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Hu J, Zhou S, Ryu S, Adams K, Gao Z. Effects of Long-Term Endurance Exercise on Cardiac Morphology, Function, and Injury Indicators among Amateur Marathon Runners. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2600. [PMID: 36767963 PMCID: PMC9916084 DOI: 10.3390/ijerph20032600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/22/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The purpose of this study was to investigate the effects of long-term endurance exercise on cardiac morphology and function, as well as injury indicators, among amateur marathon runners. We recruited 33 amateur runners who participated in a marathon. Participants were divided into experimental and control groups according to their National Athletic Grade. The experimental group included participants with a National Athletic Grade of 2 or better, and the control group included participants who did not have a National Athletic Grade. Cardiac morphology, function, and injury indicators were assessed before and after the participants' involvement in the Changsha International Marathon. All cardiac morphology and function indicators returned to pre-race levels at 24 h post-race, and left ventricular end-diastolic volume and left ventricular end-systolic volume indicators showed similar trends. Both stroke volume (SV) and percent fractional shortening (%FS) indicators showed similar trends in changes in the measurements before and after the race. SV showed no change between the pre-race and post-race periods. On the other hand, %FS showed a significant increase in the immediate post-race period, followed by restoration of its level at 24 h post-race. Among myocardial injury indicators, serum levels of cardiac troponin I, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and N-terminal pro-b-type natriuretic peptide (NT-proBNP) measured before the race, immediately after the race, and 24 h after the race displayed similar trends in changes among CK, CK-MB, LDH, and AST, while NT-proBNP levels did not change. We concluded that high-level amateur marathon runners had greater heart volumes, as well as wall and septal thicknesses, than low-level marathon runners, with differences in heart volume being the most pronounced. Long-term high-intensity endurance exercise caused some damage to the hearts of amateur runners. High-level runners showed better myocardial repair ability, and their levels of myocardial injury markers showed greater decreases at 24 h post-race, while low-level runners had poorer myocardial repair ability.
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Affiliation(s)
- Jianzhong Hu
- School of Physical Education, Hengyang Normal University, Hengyang 421002, China
| | - Songqing Zhou
- School of Physical Education, Hengyang Normal University, Hengyang 421002, China
| | - Suryeon Ryu
- School of Kinesiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kaitlyn Adams
- School of Kinesiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zan Gao
- School of Kinesiology, University of Minnesota, Minneapolis, MN 55455, USA
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Atehortúa A, Romero E, Garreau M. Characterization of motion patterns by a spatio-temporal saliency descriptor in cardiac cine MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106714. [PMID: 35263659 DOI: 10.1016/j.cmpb.2022.106714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 02/03/2022] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Abnormalities of the heart motion reveal the presence of a disease. However, a quantitative interpretation of the motion is still a challenge due to the complex dynamics of the heart. This work proposes a quantitative characterization of regional cardiac motion patterns in cine magnetic resonance imaging (MRI) by a novel spatio-temporal saliency descriptor. METHOD The strategy starts by dividing the cardiac sequence into a progression of scales which are in due turn mapped to a feature space of regional orientation changes, mimicking the multi-resolution decomposition of oriented primitive changes of visual systems. These changes are estimated as the difference between a particular time and the rest of the sequence. This decomposition is then temporarily and regionally integrated for a particular orientation and then for the set of different orientations. A final spatio-temporal 4D saliency map is obtained as the summation of the previously integrated information for the available scales. The saliency dispersion of this map was computed in standard cardiac locations as a measure of the regional motion pattern and was applied to discriminate control and hypertrophic cardiomyopathy (HCM) subjects during the diastolic phase. RESULTS Salient motion patterns were estimated from an experimental set, which consisted of 3D sequences acquired by MRI from 108 subjects (33 control, 35 HCM, 20 dilated cardiomyopathy (DCM), and 20 myocardial infarction (MINF) from heterogeneous datasets). HCM and control subjects were classified by an SVM that learned the salient motion patterns estimated from the presented strategy, by achieving a 94% AUC. In addition, statistical differences (test t-student, p<0.05) were found among groups of disease in the septal and anterior ventricular segments at both the ED and ES, with salient motion characteristics aligned with existing knowledge on the diseases. CONCLUSIONS Regional wall motion abnormality in the apical, anterior, basal, and inferior segments was associated with the saliency dispersion in HCM, DCM, and MINF compared to healthy controls during the systolic and diastolic phases. This saliency analysis may be used to detect subtle changes in heart function.
