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Li J, Chen K, He L, Luo F, Wang X, Hu Y, Zhao J, Zhu K, Chen X, Zhang Y, Tao H, Dong J. Data-driven classification of left atrial morphology and its predictive impact on atrial fibrillation catheter ablation. J Cardiovasc Electrophysiol 2024; 35:811-820. [PMID: 38424601 DOI: 10.1111/jce.16228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 03/02/2024]
Abstract
INTRODUCTION Various left atrial (LA) anatomical structures are correlated with postablative recurrence for atrial fibrillation (AF) patients. Comprehensively integrating anatomical structures, digitizing them, and implementing in-depth analysis, which may supply new insights, are needed. Thus, we aim to establish an interpretable model to identify AF patients' phenotypes according to LA anatomical morphology, using machine learning techniques. METHODS AND RESULTS Five hundred and nine AF patients underwent first ablation treatment in three centers were included and were followed-up for postablative recurrent atrial arrhythmias. Data from 369 patients were regarded as training set, while data from another 140 patients, collected from different centers, were used as validation set. We manually measured 57 morphological parameters on enhanced computed tomography with three-dimensional reconstruction technique and implemented unsupervised learning accordingly. Three morphological groups were identified, with distinct prognosis according to Kaplan-Meier estimator (p < .001). Multivariable Cox model revealed that morphological grouping were independent predictors of 1-year recurrence (Group 1: HR = 3.00, 95% CI: 1.51-5.95, p = .002; Group 2: HR = 4.68, 95% CI: 2.40-9.11, p < .001; Group 3 as reference). Furthermore, external validation consistently demonstrated our findings. CONCLUSIONS Our study illustrated the feasibility of employing unsupervised learning for the classification of LA morphology. By utilizing morphological grouping, we can effectively identify individuals at different risks of postablative recurrence and thereby assist in clinical decision-making.
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Affiliation(s)
- Jiaju Li
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ke Chen
- Department of Cardiology, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Liu He
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Fangyuan Luo
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
- Department of Integrative Medicine Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Xianqing Wang
- Department of Cardiology, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Yucai Hu
- Department of Cardiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Jiangtao Zhao
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kui Zhu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaowei Chen
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuekun Zhang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Hailong Tao
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianzeng Dong
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Bhalodia R, Elhabian S, Adams J, Tao W, Kavan L, Whitaker R. DeepSSM: A blueprint for image-to-shape deep learning models. Med Image Anal 2024; 91:103034. [PMID: 37984127 PMCID: PMC11087075 DOI: 10.1016/j.media.2023.103034] [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: 10/14/2021] [Revised: 10/06/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
Abstract
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.
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Affiliation(s)
- Riddhish Bhalodia
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA.
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA
| | - Jadie Adams
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA
| | - Wenzheng Tao
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA
| | - Ladislav Kavan
- School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA
| | - Ross Whitaker
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA
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Atkins PR, Morris A, Elhabian SY, Anderson AE. A Correspondence-Based Network Approach for Groupwise Analysis of Patient-Specific Spatiotemporal Data. Ann Biomed Eng 2023; 51:2289-2300. [PMID: 37357248 PMCID: PMC11047278 DOI: 10.1007/s10439-023-03270-6] [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: 02/17/2023] [Accepted: 06/01/2023] [Indexed: 06/27/2023]
Abstract
Methods for statistically analyzing patient-specific data that vary both spatially and over time are currently either limited to summary statistics or require elaborate surface registration. We propose a new method, called correspondence-based network analysis, which leverages particle-based shape modeling to establish correspondence across a population and preserve patient-specific measurements and predictions through statistical analysis. Herein, we evaluated this method using three published datasets of the hip describing cortical bone thickness of the proximal femur, cartilage contact stress, and dynamic joint space between control and patient cohorts to evaluate activity- and group-based differences, as applicable, using traditional statistical parametric mapping (SPM) and our proposed spatially considerate correspondence-based network analysis approach. The network approach was insensitive to correspondence density, while the traditional application of SPM showed decreasing area of the region of significance with increasing correspondence density. In comparison to SPM, the network approach identified broader and more connected regions of significance for all three datasets. The correspondence-based network analysis approach identified differences between groups and activities without loss of subject and spatial specificity which could improve clinical interpretation of results.
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Affiliation(s)
- Penny R Atkins
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA
| | - Alan Morris
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Shireen Y Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Andrew E Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
- Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
- Department of Physical Therapy, University of Utah, Salt Lake City, UT, USA.
