In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected sequentially under free-breathing conditions. A complete heartbeat from each slice is then used for cardiac function quantification. The inter-slice respiratory mismatch can compromise accurate quantification of cardiac function. Methods based on principal components analysis (PCA) have been proposed to extract the respiratory signal from RT cardiac cine, but these methods cannot resolve the inter-slice sign ambiguity of the respiratory signal. In this work, we propose a fully automatic sign correction procedure based on the similarity of neighboring slices and correlation to the center-of-mass curve. The proposed method is evaluated in eleven volunteers, with ten slices per volunteer. The motion in a manually selected region-of-interest (ROI) is used as a reference. The results show that the extracted respiratory signal has a high, positive correlation with the reference in all cases. The qualitative assessment of images also shows that the proposed approach can accurately identify heartbeats, one from each slice, belonging to the same respiratory phase. This approach can improve cardiac function quantification for RT cine without manual intervention.
Yale University（耶鲁大学）；Siemens Medical Solutions USA Inc.（美国西门子医疗解决方案公司）
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available. Thus, we propose Repetitive Motion Estimation Network (RMEN) to recover cardiac and respiratory signals. It learns the spatio-temporal repetition patterns, embedding high dimensional motion manifolds to 1D vectors with partial motion phase boundary annotations. Compared with the best alternative models, our proposed RMEN significantly decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN could handle the irregular cardiac and respiratory motion cases.…
Respiratory motion can cause strong blurring artifacts in the reconstructed image during MR acquisition. These artifacts become more prominent when use in the presence of undersampled data. Recently, compressed sensing (CS) is developed as an MR reconstruction technique, to recover good quality images from the compressive k-space samples. To maximize the benefits of CS in free breathing data, it is understandable to use CS with the motion corrected images. In this paper, we have developed a new CS based motion corrected image reconstruction technique. In this two-stage technique, we use simila…
Purpose: To develop a fully automatic method for extraction and directionality determination of respiratory signal in free-breathing, real-time (RT) cardiac MRI. Methods: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. First, the signal polarity of all slices is made consistent with a reference slice. Second, a global sign correction is performed by maximizing the correlation of the respiratory signal with the zeroth-moment center curve. The pro…
Dublin City University（都柏林城市大学）；University College Dublin（都柏林大学学院）
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those…
In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to translate noisy internal motion to external surrogate: 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of c…
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…
We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank…
Fudan University（复旦大学）；Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；National Heart Research Institute, Singapore（新加坡国家心脏研究所）
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Give…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, Shanghai 200032, China；Shanghai Key Laboratory of MICCAI, Fudan University, Shanghai, Shanghai 200032, China；Department of Electrical；Electronic Engineering \& I-X, Imperial College London, London, London SW7 2AZ, United Kingdom；Department of Radiology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, Shanghai 200032, China；Department of Computing, Imperial College London, London, London SW7 2AZ, United Kingdom；School of Computer Science, University of Sheffield, Sheffield, S1 4DP, United Kingdom；Department of Engineering Science, University of Oxford, Oxford, OX2 0ES, United Kingdom；Shanghai Pudong Hospital；Human Phenome Institute, Fudan University, Shanghai, 201203, China；School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, United Kingdom；Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 1K4, Canada；Department of Computer Science；Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong 000000, China；The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology；Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands；Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven 5612 AZ, The Netherlands；Department of Radiology, Northwestern University, 737 N. Michigan Ave, Suite 1600, Chicago 60611, United States；United Imaging Research, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China；Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands；Université Bourgogne Europe, CNRS, ICMUB UMR 6302, 21000 Dijon, France；Istanbul Technical University, Maslak, 34467, İstanbul, Türkiye；Computer Science, Technical University of Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany；Lung Institute, Faculty of Medicine, Imperial College London, Guy Scadding Building, Cale Street, London, SW3 6LY,United Kingdom；Hawkes Institute, Department of Computer Science, University College London, 66-72 Gower St, London, United Kingdom；School of Medicine, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland；CeADAR: Ireland's Centre for AI, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland；Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054, Krakow, Poland；Faculty of Mathematics；Computer Science, Jagiellonian University, S. Łojasiewicza 6, Krakow, Poland；Department of Electronic；Computer Engineering, The Hong Kong University of Science；School of Instrument Science；Engineering, Southeast University, Nanjing, Nanjing 210096, China；College of Artificial Intelligence, Nanjing University of Aeronautics；Academy for Engineering；Technology, Fudan University, Shanghai, Shanghai 200433, China；College of Biomedical Engineering, Fudan University, Shanghai, Shanghai 200433, China；Institute of Science；Technology for Brain-inspired Intelligence, Fudan University, Shanghai, Shanghai 200433, China；Department of Brain Sciences, Imperial College London, London, London SW7 2AZ, United Kingdom；Data Science Institute, Imperial College London, London, London SW7 2AZ, United Kingdom
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，俄亥俄州立大学）；Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG（研究与临床转化，磁共振，西门子医疗有限公司）；Division of Cardiovascular Medicine, The Ohio State University（心血管医学系，俄亥俄州立大学）
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…
Background: RSN whole-heart CMRA is a technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical simulation, 22 healthy volunteers, and 549 cardiac patients. We compar…
University of Stuttgart（斯图加特大学）；ShanghaiTech University（上海科技大学）；RMIT University（皇家墨尔本理工大学）
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, im…
The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. The lack of equivalent ground truth has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise. To add…
