标题： SPIRiT正则化：结合灵敏度编码和线性可预测性的并行MRI 显示英文标题

标题： SPIRiT Regularization: Parallel MRI with a Combination of Sensitivity Encoding and Linear Predictability

摘要： 加速磁共振成像（MRI）允许从更少的样本中获得高质量的图像，这些样本可以以更快的扫描速度收集。 加速MRI的两种已建立方法包括并行成像和压缩感知。 并行成像的两种类型包括线性可预测性，它假设傅里叶样本之间存在线性关系，以及敏感度编码，它结合了对敏感度图的先验知识。 在本工作中，我们使用一种新颖的正则化项：SPIRiT正则化，将压缩感知与这两种并行成像方法结合起来。 当结合使用时，重建的图像得到了改善。 我们在大脑、膝盖和脚踝的数据上展示了结果。 显示英文摘要

摘要： 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.