Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model
ABSTRACT: Background: Pulmonary hypertension (PH) is a heterogeneous condition and regardless of etiology impacts negatively on survival. Diagnosis of PH is based on hemodynamic parameters measured invasively at right heart catheterization (RHC); however, a non-invasive alternative would be clinica...
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Elsevier
2025-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664724011608 |
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| author | Li-Hsin Cheng Xiaowu Sun Charlie Elliot Robin Condliffe David G. Kiely Samer Alabed Andrew J. Swift Rob J. van der Geest David G Kiely Lisa Watson Iain Armstrong Catherine Billings Athanasios Charalampopoulos Robin Condliffe Charlie Elliot Abdul Hameed Neil Hamilton Judith Hurdman Allan Lawrie Robert A Lewis Smitha Rajaram Alex Rothman Andy J. Swift Steven Wood AA Roger Thompson Jim Wild |
| author_facet | Li-Hsin Cheng Xiaowu Sun Charlie Elliot Robin Condliffe David G. Kiely Samer Alabed Andrew J. Swift Rob J. van der Geest David G Kiely Lisa Watson Iain Armstrong Catherine Billings Athanasios Charalampopoulos Robin Condliffe Charlie Elliot Abdul Hameed Neil Hamilton Judith Hurdman Allan Lawrie Robert A Lewis Smitha Rajaram Alex Rothman Andy J. Swift Steven Wood AA Roger Thompson Jim Wild |
| author_sort | Li-Hsin Cheng |
| collection | DOAJ |
| description | ABSTRACT: Background: Pulmonary hypertension (PH) is a heterogeneous condition and regardless of etiology impacts negatively on survival. Diagnosis of PH is based on hemodynamic parameters measured invasively at right heart catheterization (RHC); however, a non-invasive alternative would be clinically valuable. Our aim was to estimate RHC parameters non-invasively from cardiac magnetic resonance (MR) data using deep learning models and to identify key contributing imaging features. Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from four different views as input to predict mean pulmonary artery pressure (mPAP). The model was trained and evaluated on 1646 examinations. The model’s attention weight and predictive performance associated with each frame, view, or phase were used to judge its importance. Additionally, the importance of each cardiac chamber was inferred by perturbing part of the input pixels. Results: The model achieved a Pearson correlation coefficient of 0.80 and R2 of 0.64 in predicting mPAP and identified the right ventricle region on short-axis view to be especially informative. Conclusion: Hemodynamic parameters can be estimated non-invasively with a CNN, using MR cine series from four views, revealing key contributing features at the same time. |
| format | Article |
| id | doaj-art-199c341d9c4a481eac0c849a7681bfc3 |
| institution | Kabale University |
| issn | 1097-6647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-199c341d9c4a481eac0c849a7681bfc32025-08-20T03:45:59ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472025-01-0127110113310.1016/j.jocmr.2024.101133Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning modelLi-Hsin Cheng0Xiaowu Sun1Charlie Elliot2Robin Condliffe3David G. Kiely4Samer Alabed5Andrew J. Swift6Rob J. van der Geest7David G Kiely8Lisa Watson9Iain Armstrong10Catherine Billings11Athanasios Charalampopoulos12Robin Condliffe13Charlie Elliot14Abdul Hameed15Neil Hamilton16Judith Hurdman17Allan Lawrie18Robert A Lewis19Smitha Rajaram20Alex Rothman21Andy J. Swift22Steven Wood23AA Roger Thompson24Jim Wild25Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical, Center, the NetherlandsDivision of Image Processing (LKEB), Department of Radiology, Leiden University Medical, Center, the NetherlandsSheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation, Trust, UKSheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation, Trust, UKSheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation, Trust, UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre, Sheffield, UKDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UKDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UKDivision of Image Processing (LKEB), Department of Radiology, Leiden University Medical, Center, the Netherlands; Corresponding author.Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321); University of Sheffield, Sheffield, UKSheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321); University of Sheffield, Sheffield, UKNational Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321); University of Sheffield, Sheffield, UKSheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield UK; National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321)National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321); University of Sheffield, Sheffield, UKNational Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (NIHR203321); University of Sheffield, Sheffield, UKABSTRACT: Background: Pulmonary hypertension (PH) is a heterogeneous condition and regardless of etiology impacts negatively on survival. Diagnosis of PH is based on hemodynamic parameters measured invasively at right heart catheterization (RHC); however, a non-invasive alternative would be clinically valuable. Our aim was to estimate RHC parameters non-invasively from cardiac magnetic resonance (MR) data using deep learning models and to identify key contributing imaging features. Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from four different views as input to predict mean pulmonary artery pressure (mPAP). The model was trained and evaluated on 1646 examinations. The model’s attention weight and predictive performance associated with each frame, view, or phase were used to judge its importance. Additionally, the importance of each cardiac chamber was inferred by perturbing part of the input pixels. Results: The model achieved a Pearson correlation coefficient of 0.80 and R2 of 0.64 in predicting mPAP and identified the right ventricle region on short-axis view to be especially informative. Conclusion: Hemodynamic parameters can be estimated non-invasively with a CNN, using MR cine series from four views, revealing key contributing features at the same time.http://www.sciencedirect.com/science/article/pii/S1097664724011608Mean pulmonary artery pressurePulmonary hypertensionMulti-view cardiac MRDeep learningExplainable AI |
| spellingShingle | Li-Hsin Cheng Xiaowu Sun Charlie Elliot Robin Condliffe David G. Kiely Samer Alabed Andrew J. Swift Rob J. van der Geest David G Kiely Lisa Watson Iain Armstrong Catherine Billings Athanasios Charalampopoulos Robin Condliffe Charlie Elliot Abdul Hameed Neil Hamilton Judith Hurdman Allan Lawrie Robert A Lewis Smitha Rajaram Alex Rothman Andy J. Swift Steven Wood AA Roger Thompson Jim Wild Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model Journal of Cardiovascular Magnetic Resonance Mean pulmonary artery pressure Pulmonary hypertension Multi-view cardiac MR Deep learning Explainable AI |
| title | Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model |
| title_full | Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model |
| title_fullStr | Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model |
| title_full_unstemmed | Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model |
| title_short | Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model |
| title_sort | mean pulmonary artery pressure prediction with explainable multi view cardiovascular magnetic resonance cine series deep learning model |
| topic | Mean pulmonary artery pressure Pulmonary hypertension Multi-view cardiac MR Deep learning Explainable AI |
| url | http://www.sciencedirect.com/science/article/pii/S1097664724011608 |
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