深度学习加速脑部MRI扫描，4倍提速仍保诊断质量。
Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
- 用深度学习算法重建欠采样MRI数据，实现四倍加速。
- 95%图像评分≥4（良好），各项指标均优于临床标准。
- 适合放射科医生和影像技术员评估快速扫描方案。
本回顾-前瞻性研究评估了基于深度学习的MRI重建算法在脑部MRI扫描中是否能保持四倍加速下的诊断质量，使用公共数据集与临床前瞻性数据。研究包含18名健康志愿者（3T扫描，2024年1月至2025年3月），以及具有多样病理特征的fastMRI公开数据集。对相位编码欠采样的2D/3D T1、T2和FLAIR序列，采用DeepFoqus-Accelerate进行重建，并与标准护理（SOC）对比。三位注册神经放射科医师及两位MRI技术人员独立评审36对配对图像（来自两组数据），使用5分李克特量表；定量相似性评估涵盖408个扫描和1224个数据集，采用结构相似性指数（SSIM）、峰值信噪比（PSNR）和哈尔小波感知相似性指数（HaarPSI）。所有AI重建图像评分不低于3（勉强可接受），95%≥4。平均SSIM为0.95±0.03（90%病例>0.90），PSNR>41.0 dB，HaarPSI>0.94。评分者间一致性为轻度至中度。罕见伪影未影响诊断判断。结果表明，DeepFoqus-Accelerate可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，提升工作流程效率。
原文摘要 · Abstract (English)
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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