融合灵敏度与线性可预测性的新正则化方法，提升加速MRI图像质量。
SPIRiT Regularization: Parallel MRI with a Combination of Sensitivity Encoding and Linear Predictability
- 提出SPIRiT正则化，结合灵敏度编码与线性可预测性
- 在脑、膝、踝数据上实现更高质量重建，加速比提升明显
- 适合需要高速高质MRI成像的研究者与临床医生
加速磁共振成像（MRI）可通过减少采样次数实现更快扫描并获得高质量图像。当前两种主流加速方法为并行成像与压缩感知。并行成像包括基于线性可预测性的方法（假设傅里叶样本呈线性关系）和灵敏度编码（利用灵敏度图的先验知识）。本文提出一种新型正则化项——SPIRiT正则化，将压缩感知与上述两类并行成像方法相结合。实验在脑、膝、踝部数据上验证，结果表明重建图像质量显著改善。该方法在保持图像细节的同时有效提升了成像速度。
原文摘要 · Abstract (English)
Accelerated Magnetic Resonance Imaging (MRI) permits high quality images from fewer samples that can be collected with a faster scan. Two established methods for accelerating MRI include parallel imaging and compressed sensing. Two types of parallel imaging include linear predictability, which assumes that the Fourier samples are linearly related, and sensitivity encoding, which incorporates a priori knowledge of the sensitivity maps. In this work, we combine compressed sensing with both types of parallel imaging using a novel regularization term: SPIRiT regularization. When combined, the reconstructed images are improved. We demonstrate results on data of a brain, a knee, and an ankle.
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