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Ganesan P, Feng R, Deb B, Tjong FVY, Rogers AJ, Ruipérez-Campillo S, Somani S, Clopton P, Baykaner T, Rodrigo M, Zou J, Haddad F, Zaharia M, Narayan SM. Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset. Diagnostics (Basel) 2024; 14:1538. [PMID: 39061675 PMCID: PMC11276420 DOI: 10.3390/diagnostics14141538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/07/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts "anatomical knowledge" by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid-boundary distance of 1.16 mm (95% CI: -4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid-boundary distances of -0.27 mm (95% CI: -3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications.
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Affiliation(s)
- Prasanth Ganesan
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Ruibin Feng
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Brototo Deb
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Fleur V. Y. Tjong
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Albert J. Rogers
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Sulaiman Somani
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Paul Clopton
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Tina Baykaner
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Miguel Rodrigo
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- CoMMLab, Universitat de València, 46100 Valencia, Spain
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Francois Haddad
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Matei Zaharia
- Department of Computer Science, University of California Berkeley, Berkeley, CA 94720, USA
| | - Sanjiv M. Narayan
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
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Hussain S, Falanga M, Chiaravalloti A, Tomasi C, Corsi C. Patient-specific left atrium contraction quantification associated with atrial fibrillation: A region-based approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108138. [PMID: 38522329 DOI: 10.1016/j.cmpb.2024.108138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND AND OBJECTIVES Atrial fibrillation (AF) is a widespread cardiac arrhythmia that significantly impacts heart function. AF disrupts atrial mechanical contraction, leading to irregular, uncoordinated, and slow blood flow inside the atria which favors the formation of clots, primarily within the left atrium (LA). A standardized region-based analysis of the LA is missing, and there is not even any consensus about how to define the LA regions. In this study we propose an automatic approach for regionalizing the LA into segments to provide a comprehensive 3D region-based LA contraction assessment. LA global and regional contraction were quantified in control subjects and in AF patients to describe mechanical abnormalities associated with AF. METHODS The proposed automatic approach for LA regionalization was tested in thirteen control subjects and seventeen AF patients. After dividing LA into standard regions, we evaluated the global and regional mechanical function by measuring LA contraction parameters, such as regional volume, global and regional strains, regional wall motion and regional shortening fraction. RESULTS LA regionalization was successful in all study subjects. In the AF group compared with control subjects, results showed: a global impairment of LA contraction which appeared more pronounced along radial and circumferential direction; a regional impairment of radial strain which was more pronounced in septal, inferior, and lateral regions suggesting a greater reduction in mechanical efficiency in these regions in comparison to the posterior and anterior ones. CONCLUSION An automatic approach for LA regionalization was proposed. The regionalization method was proved to be robust with several LA anatomical variations and able to characterize contraction changes associated with AF.
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Affiliation(s)
| | | | | | - Corrado Tomasi
- Santa Maria delle Croci Hospital, AUSL della Romagna, Ravenna, Italy
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Firouznia M, Henningsson M, Carlhäll CJ. FK-means: automatic atrial fibrosis segmentation using fractal-guided K-means clustering with Voronoi-clipping feature extraction of anatomical structures. Interface Focus 2023; 13:20230033. [PMID: 38106915 PMCID: PMC10722213 DOI: 10.1098/rsfs.2023.0033] [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: 07/25/2023] [Accepted: 11/10/2023] [Indexed: 12/19/2023] Open
Abstract
Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation. However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE-MRI data and achieved a Dice score of 0.75, similar to the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which uses the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D UNet method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis.
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Affiliation(s)
- Marjan Firouznia
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Markus Henningsson
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Clinical Psychology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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4
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Feng R, Deb B, Ganesan P, Tjong FVY, Rogers AJ, Ruipérez-Campillo S, Somani S, Clopton P, Baykaner T, Rodrigo M, Zou J, Haddad F, Zahari M, Narayan SM. Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding. Front Cardiovasc Med 2023; 10:1189293. [PMID: 37849936 PMCID: PMC10577270 DOI: 10.3389/fcvm.2023.1189293] [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: 03/18/2023] [Accepted: 09/18/2023] [Indexed: 10/19/2023] Open
Abstract
Background Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.
