{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T02:38:53Z","timestamp":1782873533632,"version":"3.54.5"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["RCYX20231211090127030"],"award-info":[{"award-number":["RCYX20231211090127030"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372441"],"award-info":[{"award-number":["62372441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2034"],"award-info":[{"award-number":["U22A2034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010857","name":"Jiangxi Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["20243BBI91022"],"award-info":[{"award-number":["20243BBI91022"]}],"id":[{"id":"10.13039\/501100010857","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013058","name":"Jiangsu Provincial Key Research and Development Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020084","name":"Guangzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["2024B03J0947"],"award-info":[{"award-number":["2024B03J0947"]}],"id":[{"id":"10.13039\/501100020084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023A1515030268"],"award-info":[{"award-number":["2023A1515030268"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.compmedimag.2025.102564","type":"journal-article","created":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T01:41:29Z","timestamp":1746927689000},"page":"102564","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Bi-VesTreeFormer: A bidirectional topology-aware transformer framework for coronary vFFR estimation"],"prefix":"10.1016","volume":"123","author":[{"given":"Congyu","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongzhi","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyun","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3289-9714","authenticated-orcid":false,"given":"Weixin","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102564_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2023.113360","article-title":"Exact solutions and conservation laws of a one-dimensional PDE model for a blood vessel","volume":"170","author":"Anco","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.compmedimag.2025.102564_b2","series-title":"International Conference on Machine Learning","first-page":"3469","article-title":"Structure-aware transformer for graph representation learning","author":"Chen","year":"2022"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102564_b3","article-title":"Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography\u2013based fractional flow reserve: result from the MACHINE consortium","volume":"11","author":"Coenen","year":"2018","journal-title":"Circ.: Cardiovasc. Imaging"},{"issue":"9","key":"10.1016\/j.compmedimag.2025.102564_b4","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D slicer as an image computing platform for the quantitative imaging network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102564_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102744","article-title":"Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance","volume":"147","author":"Feng","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.compmedimag.2025.102564_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2021.113892","article-title":"Machine learning augmented reduced-order models for FFR-prediction","volume":"384","author":"Fossan","year":"2021","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.compmedimag.2025.102564_b7","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neunet.2019.11.017","article-title":"Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging","volume":"123","author":"Gao","year":"2020","journal-title":"Neural Netw."},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102564_b8","first-page":"865","article-title":"Virtual coronary intervention: a treatment planning tool based upon the angiogram","volume":"12","author":"Gosling","year":"2019","journal-title":"JACC: Cardiovasc. Imaging"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102564_b9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1152\/japplphysiol.00752.2015","article-title":"A machine-learning approach for computation of fractional flow reserve from coronary computed tomography","volume":"121","author":"Itu","year":"2016","journal-title":"J. Appl. Physiol."},{"issue":"25","key":"10.1016\/j.compmedimag.2025.102564_b10","doi-asserted-by":"crossref","first-page":"745","DOI":"10.21105\/joss.00745","article-title":"The vascular modeling toolkit: a python library for the analysis of tubular structures in medical images","volume":"3","author":"Izzo","year":"2018","journal-title":"J. Open Source Softw."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102564_b11","doi-asserted-by":"crossref","DOI":"10.1063\/5.0152499","article-title":"One-dimensional modeling of microvascular hemodynamics in the retina using multimodal imaging","volume":"35","author":"Julien","year":"2023","journal-title":"Phys. Fluids"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102564_b12","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"10.1016\/j.compmedimag.2025.102564_b13","series-title":"APS Division of Fluid Dynamics Meeting Abstracts","first-page":"A31","article-title":"Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning","author":"Kissas","year":"2019"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102564_b14","first-page":"663","article-title":"Noninvasive CT-derived FFR based on structural and fluid analysis: a comparison with invasive FFR for detection of functionally significant stenosis","volume":"10","author":"Ko","year":"2017","journal-title":"JACC: Cardiovasc. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102564_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.107967","article-title":"A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model","volume":"169","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102564_b16","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41569-023-00880-4","article-title":"Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a consensus statement from the quantitative cardiovascular imaging study group","volume":"20","author":"M\u00e9zquita","year":"2023","journal-title":"Nat. Rev. Cardiol."},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102564_b17","first-page":"434","article-title":"Fast virtual fractional flow reserve based upon steady-state computational fluid dynamics analysis: results from the VIRTU-fast study","volume":"2","author":"Morris","year":"2017","journal-title":"Basic Transl. Sci."},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102564_b18","first-page":"1009","article-title":"\u201cVirtual\u201d(computed) fractional flow reserve: current challenges and limitations","volume":"8","author":"Morris","year":"2015","journal-title":"JACC: Cardiovasc. Interv."