Projects

We work on many projects, and the following is just a snapshot, as of May 2020.

Monte Carlo simulation with GPU parallel computation

Monte Carlo (MC) simulation is a versatile method that is widely used in radiation medicine. It is capable of faithfully describing the physical processes and flexibly handling complex geometries. Over of years, it serves as one of the most accurate methods for radiation dose computation and has increasing applications on radiation track simulation and computation of radiation-induced DNA damages. However, MC simulation is also a computationally demanding task. Computational efficiency has become a bottleneck for CPU-based simulation packages in advanced applications. Parallel computation via graphic processing units (GPUs) can be a cost-effective option to overcome the hurdle, while NVIDIA Compute Unified Device Architecture (CUDA) is especially preferred, considering its good documentation support.

In our group, we targeted on novel GPU-based MC package development and application. So far, we have developed two packages, gMicroMC and gPET, under a joint effort with Dr. Xun Jia’s group in the University of Texas Southwestern Medical Center.

• gMicroMC is a novel microscopic GPU-based MC simulation platform that deals with radiation track and DNA damage calculation. Its first version development enables water radiolysis and DNA damage computation from a single electron. It achieves a 520x speedup with a single GPU card over that executed on a single CPU card. The first version open-source code is available at the Github repository of utaresearch/gMicroMC. New development efforts include support of new physical and chemical modules, various DNA geometries, etc., while its application has been focused on quantitative understanding of radiation phenomena under various radiation scenarios.  
• gPET is a GPU-based MC simulation package dedicated for positron emission tomography (PET) imaging simulation. A speedup factor of 500 was obtained for gPET performed on a single GPU over GATE8.0 on a single CPU. The open source code of gPET is available at the Github repository of utaresearch/gPET. gPET is currently used for high resolution, high sensitivity small animal PET designs with our collaborators in UC Davis.

We are also maintaining another GPU-based MC simulation package of gDPM, initially developed by Dr. Xun Jia’s group. We are currently using gDPM for dose verifications in various radiation conditions.

Microbeam irradiation

To understand the radiation induced biological effect, except for the computational effort, our group also works on designing and constructing microbeam irradiation facilities. So far, two approaches are under investigation:

• Alpha irradiator. This is to build a cost-effective and easy-to-maintain microscopic radiation beamline in our lab. The idea is to focus the alpha particles from a natural radiation source via an electromagnetic lens system, such that the cells can be irradiated at a controlled and constant dose rate manner. The first version of the irradiator is designed based on the permanent magnetic focusing lens (simulation performed with COMSOL), while the entire system is moving from the drawing board to under-fabrication.
• Accelerator based low MeV ion microbeam line. This is to build a more versatile microscopic beamline enabling subcellular matrix irradiation with various ion species, beam currents, dose rates, etc. A novel electrostatic focusing quadruplet lens system was simulated (with GICOSY and SIMION). Another more powerful electrostatic focusing triplet lens system was also simulated and its aberration is under investigation. The ultimate goal of this project is to obtain a compact and high focusing lens system that could be attached to existing low MeV accelerators for radiobiological study.  

Motion management in radiotherapy

In the radiation clinic, one of the biggest concerns is the reproducibility of the patient set up at the treatment stage compared to that at the treatment planning stage. It is critical for achieving the two main goals of radiotherapy — full dose painting of the treatment target and enough dose sparing of organs at risk (OARs). Multiple factors could influent the patient alignment with treatment beams, while one main source is the inter- and intra- fractional (between and during treatment fractions, respectively) target motion. In our group, we are conducting researches for effective motion management in various tumor sites, for example, prostate cancer (PCa), liver cancer, etc.

• Inter- and intra- fractional prostate motion management. In our previous study, we have developed a novel algorithm to track the intrafractional prostate motion, called Projection Marker Matching Method (PM3). Our recent efforts include quantifying the intrafractional motion of high risk prostate cancer patients via this method. Meanwhile, we are investigating independent inter-fractional motion between pelvic and prostate regions in whole pelvic prostate cancer treatment. New algorithms to compensate for the observed motion are under development.
• Intra-fractional liver motion management. Different from prostate motion, liver motion is found more significantly affected by the breathing motion and the rotational motion magnitude is relatively large. Recently, we extended the PM3 method to be able to reconstruct the translational and rotational liver motion simultaneously, resulting in the 6DoF-PM3 method. Our recent efforts target on developing more novel algorithms that will be suitable for MRI-guided liver radiotherapy.

Deep learning based auto-treatment planning

Dose-to-volume histogram (DVH)-based optimization engine is widely used in modern treatment planning systems (TPSs). However, it is usually a complex process to perform inverse treatment planning using these TPSs. To obtain a clinically acceptable treatment plan, it is usually a labor intensive and time consuming process for a human planner to tune the relevant parameters.

Reinforcement learning (RL) is known for its vast capability of solving problems where sequential decision making is needed. RL algorithms that incorporate deep neural networks, known as DRL, can beat human experts playing numerous games, as well as the world champions of Go. Its general structures contain:

• Agent: the entity that performs actions in environment and gets rewards based on the action it takes;
• Environment: the world that the agent interacts with;
• State(s): a concurrent and immediate situation returned by environment that the agent can take actions against;
• Action: a response given by the agent facing a given state;
• Reward: the feedback by which we measure the success or failure of an agent’s actions in a given state.

In this project, we are trying to train a DRL neural network, which can mimic the behavior of human planners by automatically operating the DVH-based optimization engine for high-quality treatment plans. 

Auxiliary nanoparticle based target therapy

Recent years, there are multiple efforts towards advanced radiotherapy to overcome substantial obstacles in the state-of-the-art radio-scenarios, for example, radiation resistance of tumor cells, patient quality of life correlated with radiation, etc. Among them, one attracting research effort is the nanoparticle assisted radiotherapy. Nanoparticles, for example, gold nanoparticle, were found a significant dose enhancement effect when used conjugate with ionizing radiation species. Some fundamental studies revealed that this radiation enhancement effect mainly comes from significantly increased secondary particle generations due to the high Z elements in these nanoparticles.

Recently, a new type of organic nanoparticle, Copper-cystamine (Cu-Cy), was discovered in our collaborator Dr. Wei Chen’s group, who is a professor of UTA physics. It is found that Cu-Cy can be activated by kV x-rays and produce numerous singlet oxygen species, which has a significant radiation enhancement effect on multiple tumor cell lines. However, how the singlet oxygen is activated by Cu-Cy and how quantitatively the different pathways contribute to the total production is still not clear. In our group, we are targeting on studying the fundamental mechanism involved in the Cu-Cy activated singlet oxygen production process, via new computational modeling and MC simulations.