Personalized Liver Cancer Radioembolization
Yttrium-90 (Y-90) radioembolization is being increasingly used for the treatment of advanced liver cancer. Accurate pretreatment dosimetry is necessary to determine the Y-90 activity to inject in order to maximize the dose to the tumor while limiting the dose to surrounding healthy parenchyma. Current dosimetry methods are not accurate nor precise, because they do not consider the non-uniform Y-90 microsphere distribution in the hepatic arterial tree as well as anatomy variations among the patients.
Develop personalized liver cancer radioembolization dosimetry, CFDose, based on blood flow simulation inside the hepatic arterial tree
Computational fluid dynamics modeling
Computational Modeling of the Liver Arterial Blood Flow For Microsphere Therapy:Effect of Boundary Conditions
In this study, the effect of boundary conditions on the hepatic arterial tree hemodynamics was investigated. The outlet boundary conditions were modeled with three-element Windkessel circuits, representative of the downstream vasculature resistance... Read more!
Multiscale Computational Fluid Dynamics Modeling For Personalized Liver Cancer Radioembolization Dosimetry
In this work, we present a computational model to predict the radiation dose from the Y-90 activity in different liver segments to provide a more realistic and personalized dosimetry. Computational fluid dynamics simulations were performed in a 3D hepatic arterial tree model segmented from cone-beam CT angiographic data obtained from a patient with hepatocellular carcinoma. The microsphere trajectories were predicted from the velocity field. 90Y dose distribution was then calculated from the volumetric distribution of the microspheres... Read more!
Blood flow simulations are sometimes very computationally expensive depending on the complexity of the computational domain and the available computing power. To accelerate CFDose, we introduce a deep learning model to predict the blood flow distribution between the liver segments in a patient with hepatocellular carcinoma.
Taebi, A., Berk, S., Roncali, E. (2021). Realistic boundary conditions in SimVascular through inlet catheter modeling. BMC Research Notes 14, 215.
Taebi, A., Janibek, N., Goldman, R., Pillai, R., Vu, C., Roncali, E. (2022) On the impact of injection distance to bifurcations on yttrium-90 distribution in liver cancer radioembolization, Journal of Vascular and Interventional Radiology.
Taebi, A., Vu, C.T., Roncali, E. (2020). Prediction of Blood Flow Distribution in Liver Radioembolization Using Convolutional Neural Networks. Presented in 2020 ASME IMECE, Portland, OR, V005T05A036.
Taebi, A., Vu, C.T., Roncali, E. (2020). Estimation of Yttrium-90 Distribution in Liver Radioembolization using Computational Fluid Dynamics and Deep Neural Networks. IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, QC, Canada, pp. 4974-4977.
Roncali, E.,Taebi, A., Roudsari, B.S., Vu, C.T. (2020). Personalized dosimetry for liver cancer radioembolization based on computational fluid dynamics. Annals of Biomedical Engineering, 48(5): 1499-1510.
Roncali, E., Taebi, A., Spencer, B., Costa, G.C.A., Rusnak, M., Caudle, D., Roudsari, B., Pillai, R., Foster, C., Vu, C. (2020). Comparison of Y-90 liver dose distribution predicted with fluid dynamics with Y-90 PET, Journal of Nuclear Medicine 61 (supplement 1) 1308.
Roncali, E., Taebi, A., Rusnak, M., Spencer, B., Caudle, D., Foster, C., Vu, C.T. (2019). Personalized dosimetry for liver cancer radioembolization using computational fluid dynamics.European Journal of Nuclear Medicine and Molecular Imaging46 (Suppl 1): S134.
Taebi, A., Roudsari, B.S., Vu, C., Cherry, S.R., Roncali, E. (2019). Hepatic arterial tree segmentation: Towards patient-specific dosimetry for liver cancer radioembolization, Journal of Nuclear Medicine 60 (supplement 1) 122.