Cardiac function assessment aims at predicting left ventricular ejection fraction (LVEF) given an echocardiogram video, which requests models to focus on the changes in the left ventricle during the cardiac cycle. How to assess cardiac function accurately and automatically from an echocardiogram video is a valuable topic in intelligent assisted healthcare. Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. In this work, we propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. To better model the importance of motion information, we introduce a temporal channel-wise attention (TCA) module to excite those channels used to describe motion. Furthermore, we reform the TCA module with semantic perception by taking the segmentation map of the left ventricle as input to focus on the motion patterns of the left ventricle. Finally, to reduce the difficulty of direct LVEF regression, we utilize an anchor-based classification and regression method to predict LVEF. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R^2$.
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Mannheim University of Applied Science（曼海姆应用科学大学）
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium…
University of Sheffield（谢菲尔德大学）；University of Leeds（利兹大学）；University of Oxford（牛津大学）；John Radcliffe Hospital（约翰·拉德克利夫医院）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特国民保健署信托基金会）
Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-…
Guy’s and St Thomas’ Hospital NHS Foundation Trust（盖伊和圣托马斯医院NHS基金会信托）；King’s College London（伦敦国王学院）；Imperial College London（帝国理工学院）
Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good qualit…
Towards a vision foundation model for comprehensive assessment of Cardiac MRI
Siemens Healthineers（西门子医疗）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学 rechts der Isar 医院）；Siemens Healthineers AG（西门子医疗股份公司）；Imperial College London（伦敦帝国学院）
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine, Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河畔右岸医院医学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；Department of Computing, Imperial College London（帝国理工学院计算系）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
Opportunistic Cardiac Health Assessment: Estimating Phenotypes from Localizer MRI through Multi-Modal Representations
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与健康中的研究所，慕尼黑技术大学（TUM）和TUM大学医院）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital（诊断与介入神经放射科，医学院，TUM大学医院）；Department of Computing, Imperial College London（计算学院，伦敦帝国学院）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心（MCML），德国）
CineMR: Tool-Integrated Vision-Language Reasoning for Quantitative Cardiac MRI Assessment
Institute for Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）；Nemours Cardiac Center, Nemours Children’s Hospital, Florida（佛罗里达州内穆尔斯儿童医院内穆尔斯心脏中心）；Children’s Heart Center, WVU Golisano Children’s, West Virginia（西弗吉尼亚大学戈利萨诺儿童医院儿童心脏中心）；Department of Clinical Sciences, College of Medicine, University of Central Florida（中佛罗里达大学医学院临床科学系）
Royal Brompton Hospital（皇家布朗普顿医院）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of Lincoln（林肯大学）；City University London（伦敦城市大学）
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is correlated with increased morbidity and mortality. It is associated with atrial fibrosis, which may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients. Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas bas…
King's College London（伦敦国王学院）；Guy’s and St Thomas' Hospital（盖伊和圣托马斯医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Royal Brompton Hospital (Guy’s and St Thomas' NHS Foundation Trust)（皇家布朗普顿医院（盖伊和圣托马斯NHS基金会信托））
Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer descr…
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advan…
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院血管外科）；Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院放射科）；Department of Vascular Surgery, Quzhou People’s Hospital（衢州人民医院血管外科）；Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院心内科）；China Ship Scientific Research Center（中国船舶科学研究院）；Guangdong Transtek Medical Electronics Co., Ltd.（广东 Transtek 医疗电子有限公司）
Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；ETH Zurich（苏黎世联邦理工学院）；Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center（放射医学与核医学系，阿姆斯特丹大学医学中心）；Department of Radiology, Mayo Clinic, Rochester（放射医学系，梅奥诊所，罗切斯特）
King's College London（伦敦国王学院）；Guy's and St Thomas' Hospital NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托医院）；Imperial College London（帝国理工学院）
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare tw…
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images. Manual segmentation of the right ventricle requires diligence and time, while its automated segmentation is challenging due to shape variations and illdefined borders. We propose a deep learning based method for the accurate segmentation of right ventricle, which does not require post-processing and yet it achieves the state-of-the-art performance of 0.86 Dice coefficient and 6.73 mm Hausdorff di…
Fudan University（复旦大学）；Leiden University Medical Center（莱顿大学医学中心）
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of LV provides essential information on the contracting/relaxing pattern of heart, which is keenly evaluated by clinical experts in clinical practice. Inspired by the expert way of viewing Cine MRI, we propose a new CNN module that is able to incorporate the temporal information into LV segmentation from cine MRI. In the proposed CNN, the optical flow (OF) between neighboring frames is integrated and aggregated at featu…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；National Taiwan University（台湾大学）；University of Southern California（南加州大学）
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate segmentation of cardiac structures from cine MRI is a crucial step for early diagnosis and prognosis of CVD, and has been greatly improved with convolutional neural networks (CNN). There, however, are a number of limitations identified in CNN models, such as limited interpretability and high complexity, thus limiting their use in clinical practice. In this work, to address the limitations, we propose a lightweight and i…
Imperial College London（帝国理工学院）；National University of Defense Technology（国防科技大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a …