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Affiliation(s)
- Angélica Atehortúa
- Universidad Nacional de Colombia, Bogotá, Colombia; Univ Rennes, Inserm, LTSI UMR 1099, Rennes F-35000, France
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Suinesiaputra A, Mauger CA, Ambale-Venkatesh B, Bluemke DA, Dam Gade J, Gilbert K, Janse MHA, Hald LS, Werkhoven C, Wu CO, Lima JAC, Young AA. Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis. Front Cardiovasc Med 2022; 8:807728. [PMID: 35127868 PMCID: PMC8813768 DOI: 10.3389/fcvm.2021.807728] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/24/2021] [Indexed: 12/23/2022] Open
Abstract
The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular magnetic resonance (CMR) to study the mechanisms of cardiovascular disease in over 5,000 initially asymptomatic participants, and there is now a wealth of follow-up data over 20 years. However, the imaging technology used to generate the CMR images is no longer in routine use, and methods trained on modern data fail when applied to such legacy datasets. This study aimed to develop a fully automated CMR analysis pipeline that leverages the ability of machine learning algorithms to enable extraction of additional information from such a large-scale legacy dataset, expanding on the original manual analyses. We combined the original study analyses with new annotations to develop a set of automated methods for customizing 3D left ventricular (LV) shape models to each CMR exam and build a statistical shape atlas. We trained VGGNet convolutional neural networks using a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views to detect landmarks. A U-Net architecture was used to detect the endocardial and epicardial boundaries in short-axis images. The landmark detection network accurately predicted mitral valve and right ventricular insertion points with average error distance <2.5 mm. The agreement of the network with two observers was excellent (intraclass correlation coefficient >0.9). The segmentation network produced average Dice score of 0.9 for both myocardium and LV cavity. Differences between the manual and automated analyses were small, i.e., <1.0 ± 2.6 mL/m2 for indexed LV volume, 3.0 ± 6.4 g/m2 for indexed LV mass, and 0.6 ± 3.3% for ejection fraction. In an independent atlas validation dataset, the LV atlas built from the fully automated pipeline showed similar statistical relationships to an atlas built from the manual analysis. Hence, the proposed pipeline is not only a promising framework to automatically assess additional measures of ventricular function, but also to study relationships between cardiac morphologies and future cardiac events, in a large-scale population study.
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Affiliation(s)
- Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Charlène A. Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | | | - David A. Bluemke
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Josefine Dam Gade
- Department of Biomedical Engineering and Informatics, School of Medicine and Health, Aalborg University, Aalborg, Denmark
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Markus H. A. Janse
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Line Sofie Hald
- Department of Biomedical Engineering and Informatics, School of Medicine and Health, Aalborg University, Aalborg, Denmark
| | - Conrad Werkhoven
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Colin O. Wu
- Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Baltimore, MD, United States
| | | | - Alistair A. Young
- Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Alistair A. Young
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Liu D, Dangi S, Schwarz KQ, Linte CA. Combining Statistical Shape Model and Principal Component Analysis to Estimate Left Ventricular Volume and Ejection Fraction. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11319:113190E. [PMID: 32699463 PMCID: PMC7375748 DOI: 10.1117/12.2550650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%,1 which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient's LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient's LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient's LV geometry and associated blood pool volume. We tested the proposed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.
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Affiliation(s)
- Dawei Liu
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Shusil Dangi
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Karl Q Schwarz
- Medicine, Cardiology, University of Rochester Medical Center, Rochester, NY, USA
- Anesthesiology and Perioperative Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Cristian A Linte
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
- Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
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Duchateau N, King AP, De Craene M. Machine Learning Approaches for Myocardial Motion and Deformation Analysis. Front Cardiovasc Med 2020; 6:190. [PMID: 31998756 PMCID: PMC6962100 DOI: 10.3389/fcvm.2019.00190] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 12/12/2019] [Indexed: 12/21/2022] Open
Abstract
Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.
