Roncali Lab


We develop multiscale modeling and simulation tools for medical imaging and personalized medicine, with an emphasis on physics and nuclear medicine. Our models are informed with clinical images and experimental data obtained from our close collaborators.

We create in silico models of radiation detectors to advance the technology of positron emission tomography (PET). Projects include new optical Monte Carlo simulation models, studying the physics of ultrafast detectors, optimization of detector design for time-of-flight PET. * Check out our new optiGAN project! *

We develop quantitative dosimetry for targeted radionuclide therapy, with a special interest in liver radioembolization with yttrium-90 microspheres. Our projects include computational fluid dynamics to model the microsphere transport, yttrium-90 imaging, and multiscale dosimetry. We aim at improving the outcome of cancer patients through advanced treatment planning and monitoring.



Targeted radionuclide therapy is a type of radiation therapy based on radiolabeled molecules or particles injected to target tumors with short-range radiation. If attached to a tumor marker, the radionuclide has the potential to reach disseminated tumors with limited radiotoxicity, which makes it attractive and has recently accelerated the development of therapeutic radiopharmaceuticals.

Targeted radionuclide therapy uniquely sits between external beam radiation therapy and chemotherapy and is typically utilized with little consideration for the dosimetry. Our lab combines engineering principles and translational research to develop quantitative dosimetry methods personalized for each patient. We focus on two types of radionuclide therapies administered at UC Davis Health: yttrium-90 radioembolization and lutetium 177 based therapies.


Transarterial radioembolization is a radionuclide therapy based on the delivery of radioactive Y-90 microspheres to liver tumors. Accurate pretreatment dosimetry is necessary to determine the Y-90 activity to inject to optimize the dose to the tumor while sparing the rest of the liver. Combined with monitoring of the patient’s response to treatment, it could result in much greater increase in patient survival than what recent clinical trials have demonstrated.

We are developing patient-specific dosimetry (CFDose) using computational fluid dynamics (CFD) simulation and Y-90 microsphere decay physics to estimate the dose distribution in the liver. Using multiscale modeling, we carry out CFD simulations for each patient’s anatomy, estimating the microsphere transport and the radiation dose distribution.

Our long-term goal is developing a tool to assist physicians with optimizing the quantity and injection site of Y-90 microspheres during radioembolization planning. Current research includes personalizing CFD simulations, developing strategies to speed up the computation, optimizing dose calculation using multiscale modeling.


A. Taebi et al, "On the impact of injection distance to bifurcations on yttrium-90 distribution in liver cancer radioembolization", Journal of Vascular Interventional Radiology 33(6) 2022
A. Taebi et al, "Realistic boundary conditions in SimVascular through inlet catheter modeling", BMC Res Notes 2021 14(1), 215 (2021)
A. Taebi et al, “Multi-scale computational fluid dynamics modeling for personalized liver cancer radioembolization dosimetry”, Journal of Biomechanics (2020)

A. Taebi et al, Computational modeling of the liver arterial blood flow for microsphere therapy: Effect of boundary conditions. Bioengineering 7(3), 64 (2020)

A. Taebi et al., "Estimation of Yttrium-90 Distribution in Liver Radioembolization using Computational Fluid Dynamics and Deep Neural Networks", 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020 P4974-77
E. Roncali et al., “Personalized Dosimetry for Liver Cancer Y-90 Radioembolization Using Computational Fluid Dynamics and Monte Carlo Simulation”, Annals of Biomedical Engineering, 48:1499–1510 2020

A. Taebi et al, Hepatic arterial tree segmentation: Towards patient-specific dosimetry for liver cancer radioembolization, JNM 60 (supp1), 122

E. Roncali et al., Personalized dosimetry for liver cancer radioembolization using fluid dynamics, JNM 58 (supp1), 603


NIH R21 CA237686 (NCI ITCR)

CCSG P30 (NCI P30CA093373)


In parallel to dose prediction, we are also developing quantitative imaging to measure the Y-90 microsphere distribution after treatment, using Positron Emission Tomography (PET). This translational research is carried out within the Department of Radiology at UC Davis Health including the new EXPLORER Molecular Imaging Center  where we are evaluating the impact of high-sensitivity PET on Y-90 quantification using total-body PET.

