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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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Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree. Int J Comput Assist Radiol Surg 2021; 16:1493-1505. [PMID: 34101135 DOI: 10.1007/s11548-021-02421-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial. METHODS An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result. RESULTS The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 [Formula: see text] 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 [Formula: see text] 0.024 mm, 6.74 [Formula: see text] 0.013 mm, and 0.901 [Formula: see text] 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively. CONCLUSION Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs).
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Atehortúa A, Garreau M, Simon A, Donal E, Lederlin M, Romero E. Fusion of 3D real-time echocardiography and cine MRI using a saliency analysis. Int J Comput Assist Radiol Surg 2019; 15:277-285. [PMID: 31713090 DOI: 10.1007/s11548-019-02087-w] [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: 01/14/2019] [Accepted: 11/04/2019] [Indexed: 10/25/2022]
Abstract
PURPOSE This paper presents a novel 3D multimodal registration strategy to fuse 3D real-time echocardiography images with cardiac cine MRI images. This alignment is performed in a saliency space, which is designed to maximize similarity between the two imaging modalities. This fusion improves the quality of the available information. METHODS The method performs in two steps: temporal and spatial registrations. A temporal alignment is firstly achieved by nonlinearly matching pairs of correspondences between the two modalities using a dynamic time warping. A temporal registration is then carried out by applying nonrigid transformations in a common saliency space where normalized cross correlation between temporal pairs of salient volumes is maximized. RESULTS The alignment performance was evaluated with a set of 18 subjects, 3 with cardiomyopathies and 15 healthy, by computing the Dice score and Hausdorff distance with respect to manual delineations of the left ventricle cavity in both modalities. A Dice score and Hausdorff distance of [Formula: see text] and [Formula: see text], respectively, were obtained. In addition, the deformation field was estimated by quantifying its foldings, obtaining a 98% of regularity in the deformation field. CONCLUSIONS The 3D multimodal registration strategy presented is performed in a saliency space. Unlike state-of-the-art methods, the presented one takes advantage of the temporal information of the heart to construct this common space, ending up with two well-aligned modalities and regular deformation fields. This preliminary study was evaluated on heterogeneous data composed of two different datasets, healthy and pathological cases, showing similar performances in both cases. Future work will focus on testing the presented strategy in a larger dataset with a balanced number of classes.
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Affiliation(s)
- Angélica Atehortúa
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France. .,Universidad Nacional de Colombia, Bogotá, Colombia.
| | - Mireille Garreau
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Antoine Simon
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Erwan Donal
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Mathieu Lederlin
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
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Khalil A, Ng SC, Liew YM, Lai KW. An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment. Cardiol Res Pract 2018; 2018:1437125. [PMID: 30159169 PMCID: PMC6109558 DOI: 10.1155/2018/1437125] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/05/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
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Affiliation(s)
- Azira Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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