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Chen L, Li Q, Chen J, Qiu Z, Xiao J, Tang M, Wu Q, Shen Y, Dai X, Fang G, Lu H. A new procedure for elimination of atrial fibrillation associated with mitral valve disease: a proof-of-concept study. Int J Surg 2023; 109:2914-2925. [PMID: 37352525 PMCID: PMC10583919 DOI: 10.1097/js9.0000000000000566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/10/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Left atrial enlargement and fibrosis have been linked to the pathogenesis of atrial fibrillation (AF). The authors aimed to introduce a novel concept and develop a new procedure for AF treatment based on these characteristics. METHODS The study included three stages. The first stage was a descriptive study to clarify the characteristics of the left atrial enlargement and fibrosis' distribution in patients with mitral valve disease and long-standing persistent AF. Based on these characteristics, the authors introduced a novel concept for AF treatment, and then translated it into a new procedure. The second stage was a proof-of-concept study with this new procedure. The third stage was a comparative effectiveness research to compare the clinical outcomes between patients with this new procedure and those who received Cox-Maze IV treatment. RESULTS Based on the nonuniform fashion of left atrial enlargement and fibrosis' distribution, the authors introduced a novel concept: reconstructing a left atrium with appropriate geometry and uniform fibrosis' distribution for proper cardiac conduction, and translated it into a new procedure: left atrial geometric volume reduction combined with left appendage base closure. As compared to the Cox-Maze IV procedure, the new procedure spent significantly shorter total surgery time, cardiopulmonary bypass time, and aortic cross-clamp time ( P <0.001). Besides, the new procedure was related to a shorter ICU stay period (odd ratio (OR)=0.45, 95% CI=0.26-0.78), lower costs (OR=0.15, 95% CI=0.08-0.29), and a higher rate of A wave of transmitral and transtricuspid flow reappearance (OR=1.76, 95% CI=1.02-3.04). CONCLUSIONS The new procedure is safe and effective for eliminating AF associated with mitral valve disease.
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Affiliation(s)
- Liangwan Chen
- Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
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Suzuki S, Kitai T, Skoularigis J, Spiliopoulos K, Xanthopoulos A. Catheter Ablation for Atrial Fibrillation in Patients with Heart Failure: Current Evidence and Future Opportunities. J Pers Med 2023; 13:1394. [PMID: 37763161 PMCID: PMC10532515 DOI: 10.3390/jpm13091394] [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: 08/18/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Atrial fibrillation (AF) and heart failure (HF) are highly prevalent cardiac disorders worldwide, and both are associated with poor prognosis. The incidence of AF and HF has been increasing substantially in recent years, mainly due to the progressive aging of the population. These disorders often coexist, and may have a causal relationship, with one contributing to the development or progression of the other. AF is a significant risk factor for adverse outcomes in HF patients, including mortality, hospitalization, and stroke. Although the optimal treatment for AF with HF remains unclear, catheter ablation (CA) has emerged as a promising treatment option. This review provides a comprehensive overview of the current scientific evidence regarding the efficacy of CA for managing AF in HF patients. In addition, the potential benefits and risks associated with CA are also discussed. We will also explore the factors that may influence treatment outcomes and highlight the remaining gaps in knowledge in this field.
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Affiliation(s)
- Sho Suzuki
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Osaka 564-8565, Japan
- Department of Cardiovascular Medicine, Shinshu University School of Medicine, Nagano 390-8621, Japan
| | - Takeshi Kitai
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Osaka 564-8565, Japan
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, Biopolis, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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Uslu F. GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images. Comput Med Imaging Graph 2023; 108:102266. [PMID: 37385047 DOI: 10.1016/j.compmedimag.2023.102266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/04/2023] [Accepted: 06/11/2023] [Indexed: 07/01/2023]
Abstract
Atrial Fibrillation (AF) is a disease where the atria fail to properly contract but quiver instead, due to the abnormal electrical activity of the atrial tissue. In AF patients, anatomical and functional parameters of the left atrium (LA) largely differ from that of healthy people due to LA remodelling, which can continue in many cases after the catheter ablation treatment. Therefore, it is important to follow up with AF patients to detect any recurrence. LA segmentation masks obtained from short-axis CINE MRI images are used as the gold standard for the quantification of LA parameters. Thick slices of CINE MRI images hinder the use of 3D networks for segmentation while 2D architectures often fail to model inter-slice dependencies. This study presents GSM-Net which approximates 3D networks with effective modelling of inter-slice similarities with two new modules: global slice sequence encoder (GSSE) and sequence dependent channel attention module (SdCAt). In contrast to previous work modelling only local inter-slice similarities, GSSE also models global spatial dependencies across slices. SdCAt generates a distribution of attention weights over MRI slices per channel, to better trace characteristic changes in the size of the LA or other structures across slices. We found that GSM-Net outperforms previous methods on LA segmentation and helps to identify AF recurrence patients. We believe that GSM-Net can be used as an automatic tool to estimate LA parameters such as ejection fraction to identify AF, and to follow up with patients after treatment to detect any recurrence.
