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Related papers: DeepMesh: Mesh-based Cardiac Motion Tracking using…

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Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase analysis. Accurate cardiac keyframe detection can eliminate…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Sven Koehler , Sarah Kaye Mueller , Jonathan Kiekenap , Gerald Greil , Tarique Hussain , Samir Sarikouch , Florian André , Norbert Frey , Sandy Engelhardt

Cardiac deformation is a crucial biomarker for the evaluation of cardiac function. Current methods for estimating cardiac strain might underestimate local deformation due to through-plane motion and segmental averaging. Mesh-based mapping…

Tissues and Organs · Quantitative Biology 2025-04-07 Beatrice Moscoloni , Patrick Segers , Mathias Peirlinck

Cardiac Magnetic Resonance (CMR) imaging serves as the gold-standard for evaluating cardiac morphology and function. Typically, a multi-view CMR stack, covering short-axis (SA) and 2/3/4-chamber long-axis (LA) views, is acquired for a…

Image and Video Processing · Electrical Eng. & Systems 2025-07-03 Yundi Zhang , Chen Chen , Suprosanna Shit , Sophie Starck , Daniel Rueckert , Jiazhen Pan

Cardiac function evaluation necessitates continuous, non-invasive monitoring, a capability limited in MRI. Millimeter-wave (mmWave) radar and its Synthetic Aperture Radar (SAR) mode offer a privacy-preserving and portable point-of-care…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Jinye Li , Chenxi Fu , Minghang Zheng , Yang Liu , Xiahai Zhuang , Qingchao Chen

Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the…

Image and Video Processing · Electrical Eng. & Systems 2020-12-23 Yinzhe Wu , Suzan Hatipoglu , Diego Alonso-Álvarez , Peter Gatehouse , David Firmin , Jennifer Keegan , Guang Yang

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging (cMRI) is crucial for improved cardiovascular disease diagnosis and understanding of the heart's motion. However, current cardiac MRI-based reconstruction technology…

Image and Video Processing · Electrical Eng. & Systems 2023-08-21 Xiaoxiao He , Chaowei Tan , Ligong Han , Bo Liu , Leon Axel , Kang Li , Dimitris N. Metaxas

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…

Image and Video Processing · Electrical Eng. & Systems 2020-09-17 Hanchao Yu , Xiao Chen , Humphrey Shi , Terrence Chen , Thomas S. Huang , Shanhui Sun

Background: Quantitative stress perfusion cardiovascular magnetic resonance (CMR) is a powerful tool for assessing myocardial ischemia. Motion correction is essential for accurate pixel-wise mapping but traditional registration-based…

Computer Vision and Pattern Recognition · Computer Science 2025-10-02 Noortje I. P. Schueler , Nathan C. K. Wong , Richard J. Crawley , Josien P. W. Pluim , Amedeo Chiribiri , Cian M. Scannell

Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally. However, current techniques suffer from incomplete and inaccurate…

Image and Video Processing · Electrical Eng. & Systems 2024-06-28 Chengkang Shen , Hao Zhu , You Zhou , Yu Liu , Si Yi , Lili Dong , Weipeng Zhao , David J. Brady , Xun Cao , Zhan Ma , Yi Lin

Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases, for which cine cardiac magnetic resonance (CMR) has been established as the gold standard imaging modality. Many existing methods…

Image and Video Processing · Electrical Eng. & Systems 2025-07-24 Jiahui Yin , Xinxing Cheng , Jinming Duan , Yan Pang , Declan O'Regan , Hadrien Reynaud , Qingjie Meng

Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty…

Computer Vision and Pattern Recognition · Computer Science 2021-04-20 Meng Ye , Mikael Kanski , Dong Yang , Qi Chang , Zhennan Yan , Qiaoying Huang , Leon Axel , Dimitris Metaxas

Medical imaging refers to the technologies and methods utilized to view the human body and its inside, in order to diagnose, monitor, or even treat medical disorders. This paper aims to explore the application of deep learning techniques in…

Image and Video Processing · Electrical Eng. & Systems 2024-11-08 Ketan Suhaas Saichandran

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…

Computer Vision and Pattern Recognition · Computer Science 2018-09-03 Ilkay Oksuz , Bram Ruijsink , Esther Puyol-Anton , Aurelien Bustin , Gastao Cruz , Claudia Prieto , Daniel Rueckert , Julia A. Schnabel , Andrew P. King

Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility. Accurate left ventricle delineation is a prerequisite for robust and reproducible myocardial…

Image and Video Processing · Electrical Eng. & Systems 2021-04-28 Mengmeng Kuang , Yinzhe Wu , Diego Alonso-Álvarez , David Firmin , Jennifer Keegan , Peter Gatehouse , Guang Yang

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images…

Image and Video Processing · Electrical Eng. & Systems 2021-04-01 Roshan Reddy Upendra , Brian Jamison Wentz , Richard Simon , Suzanne M. Shontz , Cristian A. Linte

Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic,…

Artificial Intelligence · Computer Science 2025-06-03 Mengyun Qiao , Kathryn A McGurk , Shuo Wang , Paul M. Matthews , Declan P O Regan , Wenjia Bai

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and…

Image and Video Processing · Electrical Eng. & Systems 2023-09-12 Joseph Kettelkamp , Ludovica Romanin , Davide Piccini , Sarv Priya , Mathews Jacob

Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensional mesh models that are critical in computational medicine and in silico trials for…

Computer Vision and Pattern Recognition · Computer Science 2025-05-07 Fengming Lin , Arezoo Zakeri , Yidan Xue , Michael MacRaild , Haoran Dou , Zherui Zhou , Ziwei Zou , Ali Sarrami-Foroushani , Jinming Duan , Alejandro F. Frangi

Echocardiogram is the most commonly used imaging modality in cardiac assessment duo to its non-invasive nature, real-time capability, and cost-effectiveness. Despite its advantages, most clinical echocardiograms provide only two-dimensional…

Image and Video Processing · Electrical Eng. & Systems 2025-05-12 Xilin Gong , Yongkai Chen , Shushan Wu , Fang Wang , Ping Ma , Wenxuan Zhong

Accurate analysis of cardiac motion is crucial for evaluating cardiac function. While dynamic cardiac magnetic resonance imaging (CMR) can capture detailed tissue motion throughout the cardiac cycle, the fine-grained 4D cardiac motion…

Computer Vision and Pattern Recognition · Computer Science 2025-07-23 Xueming Fu , Pei Wu , Yingtai Li , Xin Luo , Zihang Jiang , Junhao Mei , Jian Lu , Gao-Jun Teng , S. Kevin Zhou