},{"key":"10.1016\/j.compmedimag.2025.102564_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108415","article-title":"An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions","volume":"257","author":"Nannini","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compmedimag.2025.102564_b20","doi-asserted-by":"crossref","unstructured":"Peng, Han, Li, Ge, Zhao, Yunfei, Jin, Zhi, 2022. Rethinking positional encoding in tree transformer for code representation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 3204\u20133214.","DOI":"10.18653\/v1\/2022.emnlp-main.210"},{"key":"10.1016\/j.compmedimag.2025.102564_b21","doi-asserted-by":"crossref","unstructured":"Qi, Charles R., Su, Hao, Mo, Kaichun, Guibas, Leonidas J., 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 652\u2013660.","DOI":"10.1109\/CVPR.2017.16"},{"issue":"25","key":"10.1016\/j.compmedimag.2025.102564_b22","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.compmedimag.2025.102564_b23","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"781","article-title":"CenterlinePointNet++: A new point cloud based architecture for coronary artery pressure drop and vFFR estimation","author":"Rygiel","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102564_b24","article-title":"Novel positional encodings to enable tree-based transformers","volume":"32","author":"Shiv","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102564_b25","doi-asserted-by":"crossref","first-page":"4044","DOI":"10.1109\/JBHI.2022.3169425","article-title":"Automatic coronary artery segmentation of CCTA images with an efficient feature-fusion-and-rectification 3D-UNet","volume":"26","author":"Song","year":"2022","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"23","key":"10.1016\/j.compmedimag.2025.102564_b26","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1001\/jama.2020.22708","article-title":"Association between adherence to fractional flow reserve treatment thresholds and major adverse cardiac events in patients with coronary artery disease","volume":"324","author":"Sud","year":"2020","journal-title":"Jama"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102564_b27","doi-asserted-by":"crossref","first-page":"197","DOI":"10.3390\/fluids7060197","article-title":"Deep learning for computational hemodynamics: A brief review of recent advances","volume":"7","author":"Taebi","year":"2022","journal-title":"Fluids"},{"key":"10.1016\/j.compmedimag.2025.102564_b28","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2025.102564_b29","series-title":"Tree transformer: Integrating tree structures into self-attention","author":"Wang","year":"2019"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102564_b30","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s42492-024-00157-8","article-title":"PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography","volume":"7","author":"Wang","year":"2024","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102564_b31","first-page":"42","article-title":"Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography","volume":"16","author":"Wang","year":"2019","journal-title":"J. Geriatr. Cardiol.: JGC"},{"key":"10.1016\/j.compmedimag.2025.102564_b32","first-page":"13266","article-title":"Representing long-range context for graph neural networks with global attention","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102564_b33","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1056\/NEJMoa1803538","article-title":"Five-year outcomes with PCI guided by fractional flow reserve","volume":"379","author":"Xaplanteris","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compmedimag.2025.102564_b34","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"110","article-title":"Conditional physics-informed graph neural network for fractional flow reserve assessment","author":"Xie","year":"2023"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102564_b35","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2478\/jtim-2022-0018","article-title":"Functional evaluation of intermediate coronary lesions with integrated computed tomography angiography and invasive angiography in patients with stable coronary artery disease","volume":"10","author":"Xue","year":"2022","journal-title":"J. Transl. Intern. Med."},{"key":"10.1016\/j.compmedimag.2025.102564_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2024.117338","article-title":"Attention-based multi-fidelity machine learning model for fractional flow reserve assessment","volume":"432","author":"Yang","year":"2024","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.compmedimag.2025.102564_b37","series-title":"MICCAI Workshop on Imaging Systems for GI Endoscopy","first-page":"108","article-title":"TaG-Net: Topology-aware graph network for vessel labeling","author":"Yao","year":"2022"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102564_b38","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/JBHI.2021.3116764","article-title":"Deep learning based centerline-aggregated aortic hemodynamics: An efficient alternative to numerical modeling of hemodynamics","volume":"26","author":"Yevtushenko","year":"2021","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102564_b39","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0159848","article-title":"An improved FFR design with a ventilation fan: CFD simulation and validation","volume":"11","author":"Zhang","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.compmedimag.2025.102564_b40","article-title":"Constraint-aware learning for fractional flow reserve pullback curve estimation from invasive coronary imaging","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102564_b41","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1109\/TBME.2020.2970244","article-title":"Personalized hemodynamic modeling of the human cardiovascular system: a reduced-order computing model","volume":"67","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000734?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000734?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:41:36Z","timestamp":1778762496000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611125000734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":41,"alternative-id":["S0895611125000734"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102564","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Bi-VesTreeFormer: A bidirectional topology-aware transformer framework for coronary vFFR estimation","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102564","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102564"}}