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Affiliation(s)
- Fatmatülzehra Uslu
- Bursa Technical University, Electrical and Electronics Engineering Department, Bursa, 16310, Türkiye.
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7
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Corrado C, Roney CH, Razeghi O, Lemus JAS, Coveney S, Sim I, Williams SE, O'Neill MD, Wilkinson RD, Clayton RH, Niederer SA. Quantifying the impact of shape uncertainty on predicted arrhythmias. Comput Biol Med 2023; 153:106528. [PMID: 36634600 DOI: 10.1016/j.compbiomed.2022.106528] [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: 09/09/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Caroline H Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; UCL Centre for Advanced Research Computing, London, United Kingdom
| | - Josè Alonso Solís Lemus
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Steven E Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark D O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard D Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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Liu Y, Bao S, Englot DJ, Morgan VL, Taylor WD, Wei Y, Oguz I, Landman BA, Lyu I. Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection. Comput Biol Med 2023; 152:106414. [PMID: 36525831 PMCID: PMC9832438 DOI: 10.1016/j.compbiomed.2022.106414] [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: 06/13/2022] [Revised: 11/18/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended. METHODS In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update. RESULTS We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection. CONCLUSION The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.
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Affiliation(s)
- Yue Liu
- College of Information Science and Engineering, Northeastern University, Shenyang, China; Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Shunxing Bao
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, TN, USA
| | - Victoria L Morgan
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, TN, USA
| | - Warren D Taylor
- Department of Psychiatry & Behavioral Science, Vanderbilt University Medical Center, TN, USA
| | - Ying Wei
- College of Information Science and Engineering, Northeastern University, Shenyang, China; Information Technology R&D Innovation Center of Peking University, Shaoxing, China; Changsha Hisense Intelligent System Research Institute Co., Ltd, China
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, UNIST, Ulsan, South Korea.
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Khan N, Peterson AC, Aubert B, Morris A, Atkins PR, Lenz AL, Anderson AE, Elhabian SY. Statistical multi-level shape models for scalable modeling of multi-organ anatomies. Front Bioeng Biotechnol 2023; 11:1089113. [PMID: 36873362 PMCID: PMC9978224 DOI: 10.3389/fbioe.2023.1089113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/06/2023] [Indexed: 02/18/2023] Open
Abstract
Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables the learning of population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM optimizes the placement of a dense set of landmarks (i.e., correspondence points) on a given shape cohort. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics. This paper leverages the PSM approach and proposes a new approach for correspondence-point optimization of multiple organs that overcomes these limitations. The central idea of multilevel component analysis, is that the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace. We formulate the correspondence optimization objective using this generative model. We evaluate the proposed method using synthetic shape data and clinical data for articulated joint structures of the spine, foot and ankle, and hip joint.
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Affiliation(s)
- Nawazish Khan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- School of Computing, University of Utah, Salt Lake City, UT, United States
- *Correspondence: Nawazish Khan ,
| | - Andrew C. Peterson
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | | | - Alan Morris
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Penny R. Atkins
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Amy L. Lenz
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Andrew E. Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Shireen Y. Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- School of Computing, University of Utah, Salt Lake City, UT, United States
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Machine learning in the detection and management of atrial fibrillation. Clin Res Cardiol 2022; 111:1010-1017. [PMID: 35353207 PMCID: PMC9424134 DOI: 10.1007/s00392-022-02012-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/16/2022] [Indexed: 12/04/2022]
Abstract
Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls.
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11
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Goparaju A, Iyer K, Bône A, Hu N, Henninger HB, Anderson AE, Durrleman S, Jacxsens M, Morris A, Csecs I, Marrouche N, Elhabian SY. Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Med Image Anal 2022; 76:102271. [PMID: 34974213 PMCID: PMC8792348 DOI: 10.1016/j.media.2021.102271] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 09/30/2021] [Accepted: 10/15/2021] [Indexed: 02/06/2023]
Abstract
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.
