Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance
Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院）；Siemens Healthineers Ltd.（西门子医疗有限公司）；RadioDynamic Healthcare（RadioDynamic医疗）；Tsinghua Cross-Strait Research Institute, Tsinghua University（清华大学清华两岸研究院）
背景：深度学习超分辨率（SR）可能提高肌肉骨骼磁共振图像的质量，但其在7T膝部成像中的诊断价值尚不明确。目的：比较SR、低分辨率（LR）和高分辨率（HR）7T膝部MRI的图像质量和诊断性能。方法：在前瞻性研究中，42名受试者接受了7T膝部MRI，使用LR（0.8*0.8*2 mm³）和HR（0.4*0.4*2 mm³）序列。SR图像通过混合注意力变换器模型从LR数据生成。三位放射科医生评估了图像质量、解剖可见性和膝关节病理的检测。关节镜在10例中作为参考标准。结果：SR图像的总体质量高于LR（中位数评分5 vs 4，P<.001）且噪声低于HR（5 vs 4，P<.001）。软骨、半月板和韧带的可见性在SR和HR中优于LR（P<.001）。关节内病理的检测率和诊断性能（灵敏度、特异度、AUC）在不同图像类型中相似（P>=.095）。结论：深度学习超分辨率提高了7T膝部MRI的主观图像质量，但与标准LR成像相比，未增加诊断准确性。
英文摘要：
Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image quality and diagnostic performance of SR, low-resolution (LR), and high-resolution (HR) 7T knee MRI. Methods: In this prospective study, 42 participants underwent 7T knee MRI with LR (0.8*0.8*2 mm3) and HR (0.4*0.4*2 mm3) sequences. SR images were generated from LR data using a Hybrid Attention Transformer model. Three radiologists assessed image quality, anatomic conspicuity, and detection of knee pathologies. Arthroscopy served as reference in 10 cases. Results: SR images showed higher overall quality than LR (median score 5 vs 4, P<.001) and lower noise than HR (5 vs 4, P<.001). Visibility of cartilage, menisci, and ligaments was superior in SR and HR compared to LR (P<.001). Detection rates and diagnostic performance (sensitivity, specificity, AUC) for intra-articular pathology were similar across image types (P>=.095). Conclusions: Deep learning superresolution improved subjective image quality in 7T knee MRI but did not increase diagnostic accuracy compared with standard LR imaging.
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）
Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating Radiographic Feature Information
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University Sorbonne Paris Nord（巴黎北大索邦大学）；German Cancer Research Center（德国癌症研究中心）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；Henan University of Chinese Medicine（河南中医药大学）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI
Department of Radiology & Biomedical Imaging, Yale School of Medicine（放射学与生物医学成像系，耶鲁医学院）；Department of Biomedical Engineering, Rensselaer Polytechnic Institute（生物医学工程系，伦塞拉尔理工学院）
Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University Sorbonne Paris Nord（巴黎北索邦大学）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；Henan University of Chinese Medicine（河南中医药大学）；German Cancer Research Center（德国癌症研究中心）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西诺拉·A.马蒂诺斯生物医学影像中心）；Geneva University Hospital（日内瓦大学医院）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
University Hospital Aachen（亚琛大学医院）；Technical University Dresden（德累斯顿工业大学）；University Hospital Dresden（德累斯顿大学医院）；University Hospital Heidelberg（海德堡大学医院）
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Chung-Ang University Hospital（中央大学医院）；Chung-Ang University College of Medicine（中央大学医学院）
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine（贝纳德与伊蕾娜·施瓦茨生物医学成像中心，放射学系，纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（数学科学学院，纽约大学）；Center for Advanced Imaging Innovation and Research (CAI 2 R), Department of Radiology, NYU Grossman School of Medicine（先进成像创新与研究中心（CAI 2 R），放射学系，纽约大学格罗斯曼医学院）
