Faculty of Electrical & Computer Engineering, Technion - IIT（以色列理工学院电气与计算机工程学院）；Faculty of Biomedical Engineering, Technion - IIT（以色列理工学院生物医学工程学院）；The May-Blum-Dahl MRI Research Center, Faculty of Biomedical Engineering, Technion - IIT（以色列理工学院生物医学工程学院梅-布-达尔磁共振成像研究中心）；Bnai Zion Medical Center（巴奈锡安医疗中心）
Eyal Hanania, Adi Zehavi-Lenz, Ilya Volovik, Daphna Link-Sourani, Israel Cohen, Moti Freiman
备注：
Accepted and published in Medical Image Analysis
发表信息：
Medical Image Analysis, Volume 102, May 2025, 103495 Medical Image Analysis, Volume 102, May 2025, 103495
Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSS-T1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. In a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality ($R^2$: 0.975 vs. 0.941, 0.946 for STONE; 0.987 vs. 0.982, 0.965 for MOLLI free-breathing; 0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE; 0.963 vs. 0.919, 0.851 for MOLLI free-breathing; 0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE; 4.1 vs. 3.5, 3.28 for MOLLI free-breathing; 3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at https://github.com/TechnionComputationalMRILab/MBSS-T1.
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；MRC London Institute of Medical Sciences（英国医学研究理事会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床教学中心）
本文提出 DeepMesh 学习框架，利用多视图二维 CMR 图像重建个体心脏三维网格并估计逐顶点运动场，在 UK Biobank 上优于现有图像与网格类方法。
Intensity-based 3D motion correction for cardiac MR images
Munich Center for Machine Learning, Technical University Munich（慕尼黑工业大学慕尼黑机器学习中心）；School of Computation, Information and Technology, Technical University Munich（慕尼黑工业大学计算、信息与技术学院）；Klinikum Rechts der Isar, Technical University Munich（慕尼黑工业大学右岸医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
King’s College London（伦敦国王学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；Imperial College London（帝国理工学院）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Leeds（利兹大学）
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject …
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is b…
Department of Computing, Imperial College London, London, UK（英国伦敦帝国理工学院计算机系）；Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK（英国牛津大学拉德克利夫医学院心血管医学系）；NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, UK（英国伦敦玛丽女王大学巴茨国家健康研究所）
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac funct…
Rutgers University（罗格斯大学）；SenseBrain Research（SenseBrain研究院）；University of Arkansas for Medical Sciences（阿肯色大学医学院）；Lund University（隆德大学）；New York University（纽约大学）
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volu…
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA；Department of Electrical；Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA；Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA；Institute for Informatics, Data Science \& Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的研究中心，技术大学慕尼黑（TUM）及慕尼黑技术大学医院）；School of Medicine, Klinikum rechts der Isar, TUM, Germany（医学院，右岸克里克医院，TUM，德国）；Department of Computing, Imperial College London, UK（计算学院，伦敦帝国学院，英国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
University of Iowa（爱荷华大学）；Philips HealthCare（飞利浦医疗）；University of Wisconsin（威斯康星大学）；GE HealthCare（通用电气医疗）
Free-breathing cardiac MRI schemes are emerging as competitive alternatives to breath-held cine MRI protocols, enabling applicability to pediatric and other population groups that cannot hold their breath. Because the data from the slices are acquired sequentially, the cardiac/respiratory motion patterns may be different for each slice; current free-breathing approaches perform independent recovery of each slice. In addition to not being able to exploit the inter-slice redundancies, manual intervention or sophisticated post-processing methods are needed to align the images post-recovery for qu…
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
University of Tuebingen（蒂宾根大学）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Stanford University（斯坦福大学）；Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Department of Bioengineering, Imperial College London（帝国理工学院生物工程系）
