Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
468-03-004 ISMRM Abstract

AutoPAP: Automated prediction of pulmonary artery pressure from cardiac MRI in pulmonary hypertension with deep learning.

Accepted
Ruaraidh Campbell 1, Tina Yao2,3, Anirudh Raman2, Mark Wrobel2,3, Catherine Beattie2, Charo Bruce2, Niromila Nadarajan2, Ruta Virsinskaite4, Dan Knight4, Jennifer Steeden2,3,5, Vivek Muthurangu2,3
1Institute of Cardiovascular Science, University College London, London, United Kingdom
2University College London, London, United Kingdom
3Centre for Translational Cardiovascular Imaging, University College London, London, United Kingdom
4Royal Free Hospital, London, United Kingdom
5Center for Cardiovascular Imaging, United Kingdom
Presenting Author: Ruaraidh Campbell

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: Developed by the task force for the diagnosis and treatment of pulmonary hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS). Endorsed by the International Society for Heart and Lung Transplantation (ISHLT) and the European Reference Network on rare respiratory diseases (ERN-LUNG). Eur Heart J [Internet]. 2022 Oct 7;43(38):3618–731. Available from: https://doi.org/10.1093/eurheartj/ehac237 [doi]
2. Rosenkranz S, Preston IR. Right heart catheterisation: best practice and pitfalls in pulmonary hypertension. Eur Respir Rev [Internet]. 24(138):642–52. Available from: http://err.ersjournals.com/content/errev/24/138/642.abstract
3. Vos JL, Leiner T, van Dijk APJ, Pedrizzetti G, Alenezi F, Rodwell L, et al. Cardiovascular magnetic resonance-derived left ventricular intraventricular pressure gradients among patients with precapillary pulmonary hypertension. Eur Hear J - Cardiovasc Imaging [Internet]. 2023 Jan 1;24(1):78–87. Available from: https://doi.org/10.1093/ehjci/jeab294 [doi]
4. Cheng L-H, Sun X, Elliot C, Condliffe R, Kiely DG, Alabed S, et al. Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model. J Cardiovasc Magn Reson [Internet]. 2025;27(1):101133. Available from: https://www.sciencedirect.com/science/article/pii/S1097664724011608
5. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Trans Med Imaging [Internet]. 2018 Nov;37(11):2514–25. Available from: https://ieeexplore.ieee.org/document/8360453/
6. Campello VM, Gkontra P, Izquierdo C, Martin-Isla C, Sojoudi A, Full PM, et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE Trans Med Imaging [Internet]. 2021 Dec;40(12):3543–54. Available from: https://ieeexplore.ieee.org/document/9458279/
7. Martín-Isla C, Campello VM, Izquierdo C, Kushibar K, Sendra-Balcells C, Gkontra P, et al. Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge. IEEE J Biomed Heal Informatics [Internet]. 2023 Jul;27(7):3302–13. Available from: https://ieeexplore.ieee.org/document/10103611/
8. Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, et al. UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [Internet]. 2020. Available from: https://arxiv.org/abs/2004.08790

Cite this abstract