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Affiliation(s)
- Anupama Goparaju
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Krithika Iyer
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Alexandre Bône
- ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France
| | - Nan Hu
- Robert Stempel School of Public Health and Social Work, Florida International University, Miami, FL, USA
| | - Heath B Henninger
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew E Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stanley Durrleman
- ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France
| | - Matthijs Jacxsens
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Alan Morris
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ibolya Csecs
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Nassir Marrouche
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Shireen Y Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA.
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12
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Jia S, Nivet H, Harrison J, Pennec X, Camaioni C, Jaïs P, Cochet H, Sermesant M. Left atrial shape is independent predictor of arrhythmia recurrence after catheter ablation for atrial fibrillation: A shape statistics study. Heart Rhythm O2 2022; 2:622-632. [PMID: 34988507 PMCID: PMC8703187 DOI: 10.1016/j.hroo.2021.10.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Markers of left atrial (LA) shape may improve the prediction of postablation outcomes in atrial fibrillation (AF). Correlations to LA volume and AF persistence limit their incremental value over current clinical predictors. Objective To develop a shape score independent from AF persistence and LA volume using shape-based statistics, and to test its ability to predict postablation outcome. Methods Preablation computed tomography (CT) images from 141 patients with paroxysmal (57%) or persistent (43%) AF were segmented. Deformation of an average LA shape into each patient encoded patient-specific shape. Local analysis investigates regional differences between patient groups. Linear regression was used to remove shape variations related to LA volume and AF persistence, and to build a shape score to predict postablation outcome. Cross-validation was performed to evaluate its accuracy. Results Ablation failure rate was 23% over a median 12-month follow-up. Regions associated with ablation failure mostly consisted of a large area on posteroinferior LA, mitral isthmus, and left inferior vein. On univariate analysis, strongest predictors were AF persistence (P = .005), LA indexed volume (P = .02), and the proposed shape score (P = .001). On multivariate analysis, all 3 were independent predictors of ablation failure, with the LA shape score showing the highest predictive value (odds ratio [OR] = 6.2 [2.5–15.8], P < .001), followed by LA indexed volume (OR = 3.1 [1.2–7.9], P = .019) and AF persistence (OR = 2.9 [1.2–7.6], P = .022). Conclusion Posteroinferior LA, mitral isthmus, and left inferior vein are the regions whose shape have the highest impact on outcome. LA shape predicts AF ablation failure independently from, and more accurately than, atrial volume and AF persistence.
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Affiliation(s)
- Shuman Jia
- Team Epione, Inria Sophia Antipolis, Sophia Antipolis, France.,IHU Liryc, Pessac, France
| | - Hubert Nivet
- CHU de Bordeaux, Hôpital Haut-Lévêque, Pessac, France
| | | | - Xavier Pennec
- Team Epione, Inria Sophia Antipolis, Sophia Antipolis, France
| | | | - Pierre Jaïs
- CHU de Bordeaux, Hôpital Haut-Lévêque, Pessac, France.,IHU Liryc, Pessac, France
| | - Hubert Cochet
- CHU de Bordeaux, Hôpital Haut-Lévêque, Pessac, France.,IHU Liryc, Pessac, France
| | - Maxime Sermesant
- Team Epione, Inria Sophia Antipolis, Sophia Antipolis, France.,IHU Liryc, Pessac, France
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13
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Roney CH, Sillett C, Whitaker J, Lemus JAS, Sim I, Kotadia I, O'Neill M, Williams SE, Niederer SA. Applications of multimodality imaging for left atrial catheter ablation. Eur Heart J Cardiovasc Imaging 2021; 23:31-41. [PMID: 34747450 PMCID: PMC8685603 DOI: 10.1093/ehjci/jeab205] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Atrial arrhythmias, including atrial fibrillation and atrial flutter, may be treated through catheter ablation. The process of atrial arrhythmia catheter ablation, which includes patient selection, pre-procedural planning, intra-procedural guidance, and post-procedural assessment, is typically characterized by the use of several imaging modalities to sequentially inform key clinical decisions. Increasingly, advanced imaging modalities are processed via specialized image analysis techniques and combined with intra-procedural electrical measurements to inform treatment approaches. Here, we review the use of multimodality imaging for left atrial ablation procedures. The article first outlines how imaging modalities are routinely used in the peri-ablation period. We then describe how advanced imaging techniques may inform patient selection for ablation and ablation targets themselves. Ongoing research directions for improving catheter ablation outcomes by using imaging combined with advanced analyses for personalization of ablation targets are discussed, together with approaches for their integration in the standard clinical environment. Finally, we describe future research areas with the potential to improve catheter ablation outcomes.
