自适应采样模式让心脏MRI更快更清晰
Scan-Adaptive Dynamic MRI Undersampling Using a Dictionary of Efficiently Learned Patterns
- 从大量数据中学习动态心脏MRI的最优采样模式
- 在不同加速比下实现2-3 dB的图像质量提升
- 适合临床医生快速获取高质量动态心脏影像
心脏MRI受限于扫描时间长，易导致患者不适和运动伪影。本文提出一种基于学习的框架，设计适用于动态心脏MRI的扫描或切片自适应的笛卡尔采样掩码，以加速成像并保持诊断级图像质量。利用全采样训练数据优化采样模式，在推理时通过低频k空间的最近邻搜索，从已学习的模式字典中选择最优掩码。该方法在公开和院内心脏MRI数据集上均表现出色，实现了2-3 dB的PSNR提升、降低的NMSE、更高的SSIM值，并获得更高评分的放射科医生评价。所提自适应采样框架可依据个体扫描调整采样策略，实现更快、更高质量的动态心脏MRI。
原文摘要 · Abstract (English)
Cardiac MRI is limited by long acquisition times, which can lead to patient discomfort and motion artifacts. We aim to accelerate Cartesian dynamic cardiac MRI by learning efficient, scan-adaptive undersampling patterns that preserve diagnostic image quality. We develop a learning-based framework for designing scan- or slice-adaptive Cartesian undersampling masks tailored to dynamic cardiac MRI. Undersampling patterns are optimized using fully sampled training dynamic time-series data. At inference time, a nearest-neighbor search in low-frequency $k$-space selects an optimized mask from a dictionary of learned patterns. Our learned sampling approach improves reconstruction quality across multiple acceleration factors on public and in-house cardiac MRI datasets, including PSNR gains of 2-3 dB, reduced NMSE, improved SSIM, and higher radiologist ratings. The proposed scan-adaptive sampling framework enables faster and higher-quality dynamic cardiac MRI by adapting $k$-space sampling to individual scans.
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