Photoacoustic tomography (PAT) offers optical contrast, whereas magnetic resonance imaging (MRI) excels in imaging soft tissue and organ anatomy. The fusion of PAT with MRI holds promising application prospects due to their complementary advantages. Existing image fusion have made considerable progress in pre-registered images, yet spatial deformations are difficult to avoid in medical imaging scenarios. More importantly, current algorithms focus on visual quality and statistical metrics, thus overlooking the requirements of high-level tasks. To address these challenges, we propose an unsupervised fusion model, termed PAMRFuse+, which integrates image generation and registration. Specifically, a cross-modal style transfer network is introduced to simplify cross-modal registration to single-modal registration. Subsequently, a multi-level registration network is employed to predict displacement vector fields. Furthermore, a dual-branch feature decomposition fusion network is proposed to address the challenges of cross-modal feature modeling and decomposition by integrating modality-specific and modality-shared features. PAMRFuse+ achieves satisfactory results in registering and fusing unaligned PAT-MRI datasets. Moreover, for the first time, we evaluate the performance of medical image fusion with multi-organ instance segmentation. Extensive experimental demonstrations reveal the advantages of PAMRFuse+ in improving the performance of medical image analysis tasks.
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）
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）
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Amsterdam UMC, Department of Radiation Oncology（阿姆斯特丹大学医学中心放射肿瘤科）；Cancer Center Amsterdam, Imaging and Biomarkers（阿姆斯特丹癌症中心影像与生物标志物）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Brain Imaging（阿姆斯特丹大学医学中心、阿姆斯特丹自由大学、阿姆斯特丹神经科学、脑成像）
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；Stanford University（斯坦福大学）；Duke University（杜克大学）；New York University（纽约大学）
Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine（纽约大学格罗斯曼医学院放射学系伯纳德和艾琳·施瓦茨生物医学成像中心）；Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine（纽约大学格罗斯曼医学院放射学系先进成像创新与研究中心）
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）
A Portable Brain MRI Scanner for Underserved Settings and Point-Of-Care Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital（阿西努拉·A·马丁诺斯生物医学成像中心放射科麻省总医院）；Harvard Medical School（哈佛医学院）；Dept. of Electrical Engineering, Massachusetts Institute of Technology（麻省理工学院电气工程系）；Dept. of Physics, Harvard University（哈佛大学物理系）；Dept. of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工健康科学与技术学部）
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Department of Biomedical Engineering, Johns Hopkins（约翰斯·霍普金斯大学生物医学工程系）；Division of Cardiology, University of Utah（犹他大学心脏病学系）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）
Neurodevelopmental Age Estimation of Infants Using a 3D-Convolutional Neural Network Model based on Fusion MRI Sequences
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Children's Foundation Research Institute at LeBonheur Children's Hospital（勒邦赫儿童医院儿童基金会研究所）；LeBonheur Children's Hospital（勒邦赫儿童医院）；University of Tennessee School of Medicine（田纳西大学医学院）
A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University - Newark（罗格斯大学纽瓦克分校）
我们提出了一个全三维多路径卷积网络来预测中风病变的三维脑MRI图像。我们的多路径模型具有针对包含残余卷积块的不同模态的独立编码器、来自不同模态的加权多路径特征融合以及用于组合编码器和解码器特征的加权融合模块。与DeepMedic、3D U-Net和AnatomyNet等现有的3D CNN相比，我们的网络在220名患者的大型ATLAS基准测试中实现了最高的统计学显著交叉验证准确率（60.5%）。我们还在来自Kessler Foundation和Medical College Wisconsin的多模态图像上测试了我们的模型，并实现了65%的统计学显著交叉验证准确率，显著优于多模态3D U-Net和DeepMedic。总的来说，我们的模型提供了一个原则性的，可扩展的多路径方法，优于多通道的替代品，并在现有的基准实现高骰子精度。
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那人工智能医学实验室）；Departament de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机科学系）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（生物医学成像机器学习研究所，海德堡慕尼黑）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Department of Oncology-Pathology, Karolinska Institutet（卡罗林斯卡研究所肿瘤-病理学系）；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）
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；National Institute of Neurological Disorders and Stroke（美国国家神经疾病与卒中研究所）；National Institutes of Health（美国国立卫生研究院）；University of Bern（伯尔尼大学）；University of Pennsylvania（宾夕法尼亚大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
