Machine learning–based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography
This study provides a noninvasive method for comprehensively and quantitatively evaluating pulmonary hemodynamics using CTPA images, which has the potential to serve as an alternative to RHC, pending further validation.
Authors
- Dongting Liu (ORCID: https://orcid.org/0000-0002-6103-0807)
- Guang Yang (ORCID: https://orcid.org/0000-0002-3250-5228)
- Xin Zhao (ORCID: https://orcid.org/0000-0002-1972-3669)
- Zhaoying Wen (ORCID: https://orcid.org/0000-0003-3015-1179)
- Senchun Chai (ORCID: https://orcid.org/0000-0003-1910-1795)
- Yunshan Cao (ORCID: https://orcid.org/0000-0001-8463-1093)
- Hongyan Xie (ORCID: https://orcid.org/0000-0003-1347-1015)
- Nan Zhang (ORCID: https://orcid.org/0000-0002-8309-4262)
- Jiayi Liu (ORCID: https://orcid.org/0000-0003-1819-2998)
- Zhonghua Sun
- Lei Xu
- Jialin Xu
Institutions
- Beijing Institute of Technology (CN)
- Chinese University of Hong Kong (HK)
- Capital Medical University (CN)
- Curtin University (AU)
- Beijing Anzhen Hospital (CN)
Publication Details
- Journal
- International Journal of Cardiology
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1016/j.ijcard.2025.133457
- Citations
- 2
- Primary Topic
- Pulmonary Hypertension Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Nvidia
- Boehringer Ingelheim
- UK Research and Innovation
- Engineering Research Centers
- Royal Society
- National Natural Science Foundation of China
- Science and Technology Development Fund
- Horizon 2020 Framework Programme
- Medical Research Council
- NIHR Imperial Biomedical Research Centre