Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water along different spatial directions. Unfortunately, collecting such data comes at the price of reduced spatial resolution and substantially elevated acquisition times, which limits the clinical applicability of dMRI. This problem has been thus far addressed using two principal strategies. Most of the efforts have been extended towards improving the quality of signal estimation for any, yet fixed sampling scheme (defined through the choice of diffusion-encoding gradients). On the other hand, optimization over the sampling scheme has also proven to be effective. Inspired by the previous results, the present work consolidates the above strategies into a unified estimation framework, in which the optimization is carried out with respect to both estimation model and sampling design {\it concurrently}. The proposed solution offers substantial improvements in the quality of signal estimation as well as the accuracy of ensuing analysis by means of fiber tractography. While proving the optimality of the learned estimation models would probably need more extensive evaluation, we nevertheless claim that the learned sampling schemes can be of immediate use, offering a way to improve the dMRI analysis without the necessity of deploying the neural network used for their estimation. We present a comprehensive comparative analysis based on the Human Connectome Project data. Code and learned sampling designs aviliable at https://github.com/tomer196/Learned_dMRI.
The MRI Scanner as a Diagnostic: Image-less Active Sampling
School of Engineering, The University of Edinburgh（爱丁堡大学工程学院）；Krannert Cardiovascular Research Center, Indiana University School of Medicine（印第安纳大学医学院克兰纳特心血管研究中心）；The Alan Turing Institute（阿兰·图灵研究所）
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
Graz University of Technology（格拉茨技术大学）；Silicon Austria Labs（奥地利硅谷实验室）；University of Bonn（波恩大学）；Johannes Kepler University（约翰·开普勒大学）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国理工学院）；NYU School of Medicine（纽约大学医学院）
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI
University of Cambridge（剑桥大学）；ShanghaiTech University（上海科技大学）；Zhejiang University of Technology（浙江工业大学）；Northwest University（西北大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）
End-to-End Sequential Sampling and Reconstruction for MRI
University of Texas at Austin（德克萨斯大学奥斯汀分校）；California Institute of Technology（加州理工学院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI
Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology（电气与计算机工程系，技术学院–以色列理工学院）；May-Blum-Dahl Technion Human MRI Research Center, Technion - Israel Institute of Technology（May-Blum-Dahl技术学院人类MRI研究中心，技术学院–以色列理工学院）；Department of Biomedical Engineering, Technion – Israel Institute of Technology（生物医学工程系，技术学院–以色列理工学院）
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像中心）；Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；School of Psychological Sciences, Monash University（莫纳什大学心理科学学院）；Department of Data Science and AI, Monash University（莫纳什大学数据科学与人工智能系）
Active Sampling for Accelerated MRI with Low-Rank Tensors
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
University of Georgia（佐治亚大学）；United Imaging Intelligence（联影智能）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能有限公司）
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
Leiden University Medical Center（莱顿大学医学中心）；Cardiologs；Philips（飞利浦）；Eindhoven University of Technology（埃因霍温理工大学）；Philips Innovative Technologies（飞利浦创新技术）
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstruction
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine（首尔国立大学医学院癌症生物学跨学科项目）；Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital（首尔国立大学医学院放射科，首尔国立大学医院）；Healthcare AI Research Institute, Seoul National University Hospital（首尔国立大学医院医疗人工智能研究机构）
Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI
Istanbul Technical University（伊斯坦布尔技术大学）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；KTH Royal Institute of Technology（皇家理工学院）
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification
Medical University of Graz（格拉茨医科大学）；Monash University（莫纳什大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）
