和FLAIR序列，使用DeepFoqus-Accelerate进行重建，并与标准治疗方案（SOC）进行比较。三位具有委员会认证的神经放射科医生和两位MRI技师独立使用5点Likert量表对来自两个数据集的36对SOC/AI重建图像进行评审，同时使用结构相似性指数（SSIM）、峰值信噪比（PSNR）和基于Haar小波的感知相似性指数（HaarPSI）对408次扫描和1224个数据集进行定量相似性评估。没有AI重建扫描得分低于3（最低可接受），95%的扫描得分≥4。平均SSIM为0.95±0.03（90%的病例>0.90），PSNR>41.0 dB，HaarPSI>0.94。评估者间一致性为轻微至中等。罕见伪影不影响诊断解释。这些发现表明，DeepFoqus-Accelerate能够实现稳健的四倍脑部MRI加速，扫描时间减少75%，同时保持诊断图像质量并支持改善工作流程效率。

英文摘要：

This retrospective-prospective study evaluated whether a deep learning-based MRI reconstruction algorithm can preserve diagnostic quality in brain MRI scans accelerated up to fourfold, using both public and prospective clinical data. The study included 18 healthy volunteers (scans acquired at 3T, January 2024-March 2025), as well as selected fastMRI public datasets with diverse pathologies. Phase-encoding-undersampled 2D/3D T1, T2, and FLAIR sequences were reconstructed with DeepFoqus-Accelerate and compared with standard-of-care (SOC). Three board-certified neuroradiologists and two MRI technologists independently reviewed 36 paired SOC/AI reconstructions from both datasets using a 5-point Likert scale, while quantitative similarity was assessed for 408 scans and 1224 datasets using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Haar wavelet-based Perceptual Similarity Index (HaarPSI). No AI-reconstructed scan scored below 3 (minimally acceptable), and 95% scored $\geq 4$. Mean SSIM was 0.95 $\pm$ 0.03 (90% cases >0.90), PSNR >41.0 dB, and HaarPSI >0.94. Inter-rater agreement was slight to moderate. Rare artifacts did not affect diagnostic interpretation. These findings demonstrate that DeepFoqus-Accelerate enables robust fourfold brain MRI acceleration with 75% reduced scan time, while preserving diagnostic image quality and supporting improved workflow efficiency.

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