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Zhao D, Mauger CA, Gilbert K, Wang VY, Quill GM, Sutton TM, Lowe BS, Legget ME, Ruygrok PN, Doughty RN, Pedrosa J, D'hooge J, Young AA, Nash MP. Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping. Sci Rep 2023; 13:8118. [PMID: 37208380 PMCID: PMC10199025 DOI: 10.1038/s41598-023-33968-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/21/2023] [Indexed: 05/21/2023] Open
Abstract
Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.
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Affiliation(s)
- Debbie Zhao
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand.
| | - Charlène A Mauger
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Vicky Y Wang
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Gina M Quill
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Timothy M Sutton
- Counties Manukau Health Cardiology, Middlemore Hospital, Auckland, New Zealand
| | - Boris S Lowe
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
| | - Malcolm E Legget
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Peter N Ruygrok
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Robert N Doughty
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - João Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
| | - Jan D'hooge
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King's College London, London, UK
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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Duchateau N, Loncaric F, Cikes M, Doltra A, Sitges M, Bijnens B. Variability in the Assessment of Myocardial Strain Patterns: Implications for Adequate Interpretation. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:244-254. [PMID: 31784202 DOI: 10.1016/j.ultrasmedbio.2019.10.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 10/13/2019] [Accepted: 10/22/2019] [Indexed: 06/10/2023]
Abstract
Variability in global and regional peak strain has been thoroughly studied, but variability in spatiotemporal myocardial strain patterns has not been studied as well. This study reports on such variability and its implications for adequate disease interpretation. Forty in-training operators, distributed on 20 workstations, analyzed six cases with representative deformation patterns with commercial speckle tracking. Inter-operator differences were quantified through the variability in myocardial delineations, spatiotemporal longitudinal strain patterns and peak longitudinal strain. Intra-operator differences were assessed similarly using 10 repeated measurements from a single clinician expert. Delineations varied mainly along the lateral wall and at the valve level. Peak longitudinal strain variability was low to moderate. The spatiotemporal strain patterns were consistent despite high variability at the apex and near the valve. The results indicate that relevant pattern assessment is possible despite heterogeneous experience with speckle tracking and that careful interpretation of pattern abnormalities should be recommended before a more systematic quantitative analysis.
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Affiliation(s)
- Nicolas Duchateau
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220, INSERM U1206, Université Lyon 1, Lyon, France.
| | - Filip Loncaric
- Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine and University Hospital Center Zagreb, Croatia
| | - Adelina Doltra
- Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Sitges
- Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Bart Bijnens
- Universitat Pompeu Fabra, Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain; Katholieke Universiteit Leuven, Leuven, Belgium
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Duchateau N, Giraldeau G, Gabrielli L, Fernández-Armenta J, Penela D, Evertz R, Mont L, Brugada J, Berruezo A, Sitges M, Bijnens BH. Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences. Med Image Anal 2015; 19:203-19. [DOI: 10.1016/j.media.2014.10.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Revised: 10/17/2014] [Accepted: 10/21/2014] [Indexed: 10/24/2022]
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Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models. Med Biol Eng Comput 2013; 51:1235-50. [PMID: 23430328 DOI: 10.1007/s11517-013-1044-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Accepted: 02/02/2013] [Indexed: 01/18/2023]
Abstract
This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.
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