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Yu H, Wang Z, Wu H, Zhu Z, Wang J, Fang R, Wu S, Xie H, Huang X, Benitez Mendieta J, Anbananthan H, Li Z. In-vivo left atrial surface motion and strain measurement using novel mesh regularized image block matching method with 4D-CTA. J Biomech 2024; 176:112354. [PMID: 39383691 DOI: 10.1016/j.jbiomech.2024.112354] [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: 03/12/2024] [Revised: 08/16/2024] [Accepted: 09/30/2024] [Indexed: 10/11/2024]
Abstract
Atrial strain and motion play important roles in evaluation of stroke risks for patients with atrial fibrillation. While cardiac computed tomographic angiography (CTA) provides detailed left atrial morphology with unparallel image resolution, finding a suitable strain measurement method for CTA remains a considerable challenge. In this paper, for the first time, we introduced a mesh regularized image block matching method to estimate 3D left atrial (LA) surface strain with 4D CTA. A series of performance tests with ex-vivo phantom and in-vivo 4D-CTA data were deployed. In conclusion, our proposed method could provide reliable LA motion and strain data within limited time.
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Affiliation(s)
- Han Yu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Zidun Wang
- First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China.
| | - Hao Wu
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
| | - Zhengduo Zhu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Jiaqiu Wang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; School of Engineering, London South Bank University, London SE1 0AA, UK.
| | - Runxing Fang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
| | - Shanglin Wu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Hujin Xie
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Xianjue Huang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
| | - Jessica Benitez Mendieta
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Haveena Anbananthan
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Zhiyong Li
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; Faculty of Sports Science, Ningbo University, Ningbo 315211, Zhejiang, China.
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Xie H, Wu H, Wang J, Mendieta JB, Yu H, Xiang Y, Anbananthan H, Zhang J, Zhao H, Zhu Z, Huang Q, Fang R, Zhu C, Li Z. Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107975. [PMID: 38128464 DOI: 10.1016/j.cmpb.2023.107975] [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: 06/28/2023] [Revised: 11/08/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment. METHODS The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data. RESULTS The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points. CONCLUSIONS Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation.
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Affiliation(s)
- Hujin Xie
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Hao Wu
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Jiaqiu Wang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; School of Engineering, London South Bank University, London, UK
| | - Jessica Benitez Mendieta
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Han Yu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Yuqiao Xiang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Haveena Anbananthan
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Jianjian Zhang
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, China
| | - Huilin Zhao
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, China
| | - Zhengduo Zhu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Qiuxiang Huang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Runxing Fang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Chengcheng Zhu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States
| | - Zhiyong Li
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; Faculty of Sports Science, Ningbo University, Ningbo, Zhejiang 315211, China.
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Lin X, Chen J, Sun C. High-accuracy optical coherence elastography digital volume correlation methods to measure depth regions with low correlation. JOURNAL OF BIOPHOTONICS 2024; 17:e202300094. [PMID: 37774123 DOI: 10.1002/jbio.202300094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 10/01/2023]
Abstract
The decreasing correlation of optical coherence tomography (OCT) images with depth is an unavoidable problem for the depth measurement of the digital volume correlation (DVC) based optical coherence elastography (OCE) method. We propose an OCE-DVC method to characterize biological tissue deformation in deeper regions. The method proposes a strategy based on reliability layer guided displacement tracking to achieve the OCE-DVC method for the deformation measurement in deep regions of OCT images. Parallel computing solves the computational burden associated with the OCE-DVC method. The layer-by-layer adaptive data reading methods are used to guarantee the parallel computing of high-resolution OCT images. The proposed method shown in this study nearly doubles the depth of quantitative characterization of displacement and strain. At this depth, the standard deviation of displacement and strain measurements is reduced by nearly 78%. Under nonuniform deformation field, OCE-DVC method tracked the displacement with large strain gradient in depth region.
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Affiliation(s)
- Xianglong Lin
- Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin, China
| | - Jinlong Chen
- Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin, China
| | - Cuiru Sun
- Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin, China
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