用旧MRI训练AI模型，让低成本设备快速生成高质量影像。
Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems
- 从过往高场MRI中提取个性化特征，指导低场设备成像
- 单次先验扫描即可提升图像质量，支持多厂商多参数数据
- 适用于肿瘤随访等需快速诊断的临床场景
磁共振成像（MRI）虽具优异图像质量，但受限于高昂成本，难以满足长期诊疗需求。低场MRI虽成本低廉，却因扫描时间长、信噪比（SNR）和组织对比度差而应用受限。本文提出一种新医疗范式：利用深度学习从既往标准高场MRI扫描中提取个性化特征，并用于加速与增强低成本系统上的随访成像。为克服信噪比与对比度差异，提出ViT-Fuser——一种基于特征融合的视觉变换器，可学习来自不同厂商、场强及脉冲序列的既往扫描数据（如标准DICOM光盘）。实验在四个数据集上验证，包括胶质母细胞瘤、50mT低场及6.5mT超低场数据，结果表明，仅需一次先验扫描，该方法即能显著提升低场加速扫描的图像质量，且对分布外数据具有鲁棒性。所提框架已开源，支持快速、诊断级、低成本成像，适用于广泛医疗场景。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requiring longitudinal care. Low-field MRI provides affordable imaging with low-cost devices but is hindered by long scans and degraded image quality, including low signal-to-noise ratio (SNR) and tissue contrast. We propose a novel healthcare paradigm: using deep learning to extract personalized features from past standard high-field MRI scans and harnessing them to enable accelerated, enhanced-quality follow-up scans with low-cost systems. To overcome the SNR and contrast differences, we introduce ViT-Fuser, a feature-fusion vision transformer that learns features from past scans, e.g. those stored in standard DICOM CDs. We show that \textit{a single prior scan is sufficient}, and this scan can come from various MRI vendors, field strengths, and pulse sequences. Experiments with four datasets, including glioblastoma data, low-field ($50mT$), and ultra-low-field ($6.5mT$) data, demonstrate that ViT-Fuser outperforms state-of-the-art methods, providing enhanced-quality images from accelerated low-field scans, with robustness to out-of-distribution data. Our freely available framework thus enables rapid, diagnostic-quality, low-cost imaging for wide healthcare applications.
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