University College London（伦敦大学学院）；Centre for Medical Image Computing（医学图像计算中心）
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function. Yet, due to its complex anatomy and motion segmentation of the RV has not been as largely studied as the left ventricle. This paper presents a fully automatic method for the segmentation of the RV in cardiac magnetic resonance images (MRI). The method uses a coarse-to-fine segmentation strategy in combination with a multi-atlas propagation segmentation framework. Based on a cross correlation metric, our method selects the best atlases for propagation allowing the ref…
University of Florida（佛罗里达大学）；United Imaging Intelligence（联影智能）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no special scanning procedure. However, current deep learning-based FT methods may result in unrealistic myocardium shapes since the learning is solely guided by image intensities without considering anatomy. On the other hand, motion estimation through learning is challenging because ground-truth motion fields are almost impossible to obtain. In this study, we propose a novel Anatomy-Aware Tracker (AATracker) for cardi…
No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis al…
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we pro…
Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；Department of Electrical Engineering and Computer Science, University of California at Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学系）；Division of Cardiology, Department of Medicine, and Feinberg Cardiovascular Research Institute（医学系心脏病学科及范伯格心血管研究所）
Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the primary care and rural setting. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram (echo) interpretation. Our approach entailed: 1) preprocessing; 2) convolutional neural networks (CNN) for view identification, image segmentation, and phasing of the cardiac cycle; 3) quantification of chamber volumes and left ventricular mass; 4) parti…
RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation
Le Hong Phong High School for the Gifted（黎鸿峰天才高中）；Faculty of Information Technology, University of Science, VNU-HCM（胡志明市国家大学科学学院信息技术系）；Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
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。
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain（生物医学图像技术，电信工程学院，马德里理工大学，马德里，西班牙）；Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain（生物工程、生物材料和纳米医学网络生物医学研究中心（CIBER-BBN），卡洛斯三世健康研究所，马德里，西班牙）；APOLO Biotech, Ciudad Autónoma de Buenos Aires, Argentina（APOLO Biotech，布宜诺斯艾利斯自治市，阿根廷）；Departamento de Computación, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina（计算系，布宜诺斯艾利斯大学，布宜诺斯艾利斯自治市，阿根廷）；Instituto de Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina（计算机科学研究所（ICC），CONICET-布宜诺斯艾利斯大学，布宜诺斯艾利斯自治市，阿根廷）
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks
Shenzhen Institute 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）；DJI Technology Co., Ltd.（大疆创新科技有限公司）；Shenzhen University of Advanced Technology（深圳理工大学）
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Yale University（耶鲁大学）；IBM Research（IBM研究院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Medicine（耶鲁医学院）
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more challenging since the uncertain relatedness intra and inter each type of indices may hinder the learning procedure from better convergence…
MGH/BWH Center for Clinical Data Science（MGH/BWH临床数据科学中心）；Center for Data Science, Peking University（北京大学数据科学中心）；Beijing Institute of Big Data Research（北京大数据研究院）；Heart Center, Peking University People’s Hospital（北京大学人民医院心脏中心）
Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parame…
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the $\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated data…
CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI
Leiden University Medical Center（莱顿大学医学中心）；University of Sheffield（谢菲尔德大学）；INSIGNEO；Institute for In Silico Medicine（计算医学研究所）；Department of Infection, Immunity and Cardiovascular Disease（感染、免疫与心血管疾病部门）；Sheffield Teaching Hospitals（谢菲尔德教学医院）；Department of Clinical Radiology（临床放射学部门）
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；MRC London Institute of Medical Sciences（医学研究理事会伦敦医学科学研究所）
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operator-dependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based qu…
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）
University of Central Florida（中佛罗里达大学）；Florida Hospital（佛罗里达医院）
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our …
Fudan University（复旦大学）；Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Zhongshan Hospital Affiliated to Fudan University（复旦大学附属中山医院）
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-world respiratory motion artifacts is unclear. This manuscript describes the design of extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion Challenge). The challenge aims to establish a public benchmark dataset to assess the effects of respiratory motion on image quality and examine the robustness of segmentation models. The challenge recruited 40 healthy volunteers to perform different breath-ho…
Friedrich-Alexander-University Erlangen-Nuremberg（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）
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardia…
Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）；University Hospital of Tübingen（图宾根大学医院）
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion. In this work, we propose a learning-based self-supervised framework for MCMR, to efficiently deal with non-rigid motion corruption in cardiac MR imaging. Contrary to conventional MCMR methods in which the motion is estimated prior to reconstruction and remains unchanged during the iterative optimization process, we introduce a dynamic motion estimation process and embed …
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricu…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；United Imaging Intelligence, Cambridge, MA（联影智能（剑桥，马萨诸塞州））；University of Oregon（俄勒冈大学）
Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance. Our teacher model provides more accurate motion estimati…
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing thi…