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Affiliation(s)
- Ruibin Feng
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Prasanth Ganesan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Fleur V. Y. Tjong
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Albert J. Rogers
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
- Bioengineering Department, University of California, Berkeley, Berkeley, CA, United States
| | - Sulaiman Somani
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Tina Baykaner
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Miguel Rodrigo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
- CoMMLab, Universitat Politècnica de València, Valencia, Spain
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Francois Haddad
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Matei Zahari
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States
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5
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Lyu Y, Bennamoun M, Sharif N, Lip GYH, Dwivedi G. Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation. Life (Basel) 2023; 13:1870. [PMID: 37763273 PMCID: PMC10532509 DOI: 10.3390/life13091870] [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: 08/03/2023] [Revised: 08/19/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.
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Affiliation(s)
- Yiheng Lyu
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Naeha Sharif
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UK
- Liverpool John Moores University, Liverpool L3 5UX, UK
- Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Girish Dwivedi
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia
- Medical School, The University of Western Australia, Perth, WA 6009, Australia
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6
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Solís-Lemus JA, Baptiste T, Barrows R, Sillett C, Gharaviri A, Raffaele G, Razeghi O, Strocchi M, Sim I, Kotadia I, Bodagh N, O'Hare D, O'Neill M, Williams SE, Roney C, Niederer S. Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study. Comput Biol Med 2023; 162:107009. [PMID: 37301099 PMCID: PMC10790305 DOI: 10.1016/j.compbiomed.2023.107009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/11/2023] [Accepted: 05/03/2023] [Indexed: 06/12/2023]
Abstract
This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.
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Affiliation(s)
- José Alonso Solís-Lemus
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK.
| | - Tiffany Baptiste
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Rosie Barrows
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Charles Sillett
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Ali Gharaviri
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Giulia Raffaele
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; School of Medical Education, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Orod Razeghi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Department of Haematology, NHS Blood and Transplant Centre, University of Cambridge, Cambridge, UK
| | - Marina Strocchi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Iain Sim
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Irum Kotadia
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Neil Bodagh
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Daniel O'Hare
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Mark O'Neill
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Steven E Williams
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Caroline Roney
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Queen Mary University of London, Mile End Rd, Bethnal Green, London, E1 4NS, UK
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK
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Lallah PN, Laite C, Bangash AB, Chooah O, Jiang C. The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation. Rev Cardiovasc Med 2023; 24:215. [PMID: 39076714 PMCID: PMC11266764 DOI: 10.31083/j.rcm2408215] [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: 11/30/2022] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 07/31/2024] Open
Abstract
Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.
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Affiliation(s)
- Poojesh Nikhil Lallah
- Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, 310016 Hangzhou, Zhejiang, China
| | - Chen Laite
- Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, 310016 Hangzhou, Zhejiang, China
| | - Abdul Basit Bangash
- Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, 310016 Hangzhou, Zhejiang, China
| | - Outesh Chooah
- Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, 310016 Hangzhou, Zhejiang, China
| | - Chenyang Jiang
- Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, 310016 Hangzhou, Zhejiang, China
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Quintana RA, Dong T, Vajapey R, Reyaldeen R, Kwon DH, Harb S, Wang TKM, Klein AL. Intra- and Postprocedural Multimodality Imaging in Atrial Fibrillation. Circ Cardiovasc Imaging 2022; 15:e014804. [PMID: 36378776 DOI: 10.1161/circimaging.122.014804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Multi-modality imaging plays critical roles during and after procedures associated with atrial fibrillation. Transesophageal echocardiography is an invaluable tool for left atrial appendage occlusion during the procedure and at follow-up. Both cardiac computed tomography and cardiac magnetic resonance contribute to postprocedural evaluation of pulmonary vein isolation ablation. The present review is the second of a 2-part series where we discuss the roles of cardiac imaging in the evaluation and management of patients with atrial fibrillation, focusing on intraprocedural and postprocedural assessment, including the clinical evidence and outcomes data supporting this future applications.
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Affiliation(s)
- Raymundo A Quintana
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus (R.A.Q.)
| | - Tiffany Dong
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Ramya Vajapey
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Reza Reyaldeen
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Deborah H Kwon
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Serge Harb
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Tom Kai Ming Wang
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
| | - Allan L Klein
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH (T.D., R.V., R.R., D.H.K., S.H., T.K.M.W., A.L.K.)
