Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis. Traditional approaches address metal artifact reduction (MAR) and accelerated MRI acquisition as separate problems. We propose MASC, a unified reinforcement learning framework that jointly optimizes metal-aware k-space sampling and artifact correction for accelerated MRI. To enable supervised training, we construct a paired MRI dataset using physics-based simulation, generating k-space data and reconstructions for phantoms with and without metal implants. This paired dataset provides simulated 3D MRI scans with and without metal implants, where each metal-corrupted sample has an exactly matched clean reference, enabling direct supervision for both artifact reduction and acquisition policy learning. We formulate active MRI acquisition as a sequential decision-making problem, where an artifact-aware Proximal Policy Optimization (PPO) agent learns to select k-space phase-encoding lines under a limited acquisition budget. The agent operates on undersampled reconstructions processed through a U-Net-based MAR network, learning patterns that maximize reconstruction quality. We further propose an end-to-end training scheme where the acquisition policy learns to select k-space lines that best support artifact removal while the MAR network simultaneously adapts to the resulting undersampling patterns. Experiments demonstrate that MASC's learned policies outperform conventional sampling strategies, and end-to-end training improves performance compared to using a frozen pre-trained MAR network, validating the benefit of joint optimization. Cross-dataset experiments on FastMRI with physics-based artifact simulation further confirm generalization to realistic clinical MRI data. The code and models of MASC have been made publicly available: https://github.com/hrlblab/masc
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）
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
Radiation Oncology, University of California, San Francisco, California（加州大学旧金山分校放射肿瘤学系）；Radiology and Biomedical Imaging, University of California, San Francisco, California（加州大学旧金山分校放射学与生物医学成像系）；Siemens Medical Solutions USA, Inc., Cleveland, Ohio（西门子医疗解决方案美国公司，克利夫兰，俄亥俄）；Radiology at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California（洛杉矶儿童医院放射学，美国南加州大学凯克医学院，洛杉矶，加利福尼亚）
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）
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）
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）
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）
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。
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）
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；School of Computation, Information & Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；GE HealthCare（GE医疗）；Biomedical Imaging Group Rotterdam, Erasmus MC（伊拉斯姆斯大学医学中心鹿特丹生物医学成像组）；Visionlab, University of Antwerp（安特卫普大学视觉实验室）；School of Medicine & Health, Technical University of Munich（慕尼黑工业大学医学与健康学院）；Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右伊萨尔医院神经放射科）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
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）
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）
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）
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
Fudan University（复旦大学）；The Hong Kong Polytechnic University（香港理工大学）；Imperial College London（帝国理工学院）；Xiamen University（厦门大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
