The articles in this special section focus on computational magnetic resonance imaging (MRI) using compressed sensing applications. Presents recent developments in computational MRI. These developments are pushing the frontier of computational imaging beyond CS. Similar to CS, most of these algorithms rely on image representation in one form or another. However, the common recent thread is the departure from handcrafted image representations to learning-based image representations. These learned representations are seamlessly combined with clever measurement strategies to significantly advance the state of the art in a number of areas. Several exciting applications including significantly improved spatial and temporal resolution, a considerable reduction in scan time, measurement of biophysical parameters directly from highly undersampled data, and direct measurement of very high-dimensional data are reviewed in this special issue of SPM. This issue describes key ideas underlying the computational approaches used in MRI. These approaches range from CS algorithms that rely on fixed transforms or dictionaries, to adaptive or shallow-learning algorithms that adapt the image representation to the data to recent deep-learning methods that learn a highly nonlinear representation from exemplar data. The articles provide insight into the capabilities of the current algorithms, their limitations, and their utility in challenging MRI problems.

中文翻译：

---

计算 MRI：压缩感知及超越 [来自客座编辑]

此特殊部分中的文章重点介绍使用压缩传感应用程序的计算磁共振成像 (MRI)。介绍计算 MRI 的最新进展。这些发展正在推动计算成像的前沿超越 CS。与 CS 类似，这些算法中的大多数都依赖于一种或另一种形式的图像表示。然而，最近的共同主题是从手工制作的图像表示转向基于学习的图像表示。这些学习到的表示与巧妙的测量策略无缝结合，以显着提高许多领域的技术水平。几个令人兴奋的应用，包括显着提高空间和时间分辨率，显着减少扫描时间，直接从高度欠采样的数据测量生物物理参数，本期 SPM 特刊回顾了高维数据的直接测量和直接测量。本期描述了 MRI 中使用的计算方法背后的关键思想。这些方法的范围从依赖固定变换或字典的 CS 算法，到使图像表示适应数据的自适应或浅层学习算法，再到最近从示例数据中学习高度非线性表示的深度学习方法。这些文章深入介绍了当前算法的功能、它们的局限性以及它们在解决 MRI 问题中的实用性。这些方法的范围从依赖固定变换或字典的 CS 算法，到使图像表示适应数据的自适应或浅层学习算法，再到最近从示例数据中学习高度非线性表示的深度学习方法。这些文章深入介绍了当前算法的功能、它们的局限性以及它们在解决 MRI 问题中的实用性。这些方法的范围从依赖固定变换或字典的 CS 算法，到使图像表示适应数据的自适应或浅层学习算法，再到最近从示例数据中学习高度非线性表示的深度学习方法。这些文章深入介绍了当前算法的功能、它们的局限性以及它们在解决 MRI 问题中的实用性。