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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Charles Sillett
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | | | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Steven E Williams
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
- Centre for Cardiovascular Science, The University of Edinburgh, Scotland, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
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14
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Labarbera MA, Atta-Fosu T, Feeny AK, Firouznia M, Mchale M, Cantlay C, Roach T, Axtell A, Schoenhagen P, Barnard J, Smith JD, Van Wagoner DR, Madabhushi A, Chung MK. New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 10:1800209. [PMID: 34976444 PMCID: PMC8716081 DOI: 10.1109/jtehm.2021.3134160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/08/2021] [Accepted: 11/22/2021] [Indexed: 11/30/2022]
Abstract
Objective: To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Methods: Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014-2016 were retrospectively identified. A training dataset (D1) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year. The top-performing combination of feature selection and classifier methods based on C-statistic was evaluated on a validation dataset (D2), composed of subjects retrospectively identified between 2005-2010. Clinical models ([Formula: see text]) were similarly evaluated and compared to radiomic ([Formula: see text]) and radiomic-clinical models ([Formula: see text]), each independently validated on D2. Results: Of 150 subjects in D1, 108 received radiofrequency ablation and 42 received cryoballoon. Radiomic features of recurrence included greater right carina angle, reduced anterior-posterior atrial diameter, greater atrial volume normalized to height, and steeper right inferior pulmonary vein angle. Clinical features predicting recurrence included older age, greater BMI, hypertension, and warfarin use; apixaban use was associated with reduced recurrence. AF recurrence was predicted with radio-frequency ablation models on D2 subjects with C-statistics of 0.68, 0.63, and 0.70 for radiomic, clinical, and combined feature models, though these were not prognostic in patients treated with cryoballoon. Conclusions: Pulmonary vein morphology associated with increased likelihood of AF recurrence within 1 year of catheter ablation was identified on cardiac CT. Significance: Radiomic and clinical features-based predictive models may assist in identifying atrial fibrillation ablation candidates with greatest likelihood of successful outcome.
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Affiliation(s)
- Michael A. Labarbera
- Cleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOH44106USA
| | - Thomas Atta-Fosu
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
| | - Albert K. Feeny
- Cleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOH44106USA
| | - Marjan Firouznia
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
| | - Meghan Mchale
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Catherine Cantlay
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Tyler Roach
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Alexis Axtell
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Paul Schoenhagen
- Department of Cardiovascular Medicine, Heart, VascularThoracic Institute, Cleveland ClinicClevelandOH44106USA
| | - John Barnard
- Department of Quantitative Health SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Jonathan D. Smith
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - David R. Van Wagoner
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
| | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
- Louis Stokes Cleveland Veterans Administration Medical CenterClevelandOH44106USA
| | - Mina K. Chung
- Department of Cardiovascular and Metabolic SciencesLerner Research Institute, Cleveland ClinicClevelandOH44106USA
- Department of Cardiovascular Medicine, Heart, VascularThoracic Institute, Cleveland ClinicClevelandOH44106USA
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15
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Guo F, Li C, Yang L, Chen C, Chen Y, Ni J, Fu R, Jiao Y, Meng Y. Impact of left atrial geometric remodeling on late atrial fibrillation recurrence after catheter ablation. J Cardiovasc Med (Hagerstown) 2021; 22:909-916. [PMID: 34506349 DOI: 10.2459/jcm.0000000000001255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AIMS To quantitatively investigate the impact of left atrial geometric remodeling on atrial fibrillation recurrence after catheter ablation. METHODS A retrospective analysis of 105 patients with atrial fibrillation who underwent coronary computed tomographic angiography before catheter ablation. Risk factors for atrial fibrillation recurrence were identified by multivariable logistic regression analysis and used to create a nomogram. RESULTS After at least 12 months of follow-up, 30 patients (29%) developed recurrent atrial fibrillation. Patients with recurrence had higher left atrial volume, left atrial sphericity, and lower left atrial ejection fraction (LAEF) (P < 0.05). There was no significant difference in asymmetry index between the two groups (P = 0.121). Multivariable regression analysis showed that left atrial minimal volume index (LAVImin) [odds ratio (OR): 1.026, 95% confidence interval (CI): 1.002-1.050, P = 0.034], left atrial sphericity (OR: 1.222, 95% CI: 1.040-1.435, P = 0.015) and CHADS2 score (OR: 1.511, 95% CI: 1.024-2.229, P = 0.038) were independent predictors of atrial fibrillation recurrence. The combined model of the left atrial sphericity to the LAVImin substantially increased the predictive power for atrial fibrillation recurrence [area under the curve (AUC) = 0.736, 95% CI: 0.627-0.844, P < 0.001], with a sensitivity of 80% and a specificity of 61%. A nomogram was generated based on the contribution weights of the risk factors; the AUC was 0.772 (95% CI: 0.670-0.875) and had good internal validity. CONCLUSION The CHADS2 score, left atrial sphericity, and LAVImin were significant and independent predictors of atrial fibrillation recurrence after catheter ablation. Furthermore, the nomogram had a better predictive capacity for atrial fibrillation recurrence.
