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Sharp AJ, Betts TR, Banerjee A. Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review. J Clin Med 2024; 13:4442. [PMID: 39124709 PMCID: PMC11313299 DOI: 10.3390/jcm13154442] [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: 06/28/2024] [Revised: 07/19/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals. However, traditional diagnostic approaches often overlook the complex spatial dynamics of the atria. This review explores the emerging role of three-dimensional (3D) atrial geometry in the evaluation and management of AF. Advancements in imaging technologies and computational modeling have enabled detailed reconstructions of atrial anatomy, providing insights into the pathophysiology of AF that were previously unattainable. We examine current methodologies for interpreting 3D atrial data, including qualitative, basic quantitative, global quantitative, and statistical shape modeling approaches. We discuss their integration into clinical practice, highlighting potential benefits such as personalized treatment strategies, improved outcome prediction, and informed treatment approaches. Additionally, we discuss the challenges and limitations associated with current approaches, including technical constraints and variable interpretations, and propose future directions for research and clinical applications. This comprehensive review underscores the transformative potential of leveraging 3D atrial geometry in the evaluation and management of AF, advocating for its broader adoption in clinical practice.
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Affiliation(s)
- Alexander J. Sharp
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Timothy R. Betts
- Cardiology Department, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
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Backhaus SJ, Nasopoulou A, Lange T, Schulz A, Evertz R, Kowallick JT, Hasenfuß G, Lamata P, Schuster A. Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction. Circ Cardiovasc Imaging 2024; 17:e016424. [PMID: 39012942 PMCID: PMC11251503 DOI: 10.1161/circimaging.123.016424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/29/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. METHODS Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. RESULTS Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). CONCLUSIONS LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. REGISTRATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621.
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Affiliation(s)
- Sören J. Backhaus
- Department of Cardiology, Campus Kerckhoff of the Justus-Liebig-University Giessen, Kerckhoff-Clinic, Bad Nauheim, Germany (S.J.B.)
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Bad Nauheim, Germany (S.J.B.)
| | - Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom (A.N., P.L.)
| | - Torben Lange
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Alexander Schulz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Ruben Evertz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Johannes T. Kowallick
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
- FORUM Radiology, Rosdorf, Germany (J.T.K.)
| | - Gerd Hasenfuß
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom (A.N., P.L.)
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
- FORUM Cardiology, Rosdorf, Germany (A. Schuster)
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (A. Schuster)
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [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: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [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: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Xu H, Williams SE, Williams MC, Newby DE, Taylor J, Neji R, Kunze KP, Niederer SA, Young AA. Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views. Eur Heart J Cardiovasc Imaging 2023; 24:607-615. [PMID: 36725705 PMCID: PMC10125223 DOI: 10.1093/ehjci/jead010] [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: 10/26/2022] [Revised: 12/16/2022] [Accepted: 01/09/2023] [Indexed: 02/03/2023] Open
Abstract
AIMS Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views. METHODS AND RESULTS A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n = 20). For the 200 test cases generated from CCTA, the network achieved a mean Dice score value of 93.7%, showing excellent 3D shape reconstruction from two views compared with the 3D segmentation Dice of 97.4%. The network also showed significantly lower mean absolute error values of 3.5 mL/4.9 cm2 for LA volume/surface area respectively compared to the area-length method errors of 13.0 mL/34.1 cm2 respectively (P < 0.05 for both). For the independent CMR test set, the network achieved accurate 3D shape estimation (mean Dice score value of 87.4%), and a mean absolute error values of 6.0 mL/5.7 cm2 for left atrial volume/surface area respectively, significantly less than the area-length method errors of 14.2 mL/19.3 cm2 respectively (P < 0.05 for both). CONCLUSIONS Compared to the bi-plane area-length method, the network showed higher accuracy and robustness for both volume and surface area.
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Affiliation(s)
- Hao Xu
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
| | - Steven E Williams
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Michelle C Williams
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - David E Newby
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Jonathan Taylor
- 3DLab, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, s5 7AU, UK
| | - Radhouene Neji
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
- MR Research Collaborations, Siemens Healthcare Limited, Newton House, Sir William Siemens Square, Frimley, Camberley, Surrey, GU16 8QD, UK
| | - Karl P Kunze
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
- MR Research Collaborations, Siemens Healthcare Limited, Newton House, Sir William Siemens Square, Frimley, Camberley, Surrey, GU16 8QD, UK
| | - Steven A Niederer
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
| | - Alistair A Young
- Department of Biomedical Engineering, King’s College London, Lambeth Palace Rd, London SE1 7EU, UK
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Lamata P. Unleashing the prognostic value of atrial shape in atrial fibrillation. Heart Rhythm O2 2021; 2:633-634. [PMID: 34988508 PMCID: PMC8703176 DOI: 10.1016/j.hroo.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
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