Problems we are solving include optimizing the reconstruction of the low signal Y-90 PET images, developing quantitative comparison of the Y-90 PET dose distribution with CFDose estimates, incorporating anatomical information from other imaging modalities such as CT to segment tumors and improve quantification.


G. C. Costa et al.,"Radioembolization Dosimetry with Total-Body Y-90 PET", The Journal of Nuclear Medicine (2021)
E. Roncali et al., "Overview of the First NRG Oncology–National Cancer Institute Workshop on Dosimetry of Systemic Radiopharmaceutical Therapy", The Journal of Nuclear Medicine (2021)

A. Taebi et al.,
"Realistic boundary conditions in SimVascular through inlet catheter modeling",  BMC Res Notes 14, 215 (2021).
A. Taebi et al., "Multi-scale computational fluid dynamics modeling for personalized liver cancer radioembolization dosimetry", J. Biomech. Eng., 2020.
A. Taebi et al., "Computational modeling of the liver arterial blood flow for microsphere therapy: Effect of boundary conditions". Bioengineering 7, 2020.
E. Roncali et al., "Personalized dosimetry for liver cancer Y-90 radioembolization using computational fluid dynamics and monte carlo simulation", ABMES 2020
E. Roncali et al., “Comparison of Y-90 liver dose distribution predicted with fluid dynamics with Y-90 PET”, Journal of Nuclear Medicine 61 (supplement 1), 1308-1308

A. Taebi, C. T. Vu and E. Roncali, "Estimation of Yttrium-90 Distribution in Liver Radioembolization using Computational Fluid Dynamics and Deep Neural Networks," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 4974-4977.

Imaging physics modeling and simulation tools

Monte Carlo simulations are extensively used to design radiation detectors and predict medical imaging system performance. Accurate modeling of radiation detectors, typically composed of a semiconductor or a scintillation crystal coupled to a photodetector, is required to predict performance. Until recently, simulation tools used to study radiation detectors for nuclear medicine were designed for high-energy physics and only included simplistic models to track visible photons emitted in the scintillator. Since the spatial and temporal distribution of light defines the spatial, energy, and timing resolution of detectors, this is a huge limitation.

We developed a new optical model, the “LUT Davis model”, based on measured 3D crystal surfaces and ray tracing with data stored in optical look-up-tables (LUTs). This work was validated in a series of benchtop experiments with positron emission tomography detectors and implemented in one of the widespread physics simulation software GATE (2000 users world-wide) in 2017.

We have also developed a standalone application, LUTDavisModel, for the users to generate their customized LUTs.

The Roncali lab is one of the 22 international partners of the  openGATE collaboration that develops, maintains this opensource software, and organizes user worskhops and trainings.

Please visit  for more information on GATE.

Our current work focuses on the development and validation of new optical surfaces with novel reflectors, and the implementation of free computational tools to allow scientists to independently design and test optical surfaces tailored to their application.


D. Sarrut et al., "Advanced Monte Carlo simulations of emission tomography imaging systems with GATE", Phys. Med. Biol. 2021
C. Trigila and E. Roncali, "Integration of polarization in the LUTDavis model for optical Monte Carlo simulation in radiation detectors", Phys. Med. Biol. 2021
C. Trigila and E. Roncali, "Optimization of scintillator–reflector optical interfaces for the LUT Davis model", Med. Phhys. 2021
C. Trigila et al.., "Standalone application to generate custom reflectance Look-Up Table for advanced optical Monte Carlo simulation in GATE/Geant4", Med. Phys. 2021

M. Stockhoff et al., “Advanced optical simulation of scintillation detectors in GATE V8.0: first implementation of a reflectance model based on measured data,” Phys. Med. Biol., 2017. PMB Highlight 2017

E Roncali et al., "An integrated model of scintillator-reflector properties for advanced simulations of optical transport", Phys. Med. Biol., 2017