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Affiliation(s)
- Fuqian Guo
- Department of Medical Imaging, The Second Hospital, Hebei Medical University, Shijiazhuang, Hebei, China
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16
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Bhalodia R, Elhabian S, Kavan L, Whitaker R. Leveraging unsupervised image registration for discovery of landmark shape descriptor. Med Image Anal 2021; 73:102157. [PMID: 34293535 PMCID: PMC8489970 DOI: 10.1016/j.media.2021.102157] [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: 02/09/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 10/20/2022]
Abstract
In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.
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Affiliation(s)
- Riddhish Bhalodia
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA.
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA
| | - Ladislav Kavan
- School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA
| | - Ross Whitaker
- Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA
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17
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A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations. Med Image Anal 2021; 74:102210. [PMID: 34450467 DOI: 10.1016/j.media.2021.102210] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 06/29/2021] [Accepted: 08/04/2021] [Indexed: 11/20/2022]
Abstract
Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 24 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 109.7±12.2 ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. This novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches.
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18
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Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation. Int J Cardiovasc Imaging 2021; 37:2521-2527. [PMID: 33956285 DOI: 10.1007/s10554-021-02262-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/27/2021] [Indexed: 10/21/2022]
Abstract
The shape of the left atrium (LA) and left atrial appendage (LAA) have been shown to predict stroke in patients with atrial fibrillation (AF). Prior studies rely on qualitative assessment of shape, which limits reproducibility and clinical utility. Statistical shape analysis (SSA) allows for quantitative assessment of shape. We use this method to assess the shape of the LA and LAA and predict stroke in patients with AF. From a database of AF patients who had previously undergone MRI of the LA, we identified 43 patients with AF who subsequently had an ischemic stroke. We also identified a cohort of 201 controls with AF who did not have a stroke after the MRI. We performed SSA of the LA and LAA shape to quantify the shape of these structures. We found three of the candidate LAA shape parameters to be predictive of stroke, while none of the LA shape parameters predicted stroke. When the three predictive LAA shape parameters were added to a logistic regression model that included the CHA2DS2-VASc score, the area under the ROC curve increased from 0.640 to 0.778 (p = .003). The shape of the LA and LAA can be assessed quantitatively using SSA. LAA shape predicts stroke in AF patients, while LA shape does not. Additionally, LAA shape predicts stroke independent of CHA2DS2-VASc score. SSA for assessment of LAA shape may improve stroke risk stratification and clinical decision making for AF patients.
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19
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Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging 2021; 21:45. [PMID: 33750343 PMCID: PMC7941998 DOI: 10.1186/s12880-021-00578-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 02/28/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence. METHODS This retrospective study included 68 AF patients who had pre-catheter ablation cardiac CT scans with contrast. AF recurrence was defined at 1 year, excluding a 3-month post-ablation blanking period. After creating atlases of atrial models from segmented AF+ and AF- CT images, an atlas-based implicit shape differentiation method was used to identify surface of interest (SOI). After registering the SOI to each patient model, statistics of the deformation on the SOI were used to create shape descriptors. The performance in predicting AF recurrence using shape features at and outside the SOI and eight clinical factors (age, sex, left atrial volume, left ventricular ejection fraction, body mass index, sinus rhythm, and AF type [persistent vs paroxysmal], catheter-ablation type [Cryoablation vs Irrigated RF]) were compared using 100 runs of fivefold cross validation. RESULTS Differences in atrial shape were found surrounding the pulmonary vein ostia and the base of the left atrial appendage. In the prediction of AF recurrence, the area under the receiver-operating characteristics curve (AUC) was 0.67 for shape features from the SOI, 0.58 for shape features outside the SOI, 0.71 for the clinical parameters, and 0.78 combining shape and clinical features. CONCLUSION Differences in left atrial shape were identified between AF recurrent and non-recurrent patients using pre-procedure CT scans. New radiomic features corresponding to the differences in shape were found to predict post-ablation AF recurrence.
