Deep learning for temporal super-resolution 4D Flow MRI
Karolinska Institute（卡罗林斯卡学院）；University of Michigan（密歇根大学）；Telkom University（Telkom大学）；University of Auckland（奥克兰大学）；Siemens Healthineers AG（西门子医疗健康股份公司）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；King’s College London（伦敦国王学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；Massachusetts Institute of Technology（麻省理工学院）
Pia Callmer, Mia Bonini, Edward Ferdian, David Nordsletten, Daniel Giese, Alistair A. Young, Alexander Fyrdahl, David Marlevi
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. To overcome these issues, post-processing techniques using deep learning have shown promising results to enhance resolution post-scan using so-called super-resolution networks. However, while super-resolution has been focusing on spatial upsampling, temporal super-resolution remains largely unexplored. The aim of this study was therefore to implement and evaluate a residual network for temporal super-resolution 4D Flow MRI. To achieve this, an existing spatial network (4DFlowNet) was re-designed for temporal upsampling, adapting input dimensions, and optimizing internal layer structures. Training and testing were performed using synthetic 4D Flow MRI data originating from patient-specific in-silico models, as well as using in-vivo datasets. Overall, excellent performance was achieved with input velocities effectively denoised and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an unseen in-silico setting, outperforming deterministic alternatives (linear interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the network synthesized high-resolution temporal information from unseen low-resolution in-vivo data, with strong correlation observed at peak flow frames. As such, our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI, enabling high-frame-rate flow quantification without extending acquisition times beyond clinically acceptable limits.
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学）；Faculty of Computer Science, Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学计算机学院）；Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学数据与知识工程组）；Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学生物医学磁共振研究所）；Research Campus STIMULATE, Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学STIMULATE研究园区）；Institute for Medical Engineering, Otto von Guericke University Magdeburg（马格德堡奥托-冯-格里克大学医学工程研究所）；Center for Behavioral Brain Sciences, Magdeburg（马格德堡行为脑科学中心）；Magdeburg（马格德堡（注：单独“Magdeburg”为城市名，非机构名，根据要求未提取））
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health（柏林健康研究所）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院（PTB））；King’s College London（伦敦国王学院）
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Millenium Institute for Intelligent Healthcare Engineering（千禧智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
Technical University of Munich（慕尼黑工业大学）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University of Tübingen（蒂宾根大学）；University of Michigan（密歇根大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）；Imperial College London（帝国理工学院）
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Tri-institutional Center for Translational Research in Neuroimaging & Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
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）
