With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
Philips Research（飞利浦研究院）；Philips Healthcare（飞利浦医疗）；Leiden University Medical Center（莱顿大学医学中心）
自适应智能旨在通过额外使用领域知识来增强机器学习技术。在这项工作中，我们提出了应用自适应智能，以加快MR采集。从欠采样的k空间数据出发，采用一种受压缩感知理论启发的基于迭代学习的重建方案来重建图像。我们采用深度神经网络来改进和纠正给定训练数据的先验重建假设。该网络在Facebook AI Research和NYU Langone Health组织的2019年fastMRI挑战赛的膝关节MRI数据集上进行了训练和测试。所有提交的挑战最初根据与已知地面实况的相似性进行排名，之后对前4名提交进行放射学评价。我们的方法由fastMRI组织者在一个独立的挑战数据集上进行了评估。它在8倍加速多线圈、4倍多线圈和4倍单线圈跟踪中分别排名第1、第1和第3。这证明了该方法的优越性能和广泛适用性。
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin（查德拉家族电子与计算机工程系，德克萨斯大学奥斯汀分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA（影像物理，MD安德森癌症中心，休斯顿，德克萨斯州，美国）；Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学，加州大学伯克利分校）；Department of Diagnostic Medicine, Dell Medical School, Austin, Texas, USA（诊断医学系，德勒医学学院，奥斯汀，德克萨斯州，美国）
DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems
Department of Computer Science, Universidad Industrial de Santander（圣安德烈斯工业大学计算机科学系）；Department of Electrical Engineering, Universidad Industrial de Santander（圣安德烈斯工业大学电气工程系）