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Affiliation(s)
- Thomas Atta-Fosu
- Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, 2071 Martin Luther King Drive, Cleveland, OH, 44106-7207, USA.
| | - Michael LaBarbera
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Soumya Ghose
- Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, 2071 Martin Luther King Drive, Cleveland, OH, 44106-7207, USA
| | - Paul Schoenhagen
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Walid Saliba
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Patrick J Tchou
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Bruce D Lindsay
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Milind Y Desai
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Deborah Kwon
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.,Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.,Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, 2071 Martin Luther King Drive, Cleveland, OH, 44106-7207, USA.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
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20
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Firouznia M, Feeny AK, LaBarbera MA, McHale M, Cantlay C, Kalfas N, Schoenhagen P, Saliba W, Tchou P, Barnard J, Chung MK, Madabhushi A. Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation. Circ Arrhythm Electrophysiol 2021; 14:e009265. [PMID: 33576688 DOI: 10.1161/circep.120.009265] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Marjan Firouznia
- Department of Biomedical Engineering (M.F., A.M.), Case Western Reserve University
| | - Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University
| | - Michael A LaBarbera
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University
| | - Meghan McHale
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.).,Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Catherine Cantlay
- Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Natalie Kalfas
- Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Paul Schoenhagen
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University.,Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.).,Imaging Institute (P.S.), Diagnostic Radiology, Cleveland Clinic
| | - Walid Saliba
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - Patrick Tchou
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - John Barnard
- Quantitative Health Sciences, Lerner Research Institute (J.B.), Diagnostic Radiology, Cleveland Clinic
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University.,Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (M.F., A.M.), Case Western Reserve University.,Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic.,Louis Stokes Cleveland Veterans Administration Medical Center, OH (A.M.)
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21
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Abstract
AF is the most common arrhythmia in clinical practice. In addition to the severe effect on quality of life, patients with AF are at higher risk of stroke and mortality. Recent studies have suggested that atrial and ventricular substrate play a major role in the development and maintenance of AF. Cardiac MRI has emerged as a viable tool for interrogating the underlying substrate in AF patients. Its advantage includes localisation and quantification of structural remodelling. Cardiac MRI of the atrial substrate is not only a tool for management and treatment of arrhythmia, but also to individualise the prevention of stroke and major cardiovascular events. This article provides an overview of atrial imaging using cardiac MRI and its clinical implications in the AF population.
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Affiliation(s)
- Yan Zhao
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, LA, US
| | - Lilas Dagher
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, LA, US
| | - Chao Huang
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, LA, US
| | - Peter Miller
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, LA, US
| | - Nassir F Marrouche
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, LA, US
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22
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Bhalodia R, Kavan L, Whitaker RT. Self-Supervised Discovery of Anatomical Shape Landmarks. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12264:627-638. [PMID: 33778817 PMCID: PMC7993653 DOI: 10.1007/978-3-030-59719-1_61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.
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Affiliation(s)
- Riddhish Bhalodia
- Scientific Computing and Imaging Institute, University of Utah
- School of Computing, University of Utah
| | | | - Ross T Whitaker
- Scientific Computing and Imaging Institute, University of Utah
- School of Computing, University of Utah
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23
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Feeny AK, Chung MK, Madabhushi A, Attia ZI, Cikes M, Firouznia M, Friedman PA, Kalscheur MM, Kapa S, Narayan SM, Noseworthy PA, Passman RS, Perez MV, Peters NS, Piccini JP, Tarakji KG, Thomas SA, Trayanova NA, Turakhia MP, Wang PJ. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol 2020; 13:e007952. [PMID: 32628863 PMCID: PMC7808396 DOI: 10.1161/circep.119.007952] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
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Affiliation(s)
- Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH (A.M.)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine & University Hospital Center Zagreb, Croatia (M.C.)
| | - Marjan Firouznia
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Matthew M Kalscheur
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine & Public Health, University of Wisconsin (M.M.K.)
- William S. Middleton Veterans Hospital, Madison, WI (M.M.K.)
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Sanjiv M Narayan
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Rod S Passman
- Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.)
| | - Marco V Perez
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Nicholas S Peters
- National Heart Lung Institute & Centre for Cardiac Engineering, Imperial College London, United Kingdom (N.S.P.)
| | - Jonathan P Piccini
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.)
