TY - JOUR
T1 - Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches
AU - Fedotova, Y. V.
AU - Epifanov, R. U.I.
AU - Karpenko, A. A.
AU - Mullyadzhanov, R. I.
N1 - Funding Information:
Acknowledgements. We thank N. Nikitin, I. Popova, L. Kurdyukov and E. Amelina for helping with the CT image preprocessing and D. Morozov for useful comments on the paper. The work is supported by the Russian Science Foundation grant No. 21-15-00091.
Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
PY - 2021/12/15
Y1 - 2021/12/15
N2 - Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.
AB - Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.
UR - http://www.scopus.com/inward/record.url?scp=85123591237&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2119/1/012069
DO - 10.1088/1742-6596/2119/1/012069
M3 - Conference article
AN - SCOPUS:85123591237
VL - 2119
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012069
T2 - 37th Siberian Thermophysical Seminar, STS 2021
Y2 - 14 September 2021 through 16 September 2021
ER -