Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. Once learned, the density can be used for a variety of tasks, including data imputation. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution MRI data considered in this work. To overcome this problem, we introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multislice free breathing and ungated cardiac MRI data from highly undersampled measurements. Most of the current self-gating and manifold cardiac MRI approaches consider the independent recovery of images from each slice; these methods are not capable of exploiting the inter-slice redundancies in the datasets and require sophisticated post-processing or manual approaches to align the images from different slices. By contrast, the proposed scheme is able to align the multislice data and exploit the redundancies. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.
College of Health Science and Environmental Engineering, Shenzhen Technology University（深圳科技大学健康科学与环境工程学院）；University Medical Center Göttingen（哥廷根大学医学中心）；Guangdong-Hongkong-Macau CNS Regeneration Institute, Key Laboratory of CNS Regeneration (Jinan University)-Ministry of Education, Jinan University（广东-港澳-澳门神经科学再生研究所，神经科学再生重点实验室（暨南大学）-教育部，暨南大学）；Department of Radiology, The First Hospital of Tsinghua University（清华大学第一医院放射科）；Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University（清华大学生物医学成像研究中心，生物医学工程系，医学院）；Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University（教育部生物医学工程重点实验室，生物医学工程与仪器科学学院，浙江大学）；Department of Endocrinology and Metabolism, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院邵逸夫医院内分泌科）；Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen (Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine（分子成像与医学智能实验室，放射科，深圳龙岗中心医院（深圳临床医学院，广州中医药大学；龙岗临床学院，汕头大学医学院））；Longgang Clinical Institute of Shantou University Medical College)
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；National University of Singapore（新加坡国立大学）；Shanghai East Hospital, Tongji University School of Medicine（同济大学医学院附属东方医院）；McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth Houston)（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物信息学院）；School of Transportation, Southeast University（东南大学交通学院）；Ningbo Hangzhou Bay Hospital（宁波杭州湾医院）；Changhai Hospital, Naval Medical University（海军军医大学长海医院）
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；Guangxi Medical University（广西医科大学）；The Hong Kong University of Science and Technology（香港科技大学）
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）
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院阿蒂努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院放射科）；Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系医学图像计算中心）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Department of Neurology and Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院神经内科与基因组医学中心）；Swiss Federal Institute of Technology (ETH)（苏黎世联邦理工学院）；Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院急诊医学科）；Department of Neurology, Yale New Haven Hospital（耶鲁纽黑文医院神经内科）；Department of Physics, Harvard University（哈佛大学物理系）
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
Department of Bioengineering and I-X, Imperial College London（生物工程系和I-X，帝国理工学院伦敦分校）；Cardiovascular Research Centre, Royal Brompton Hospital（心脏血管研究中心，皇家布隆特医院）；National Heart and Lung Institute, Imperial College London（国家心脏和肺研究所，帝国理工学院伦敦分校）；School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Shanghai Pudong Hospital and Human Phenome Institute, Fudan University（上海浦东医院和人类表型研究所，复旦大学）；International Human Phenome Institute (Shanghai), Shanghai, China（国际人类表型研究所（上海），上海，中国）
GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences
School of Biomedical Engineering and Technology Innovation, Fudan University（复旦大学生物医学工程与技术创新学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发智能科学技术研究院）；Intelligent Diagnosis and Treatment Laboratory for Brain Diseases, Joint Laboratory of Neurosurgery Department of Huashan Hospital and School of Information Science and Technology, Fudan University（脑病智能诊断与治疗实验室，华山医院神经外科部门联合实验室，复旦大学信息科学学院）
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
University College London（伦敦大学学院）；University of Ibadan（伊巴丹大学）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）；UCL Great Ormond Street Institute of Child Health（伦敦大学学院大奥蒙德街儿童健康研究所）；King’s College London（伦敦国王学院）；Microsoft Research Cambridge（微软剑桥研究院）；Department of Radiology, College of Medicine, University of Ibadan, Nigeria（伊巴丹大学医学院放射科）；Department of Paediatrics, College of Medicine, University of Ibadan, Nigeria（伊巴丹大学医学院儿科）；Centre for Medical Image Computing and Department of Computer Science, University College London, UK（伦敦大学学院医学影像计算中心和计算机科学系）；Department of Biomedical Engineering, King’s College London, UK（伦敦国王学院生物医学工程系）；Machine Intelligence and Perception Group, Microsoft Research Cambridge, UK（微软剑桥研究院机器智能与感知组）
DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation
University of Science, Ho Chi Minh City（胡志明市自然科学大学）；John von Neumann Institute, Ho Chi Minh City（胡志明市约翰·冯·诺依曼研究所）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）；University of Arkansas, Fayetteville（阿肯色大学费耶特维尔分校）
A Trust-Guided Approach to MR Image Reconstruction with Side Information
NYU Center for Data Science（纽约大学数据科学中心）；Bernard and Irene Schwartz Center for Biomedical Imaging（伯纳德和艾琳·施瓦茨生物医学成像中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences（库朗数学科学研究所）
Cross-conditioned Diffusion Model for Medical Image to Image Translation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction
Dept. of Electrical and Electronics Engineering, and National Magnetic Resonance Research Center (UMRAM), Bilkent University（电气与电子工程系，国家磁共振研究中心（UMRAM），比尔肯大学）