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Suma A Thomas
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD (N.A.T.)
| | - Mintu P Turakhia
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Center for Digital Health, Stanford University School of Medicine, CA (M.P.T.)
| | - Paul J Wang
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
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24
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Corrado C, Razeghi O, Roney C, Coveney S, Williams S, Sim I, O'Neill M, Wilkinson R, Oakley J, Clayton RH, Niederer S. Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal 2020; 61:101626. [PMID: 32000114 DOI: 10.1016/j.media.2019.101626] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/05/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Caroline Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard Wilkinson
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Jeremy Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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25
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Nedios S, Kircher S, Hindricks G. Cardiovascular magnetic resonance imaging for the detection of left atrial remodeling and the prediction of atrial fibrillation ablation success: More than meets the eye. Int J Cardiol 2020; 305:161-162. [PMID: 32005453 DOI: 10.1016/j.ijcard.2020.01.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 11/18/2022]
Affiliation(s)
- Sotirios Nedios
- Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Germany.
| | - Simon Kircher
- Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Germany
| | - Gerhard Hindricks
- Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Germany
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26
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Bhalodia R, Subramanian A, Morris A, Cates J, Whitaker R, Kholmovski E, Marrouche N, Elhabian S. Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction? COMPUTING IN CARDIOLOGY 2019; 46. [PMID: 32632370 DOI: 10.22489/cinc.2019.200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Evidence suggests that the shape of left atrium appendages (LAA) is a primary indicator in predicting stroke for patients diagnosed with atrial fibrillation (AF). Statistical shape modeling tools used to represent (i.e., parameterize) the underlying LAA variability are of crucial importance to learn shape-based predictors of stroke. Most shape modeling techniques use some form of alignment either as a data pre-processing step or during the modeling step. However, the LAA is a joint anatomy along with left atrium (LA), and the relative position and alignment plays a crucial part in determining risk of stroke. In this paper, we explore different alignment strategies for statistical shape modeling and how each strategy affects the stroke prediction capability. This allows for identifying a unified approach of alignment while analyzing the LAA anatomy for stroke. Here, we study three different alignment strategies, (i) global alignment, (ii) global translational alignment and (iii) cluster based alignment. Our results show that alignment strategies that take into account LAA orientation, i.e., (ii), or the inherent natural clustering of the population under study, i.e., (iii), provide significant improvement over global alignment in both qualitative as well as quantitative measures.
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Affiliation(s)
- Riddhish Bhalodia
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Archanasri Subramanian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Alan Morris
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Joshua Cates
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Ross Whitaker
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Evgueni Kholmovski
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA.,Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Nassir Marrouche
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
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27
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Bhalodia R, Elhabian SY, Kavan L, Whitaker RT. DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images. SHAPE IN MEDICAL IMAGING : INTERNATIONAL WORKSHOP, SHAPEMI 2018, HELD IN CONJUNCTION WITH MICCAI 2018, GRANADA, SPAIN, SEPTEMBER 20, 2018 : PROCEEDINGS. SHAPEMI (WORKSHOP) (2018 : GRANADA, SPAIN) 2018; 11167:244-257. [PMID: 30805572 DOI: 10.1007/978-3-030-04747-4_23] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users. We propose DeepSSM: a deep learning approach to extract a low-dimensional shape representation directly from 3D images, requiring virtually no parameter tuning or user assistance. DeepSSM uses a convolutional neural network (CNN) that simultaneously localizes the biological structure of interest, establishes correspondences, and projects these points onto a low-dimensional shape representation in the form of PCA loadings within a point distribution model. To overcome the challenge of the limited availability of training images with dense correspondences, we present a novel data augmentation procedure that uses existing correspondences on a relatively small set of processed images with shape statistics to create plausible training samples with known shape parameters. In this way, we leverage the limited CT/MRI scans (40-50) into thousands of images needed to train a deep neural net. After the training, the CNN automatically produces accurate low-dimensional shape representations for unseen images. We validate DeepSSM for three different applications pertaining to modeling pediatric cranial CT for characterization of metopic craniosynostosis, femur CT scans identifying morphologic deformities of the hip due to femoroacetabular impingement, and left atrium MRI scans for atrial fibrillation recurrence prediction.
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Affiliation(s)
- Riddhish Bhalodia
- Scientific Computing and Imaging Institute, University of Utah.,School of Computing, University of Utah
| | - Shireen Y Elhabian
- Scientific Computing and Imaging Institute, University of Utah.,School of Computing, University of Utah.,Comprehensive Arrhythmia Research and Management Center, University of Utah
| | | | - Ross T Whitaker
- Scientific Computing and Imaging Institute, University of Utah.,School of Computing, University of Utah.,Comprehensive Arrhythmia Research and Management Center, University of Utah
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