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Affiliation(s)
| | - Andrew P. King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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6
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Liu D, Peck I, Dangi S, Schwarz KQ, Linte CA. A Statistical Shape Model Approach for Computing Left Ventricle Volume and Ejection Fraction Using Multi-plane Ultrasound Images. VIPIMAGE 2019 : PROCEEDINGS OF THE VII ECCOMAS THEMATIC CONFERENCE ON COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING, OCTOBER 16-18, 2019, PORTO, PORTUGAL. VIPIMAGE (CONFERENCE) (2019 : PORTO, PORTUGAL) 2019; 34:540-550. [PMID: 32661520 PMCID: PMC7357900 DOI: 10.1007/978-3-030-32040-9_55] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Assessing the left ventricular ejection fraction (LVEF) accurately requires 3D volumetric data of the LV. Cardiologists either have no access to 3D ultrasound (US) systems or prefer to visually estimate LVEF based on 2D US images. To facilitate the consistent estimation of the end-diastolic and end-systolic blood pool volume and LVEF based on 3D data without extensive complicated manual input, we propose a statistical shape model (SSM) based on 13 key anchor points-the LV apex (1), mitral valve hinges (6), and the midpoints of the endocardial contours (6)-identified from the LV endocardial contour of the tri-plane 2D US images. We use principal component analysis (PCA) to identify the principle modes of variation needed to represent the LV shapes, which enables us to estimate an incoming LV as a linear combination of the principle components (PC). For a new, incoming patient image, its 13 anchor points are projected onto the PC space; its shape is compared to each LV shape in the SSM based on Mahalanobis distance, which is normalized with respect to the LV size, as well as direct vector distance (i.e., PCA distance), without any size normalization. These distances are used to determine the weight each training shape in the SSM contributes to the description of the new patient LV shape. Finally, the new patient's LV systolic and diastolic volumes are estimated as the weighted average of the training volumes in the SSM. To assess our proposed method, we compared the SSM-based estimates of diastolic, systolic, stroke volumes, and LVEF with those computed directly from 16 tri-plane 2D US imaging datasets using the GE Echo-Pac PC clinical platform. The estimated LVEF based on Mahalanobis distance and PCA distance were within 6.8% and 1.7% of the reference LVEF computed using the GE Echo-Pac PC clinical platform.
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Affiliation(s)
- Dawei Liu
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
| | - Isabelle Peck
- Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA
| | - Shusil Dangi
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
| | - Karl Q Schwarz
- University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642, USA
| | - Cristian A Linte
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
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7
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Lu G, Zhou L. Localization of prostatic tumor's infection based on normalized mutual information MRI image segmentation. J Infect Public Health 2019; 14:432-436. [PMID: 31492598 DOI: 10.1016/j.jiph.2019.08.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/11/2019] [Accepted: 08/15/2019] [Indexed: 01/23/2023] Open
Abstract
To investigate the effect of normalized mutual information (MRI) image segmentation in accurate localization of prostate cancer with infection and the role in disease treatment, the normalized mutual information method is used to measure the similarity of images, so as to select the maps. Then, the popular global weighted voting method and normalized mutual information method are applied to calculate the weights and carry out the label image fusion. The map selection method based on mutual information substantially completes the segmentation of the MRI image prostate. The prostate position is basically found on the 10 test images, and the positioning of the prostate organs is deviated in the worst case. In the case of poor multi-map segmentation, it usually happens when those are not well represented in the map. Because of the structural similarity of medical images, multi-atlas segmentation based on normalized mutual information method can be done. Using the prior information of atlas, the atlas label image can be selected. After fusion, the final segmentation of the test image can be completed, which has a high accuracy for the location of prostate cancer. This method can accurately delineate the target area in radiotherapy of prostate cancer and reduce the damage of rectum, bladder and other organs caused by radiotherapy. However, there are still some problems in this study, such as inadequate segmentation accuracy, long data processing time and so on. There is still a certain distance from practicality, and further research is needed.
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Affiliation(s)
- Guoping Lu
- Department of Urology, Guangxi Minzu Hospital, Nanning, Guangxi 530001, China.
| | - Lixin Zhou
- Department of Radiology, Guangxi Minzu Hospital, Nanning, Guangxi 530001, China
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Piras P, Torromeo C, Evangelista A, Esposito G, Nardinocchi P, Teresi L, Madeo A, Re F, Chialastri C, Schiariti M, Varano V, Puddu PE. Non-invasive prediction of genotype positive-phenotype negative in hypertrophic cardiomyopathy by 3D modern shape analysis. Exp Physiol 2019; 104:1688-1700. [PMID: 31424582 DOI: 10.1113/ep087551] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 08/14/2019] [Indexed: 11/08/2022]
Abstract
NEW FINDINGS What is the central question of this study? Can impaired deformational indicators for genotype positive for hypertrophic cardiomyopathy in subjects that do not exhibit a left-ventricular wall hypertrophy condition (G+LVH-) be determined using non-invasive 3D echocardiography? What is the main finding and its importance? Using 3D-STE and modern shape analysis, peculiar deformational impairments can be detected in G+LVH- subjects that can be classified with good accuracy. Moreover, the patterns of impairment are located mainly on the apical region in agreement with other evidence coming from previous biomechanical investigations. ABSTRACT We propose a non-invasive procedure for predicting genotype positive for hypertrophic cardiomyopathy (HCM) in subjects that do not exhibit a left-ventricular wall hypertrophy condition (G+LVH-); the procedure is based on the enhanced analysis of medical imaging from 3D speckle tracking echocardiography (3D-STE). 3D-STE, due to its low quality images, has not been used so far to detect effectively the G+LVH- condition. Here, we post-processed echocardiographic images exploiting the tools of modern shape analysis, and we studied the motion of the left ventricle (LV) during an entire cycle. We enrolled 82 controls, 21 HCM patients and 11 G+LVH- subjects. We followed two steps: (i) we selected the most impaired regions of the LV by analysing its strains; and (ii) we used shape analysis on these regions to classify the subjects. The G+LVH- subjects showed different trajectories and deformational attributes. We found high classification performance in terms of area under the receiver operating characteristic curve (∼90), sensitivity (∼78) and specificity (∼79). Our results showed that (i) G+LVH- subjects present important deformational impairments relative to healthy controls and (ii) modern shape analysis can efficiently predict genotype by means of a non-invasive and inexpensive technique such as 3D-STE.
