While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their practical adoption as fully-sampled MRI is prohibitively time-consuming to acquire clinically. Further, non-Cartesian sampling patterns are particularly desirable as they are more amenable to acceleration and show improved motion robustness. To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains. In training, the undersampled data are split into disjoint k-space domain partitions. For the k-space self-supervision, we train a network to reconstruct the input undersampled data from both the disjoint partitions and from itself. For the image-level self-supervision, we enforce appearance consistency obtained from the original undersampled data and the two partitions. Experimental results on our simulated multi-coil non-Cartesian MRI dataset demonstrate that DDSS can generate high-quality reconstruction that approaches the accuracy of the fully supervised reconstruction, outperforming previous baseline methods. Finally, DDSS is shown to scale to highly challenging real-world clinical MRI reconstruction acquired on a portable low-field (0.064 T) MRI scanner with no data available for supervised training while demonstrating improved image quality as compared to traditional reconstruction, as determined by a radiologist study.
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
CEA/NeuroSpin（法国原子能与替代能源委员会神经自旋中心）；CEA（法国原子能与替代能源委员会）；CNRS（法国国家科学研究中心）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris Diderot（巴黎第七大学）；Sorbonne Paris Cité（索邦巴黎西岱大学）；Inria Saclay Ile-de-France（法国国家信息与自动化研究所萨克雷-法兰西岛分院）
Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction
Faculty of Mathematics, Chemnitz University of Technology（开姆尼茨工业大学数学学院）；NeuroSpin, Frédéric Joliot Institute for Life Sciences, CEA Paris-Saclay（法国原子能委员会巴黎-萨克雷大学弗雷德里克·约里奥生命科学研究所神经自旋中心）；MIND, Inria Saclay Centre（法国国家信息与自动化研究所萨克雷中心MIND团队）；Siemens Healthineers（西门子医疗）
Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration
Institute of Sensors, Signals and Systems, Heriot-Watt University Edinburgh, United Kingdom（传感器、信号与系统研究所，赫里奥特-沃斯大学爱丁堡分校，英国）；Department of Diagnostic Imaging and Interventional Radiology, Lausanne University and Hospital, Switzerland（诊断影像与介入放射学系，洛桑大学及医院，瑞士）
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center (UMRAM)（国家磁共振研究中心（UMRAM））；Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University（比尔肯特大学艾塞尔·萨本居脑研究中心神经科学项目）
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）
Rice University（莱斯大学）；Technical University of Munich（慕尼黑工业大学）
卷积神经网络（CNN）对于图像重建问题是非常有效的。通常，CNN是在大量训练图像上训练的。然而，最近，未经训练的CNN（如深度图像先验和深度解码器）在图像重建问题（如去噪和修复，\emph{without using any training data}）方面取得了优异的性能。出于这一发展的动机，我们解决了在加速MRI与未经训练的神经网络中出现的重建问题。我们提出了一种基于深度解码器变体的高度优化的未训练恢复方法，并表明它显著优于其他未训练方法，特别是基于稀疏的经典压缩感知方法和未训练神经网络的朴素应用。我们还比较了训练方法的理想设置中的性能（无论是重建准确性还是计算成本），特别是在fastMRI数据集上，其中训练和测试数据来自相同的分布。我们发现，我们的未经训练的算法实现了与基线训练的神经网络相似的性能，但最先进的训练网络优于未经训练的网络。最后，我们在训练和测试分布略有不同的非理想设置上进行比较，发现我们的未训练方法与最先进的加速MRI重建方法具有相似的性能。
Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI
East China Normal University（华东师范大学）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；West China Hospital, Sichuan University（四川大学华西医院）；Chinese Academy of Medical Sciences（中国医学科学院）；RadioDynamic Medical（镭动医疗）；Lucile Packard Children’s Hospital（露西尔·帕卡德儿童医院）
Learning Task-Specific Strategies for Accelerated MRI
California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；MGH（麻省总医院）；Harvard Medical School（哈佛医学院）
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）
Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞州总医院）
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；MS Center Amsterdam（阿姆斯特丹多发性硬化中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Leiden University（莱顿大学）；Delft University of Technology（代尔夫特理工大学）
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Xiamen University（厦门大学）；Xiamen University of Technology（厦门理工学院）；United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；Nanjing Medical University（南京医科大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
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）
Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
Humber River Hospital（亨伯河医院）；Toronto Western Hospital, University of Toronto（多伦多大学多伦多西区医院）；Foqus Technologies Inc.（Foqus 技术公司）；Robarts Research Institute, Western University（西安大略大学罗巴茨研究所）
