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Willems E, Janssens KLPM, Dekker LRC, van de Vosse FN, Cluitmans MJM, Bovendeerd PHM. Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia. Front Physiol 2024; 15:1330157. [PMID: 38655031 PMCID: PMC11036413 DOI: 10.3389/fphys.2024.1330157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.
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Affiliation(s)
- Evianne Willems
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Koen L. P. M. Janssens
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Lukas R. C. Dekker
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands
| | - Frans N. van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Matthijs J. M. Cluitmans
- Maastricht University Medical Center, Maastricht, Netherlands
- Philips Research Eindhoven, Eindhoven, Netherlands
| | - Peter H. M. Bovendeerd
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Amirrajab S, Khalil YA, Lorenz C, Weese J, Pluim J, Breeuwer M. A Framework for Simulating Cardiac MR Images With Varying Anatomy and Contrast. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:726-738. [PMID: 36260571 DOI: 10.1109/tmi.2022.3215798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
One of the limiting factors for the development and adoption of novel deep-learning (DL) based medical image analysis methods is the scarcity of labeled medical images. Medical image simulation and synthesis can provide solutions by generating ample training data with corresponding ground truth labels. Despite recent advances, generated images demonstrate limited realism and diversity. In this work, we develop a flexible framework for simulating cardiac magnetic resonance (MR) images with variable anatomical and imaging characteristics for the purpose of creating a diversified virtual population. We advance previous works on both cardiac MR image simulation and anatomical modeling to increase the realism in terms of both image appearance and underlying anatomy. To diversify the generated images, we define parameters: 1)to alter the anatomy, 2) to assign MR tissue properties to various tissue types, and 3) to manipulate the image contrast via acquisition parameters. The proposed framework is optimized to generate a substantial number of cardiac MR images with ground truth labels suitable for downstream supervised tasks. A database of virtual subjects is simulated and its usefulness for aiding a DL segmentation method is evaluated. Our experiments show that training completely with simulated images can perform comparable with a model trained with real images for heart cavity segmentation in mid-ventricular slices. Moreover, such data can be used in addition to classical augmentation for boosting the performance when training data is limited, particularly by increasing the contrast and anatomical variation, leading to better regularization and generalization. The database is publicly available at https://osf.io/bkzhm/ and the simulation code will be available at https://github.com/sinaamirrajab/CMRI.
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Kruithof E, Amirrajab S, Lau KD, Breeuwer M. Simulated late gadolinium enhanced cardiac magnetic resonance imaging dataset from mechanical XCAT phantom including a myocardial infarct. Data Brief 2022; 40:107691. [PMID: 34984215 PMCID: PMC8693344 DOI: 10.1016/j.dib.2021.107691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/18/2022] Open
Abstract
The late enhanced magnetic resonance image dataset in this article is simulated using a mechanistic cardiac phantom that includes an myocardial infarct. Settings of the image simulation pipeline are adjusted such that high- and low-resolution images, with and without slice alignment artifacts, are simulated. Our article on the influence of image artifacts on image-based models of the cardiac electrophysiology is based on this data (Kruithof et al., 2021). This dataset provides image-analysis researchers a reference to perform validation of their methods using the included high-resolution ground truth image, a resource that is often unavailable clinically.
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Affiliation(s)
- Evianne Kruithof
- Eindhoven University of Technology, the Netherland
- Corresponding author.
| | | | | | - Marcel Breeuwer
- Eindhoven University of Technology, the Netherland
- Philips Healthcare Best, the Netherland
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Tong L, Zhao C, Fu Z, Dong R, Wu Z, Wang Z, Zhang N, Wang X, Cao B, Sun Y, Zheng D, Xia L, Deng D. Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Front Physiol 2021; 12:733500. [PMID: 35002750 PMCID: PMC8739986 DOI: 10.3389/fphys.2021.733500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.
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Affiliation(s)
- Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Caiming Zhao
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhenyin Fu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruiqing Dong
- Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Zhenghong Wu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zefeng Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Xinlu Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Boyang Cao
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Yutong Sun
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, United Kingdom
| | - Ling Xia
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
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