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Affiliation(s)
- Paolo Piras
- Department of Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Rome, 00161, Italy
| | - Concetta Torromeo
- Department of Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Rome, 00161, Italy
| | | | - Giuseppe Esposito
- Department of Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Rome, 00161, Italy
| | - Paola Nardinocchi
- Department of Structural Engineering & Geotechnics, Sapienza Università di Roma, Rome, 00161, Italy
| | - Luciano Teresi
- Department of Mathematics & Physics, Roma Tre University, Rome, 00146, Italy
| | - Andrea Madeo
- Ospedale San Camillo-Forlanini, Rome, 00152, Italy
| | - Federica Re
- Ospedale San Camillo-Forlanini, Rome, 00152, Italy
| | | | - Michele Schiariti
- Department of Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Rome, 00161, Italy
| | - Valerio Varano
- Department of Architecture, Roma Tre University, Rome, 00146, Italy
| | - Paolo Emilio Puddu
- Department of Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Rome, 00161, Italy
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9
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Bello GA, Dawes TJ, Duan J, Biffi C, de Marvao A, Howard LSGE, Gibbs JSR, Wilkins MR, Cook SA, Rueckert D, O’Regan DP. Deep learning cardiac motion analysis for human survival prediction. NAT MACH INTELL 2019; 1:95-104. [PMID: 30801055 PMCID: PMC6382062 DOI: 10.1038/s42256-019-0019-2] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 01/09/2019] [Indexed: 01/09/2023]
Abstract
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
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Affiliation(s)
- Ghalib A. Bello
- MRC London Institute of Medical Sciences, Imperial College London,UK
| | - Timothy J.W. Dawes
- MRC London Institute of Medical Sciences, Imperial College London,UK
- National Heart and Lung Institute, Imperial College London, UK
| | - Jinming Duan
- MRC London Institute of Medical Sciences, Imperial College London,UK
- Department of Computing, Imperial College London, UK
| | - Carlo Biffi
- MRC London Institute of Medical Sciences, Imperial College London,UK
- Department of Computing, Imperial College London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London,UK
| | | | - J. Simon R. Gibbs
- National Heart and Lung Institute, Imperial College London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Martin R. Wilkins
- Division of Experimental Medicine, Department of Medicine, Imperial College London, UK
| | - Stuart A. Cook
- MRC London Institute of Medical Sciences, Imperial College London,UK
- National Heart and Lung Institute, Imperial College London, UK
- National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore
| | | | - Declan P. O’Regan
- MRC London Institute of Medical Sciences, Imperial College London,UK
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Di Achille P, Harouni A, Khamzin S, Solovyova O, Rice JJ, Gurev V. Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics. Front Physiol 2018; 9:1002. [PMID: 30154725 PMCID: PMC6102646 DOI: 10.3389/fphys.2018.01002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 07/09/2018] [Indexed: 11/13/2022] Open
Abstract
Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. For training, we designed high resolution models of non-transmural infarcts including refinements of the border zone around the lesion. This study is a first effort in developing a platform combining HPC models and machine learning to investigate cardiac function in heart failure patients with the goal of assisting clinical diagnostics.
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Affiliation(s)
- Paolo Di Achille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States
| | | | - Svyatoslav Khamzin
- Ural Federal University, Yekaterinburg, Russia.,Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
| | - Olga Solovyova
- Ural Federal University, Yekaterinburg, Russia.,Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
| | - John J Rice
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States
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3D myocardial deformation analysis from cine MRI as a marker of amyloid protein burden in cardiac amyloidosis: validation versus T1 mapping. Int J Cardiovasc Imaging 2018; 34:1937-1946. [DOI: 10.1007/s10554-018-1410-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 07/10/2018] [Indexed: 01/16/2023]
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