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Campos FO, Neic A, Mendonca Costa C, Whitaker J, O'Neill M, Razavi R, Rinaldi CA, DanielScherr, Niederer SA, Plank G, Bishop MJ. An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med Image Anal 2022; 80:102483. [PMID: 35667328 PMCID: PMC10114098 DOI: 10.1016/j.media.2022.102483] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/22/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.
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Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | | | - Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - DanielScherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Austria
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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10
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Li MD, Cheng MQ, Chen LD, Hu HT, Zhang JC, Ruan SM, Huang H, Kuang M, Lu MD, Li W, Wang W. Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing. Eur Radiol 2022; 32:5843-5851. [PMID: 35314881 DOI: 10.1007/s00330-022-08662-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/20/2022] [Accepted: 02/13/2022] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing. MATERIALS AND METHODS A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms. Reproducible ultrasomics features were selected based on coefficients of variation. RESULTS A total of 108 US images from three scanners were obtained; 5253 ultrasomics features including seven categories of features were extracted and evaluated for each US image. From intra-US machine analysis, 37.0-38.8% of features showed good reproducibility. From inter-US machine analysis, 42.8% (2248/5253) of features exhibited good reproducibility. From segmentation location analysis, 55.7-57.6% of features showed good reproducibility. No significant difference in the normalized feature ranges was found between the 100 features extracted by the Ultrasomics and PyRadiomics platforms with the same algorithm (p = 0.563). A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed, most of which were wavelet and shearlet features. CONCLUSIONS Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. Wavelet and shearlet features showed the best reproducibility across all procedures. KEY POINTS • Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. • A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed. • Wavelet and shearlet features showed the best reproducibility across all procedures.
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Affiliation(s)
- Ming-De Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Mei-Qing Cheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Hang-Tong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Jian-Chao Zhang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Si-Min Ruan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Hui Huang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-De Lu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
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11
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Chen J, Zhang H, Mohiaddin R, Wong T, Firmin D, Keegan J, Yang G. Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:420-433. [PMID: 34534077 DOI: 10.1109/tmi.2021.3113678] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.
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12
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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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13
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Peters DC, Lamy J, Sinusas AJ, Baldassarre LA. Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers. Eur Heart J Cardiovasc Imaging 2021; 23:14-30. [PMID: 34718484 DOI: 10.1093/ehjci/jeab221] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022] Open
Abstract
Left atrial (LA) imaging is still not routinely used for diagnosis and risk stratification, although recent studies have emphasized its importance as an imaging biomarker. Cardiovascular magnetic resonance is able to evaluate LA structure and function, metrics that serve as early indicators of disease, and provide prognostic information, e.g. regarding diastolic dysfunction, and atrial fibrillation (AF). MR angiography defines atrial anatomy, useful for planning ablation procedures, and also for characterizing atrial shapes and sizes that might predict cardiovascular events, e.g. stroke. Long-axis cine images can be evaluated to define minimum, maximum, and pre-atrial contraction LA volumes, and ejection fractions (EFs). More modern feature tracking of these cine images provides longitudinal LA strain through the cardiac cycle, and strain rates. Strain may be a more sensitive marker than EF and can predict post-operative AF, AF recurrence after ablation, outcomes in hypertrophic cardiomyopathy, stratification of diastolic dysfunction, and strain correlates with atrial fibrosis. Using high-resolution late gadolinium enhancement (LGE), the extent of fibrosis in the LA can be estimated and post-ablation scar can be evaluated. The LA LGE method is widely available, its reproducibility is good, and validations with voltage-mapping exist, although further scan-rescan studies are needed, and consensus regarding atrial segmentation is lacking. Using LGE, scar patterns after ablation in AF subjects can be reproducibly defined. Evaluation of 'pre-existent' atrial fibrosis may have roles in predicting AF recurrence after ablation, predicting new-onset AF and diastolic dysfunction in patients without AF. LA imaging biomarkers are ready to enter into diagnostic clinical practice.
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Affiliation(s)
- Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Cardiology, Yale School of Medicine, New Haven, CT, USA
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