E Roncali et al., “Modelling the transport of optical photons in scintillation detectors for diagnostic and radiotherapy imaging”, Phys. Med. Biol. 2017. PMB Highlight 2017

E. Berg et al., “Optimizing light transport in scintillation crystals for time-of-flight PET: an experimental and optical Monte Carlo simulation study,” Biomed. Opt. Express, 2015

E. Roncali et al., “Predicting the timing properties of phosphor-coated scintillators using Monte Carlo light transport simulation,” Phys. Med. Biol., 2014

E. Roncali et al., “Simulation of light transport in scintillators based on 3D characterization of crystal surfaces,” Phys. Med. Biol., 2013.


NIH R03 EB025533 2015-2017

NIH R01EB027130 2019-2022

Ultrafast radiation detectors using prompt photons

Cerenkov emission in scintillators only takes ~10 ps, much faster than scintillation (10-300 ns). It is a promising alternative for timing triggering to achieve ultra-fast detectors with timing resolution of less than 100 ps, but very few photons are produced (10-20 per 511 keV photoelectric interaction). Cerenkov-based detectors must minimize the travel time in the crystal, maximize the photon collection, and employ photodetectors that generate a fast signal to trigger on these prompt photons (collaboration with Dr. Sun Il Kwon). We are applying our unique optical modeling tools to study these combined factors and have demonstrated that our simulations can explain complex timing patterns measured reported by several international research groups.

These tools are critical to develop the next generation of ultrafast detectors. We are now pushing the frontiers of Cerenkov-based detectors by studying and optimizing the use of Cerenkov light in semi-conductor radiation detectors such as Thallium Bromide and Thallium Chloride (collaboration with Dr. Gerard Ariño-Estrada).


C. Trigila et al., "The Accuracy of Cerenkov Photons Simulation in Geant4/Gate Depends on the Parameterization of Primary Electron Propagation", Frontiers in Physics 10 (2022)
G. Terragni et al., "Time Resolution Studies of Thallium Based Cherenkov Semiconductors" Frontiers in Physics 10 (2022)
G. Arino-Estrada et al.,"Study of Čerenkov Light Emission in the Semiconductors TlBr and TlCl for TOF-PET", IEEE TRPMS 2020
E. Roncali et al, “Cerenkov light transport in scintillation crystals explained: realistic simulation with GATE,” Biomed.Opt. Express, 2019

S. I. Kwon et al., “Dual-ended readout of bismuth germanate to improve timing resolution in time-of-flight PET,” Phys Med Biol, 2019


The time and memory burden caused by the track-wise approach to particle transport and detection Monte Carlo codes make optical simulation prohibitive at a system level, where hundreds or thousands of scintillators have to be modeled. Generative Adversarial Networks (GANs) are explored as a tool to accelerate the optical simulation of scintillator-based detectors. GANs learn representations of a training dataset by modeling high-dimensional data distributions. Once trained, the resulting model is a neural network that produces distributions following the probability distribution of the training data. Consequently, GANs can enable high-fidelity optical simulation of nuclear medicine systems, mitigating their computational complexity. We present the architecture and training set of the first “optiGAN” version. The training set comprises a large dataset of phase space generated by optical Monte Carlo simulation with GATE/Geant4, which contains multidimensional distributions (spatial, timing, energy) of the optical photons emitted by 511 keV gammas in bismuth germanate and detected on the photodetector face.

The preliminary version of this conditional GAN allows for generating hundreds of realistic optical photon spatial distributions on the photodetector face at different emission points in a few milliseconds, compared to the few seconds needed for a complete transport simulation.
The GAN  accurately generates optical photon distributions for points outside of the training dataset, showing excellent agreement with simulations and great potential to bypass lengthy optical tracking.


C. Trigila and E. Roncali, “A generative adversarial network to accelerate optical Monte Carlo simulation with GATE,” IEEE NSS/MIC, 2022
Trigila C, Srikanth A, Roncali E. A generative adversarial network to speed up optical Monte Carlo simulations. Machine Learning: Science and Technology. 2023 Apr 12;4(2):025005.