Prior to medical physics residency at Emily Couric Clinical Cancer Center, I worked as a professional research staff in radiation oncology department at University of Virginia. The research was focus on different aspects of modeling and simulating uncertainties, and robust planning techniques in external beam radiation therapy. In 2018, this project was awarded an R01 NIH grant.

Radiation therapy robustness analyzer (RTRA)

Since simulation of different uncertainty sources is a computationally demanding process, we developed a stand-alone GPU-accelerated software called radiation therapy robustness analyzer (RTRA) that quickly simulates prevalent uncertainty sources. This software works in two different modes , i.e. graphical user interface (GUI) mode and scripted mode. In the GUI mode, the simulation can be run after selecting pertinent DICOM/Pinnacle files, ROIs to analyze, uncertainty model, and virtual treatment simulation parameters. In the scripted mode, the software is interfaced with pinnacle treatment planning system where it automatically generates plan robustness report for a given treatment plan and simulation parameters.

Knowledge-based quality control system (KBQC)

Delineation error-detection is a tedious process. Such errors propagate throughout the radiation treatment and could impact the treatment outcome. KBQC extensively employs machine learning techniques to learn from historical data of previously treated patients in order to identify the gross errors in regions of interests. There are two methods that out group have applied one employ statistical anomaly detection method, and the other one utilizes a classification-based anomaly detection scheme.

Clinical adequacy of auto-segmented regions of interest via plan robustness analysis

The radiation therapy community needs more evidential data/analysis to fully trust in auto-segmentation methods. Because of this need, we developed a novel method to inter-compare the manual and auto-segmented delineation in the presence of other uncertainties that occur in the radiation treatment process. For a population of prostate cancer, our method shows the effect of delineation uncertainty due to auto-segmentation on the probabilistic dosimetric and biological indices. The method helps to increase the confidence of using auto-segmentation algorithm which eventually saves hours of manual, tedious and error-prone contouring process in the clinics.

Numerous authors have investigated differences between auto-segmented and manually drawn contours, mainly investigating the accuracy through comparison of contour-based similarity coefficients. These similarity analyses are intended to reveal how the auto-segmented ROIs compared with the manual gold standard, however, they do not assess the adequacy of the auto-segmented ROIs for the radiation therapy treatment. Dice similarity coefficient (DSC) is one of the most commonly used metrics to assess the quality of segmented ROIs. However, DSC lacks the spatial information, and therefore infinitely many configurations with different dosimetric outcomes could result in the same DSC. In this paper, the authors’ intention was to analyze auto-segmentation algorithm based on dosimetric and biological metrics without considering any other shape similarity metrics. For reference, DSC analysis is in the supplemental material.

The research is mainly focused on the enhancement of existing external beam inverse treatment planning techniques - often referred to as external beam automated treatment planning techniques - used as a part of the medical procedure of radiotherapy. A radiation treatment plan is collaboratively designed by a team consisting of radiation oncologists, radiation therapists, medical physicists and medical dosimetrists. The procedure generally starts with capturing a patient’s organs and tumor(s) using Computed Tomography (CT) or multi-modality image matching techniques. After delineating the target volumes and organs at risk, a radiation oncologist chooses an appropriate state-of-the-art technique such as Intensity Modulated Radiation Therapy (IMRT) or Volumetric Modulated Arc Therapy (VMAT) to treat the patient. The selected technique determines the settings of a sophisticated computer controlled device called a Linear Accelerator (Linac) to deliver high-energy X-rays to the region of a patient’s tumor in order to destroy cancer cells while sparing the surrounding normal tissue.

In prevailing inverse treatment planning systems (TPS), it could take several hours for the dosimetrists to find a viable set of parameters that enforces the prescribed clinical protocol for difficult cases. To expedite the process and also to make it more intuitive for the planners, the Reduced Order Constrained Optimization (ROCO) paradigm was developed for IMRT at Rensselaer Polytechnic Institute (RPI) with the collaboration of researchers at Memorial Sloan-Kettering Cancer Center (MSKCC). Using dimensionality reduction techniques from machine learning, the new paradigm enables the dosimetrists to quickly devise clinically relevant IMRT treatment plans with less intervention. In this research work, we pursued the following objectives:

Integrating IMRT ROCO in the clinical treatment planning pipeline

we have vailidated the process in the newly employed TPS at MSKCC, namely Eclipse from Varian Medical Systems. The idea is to make IMRT ROCO available for dosimetrists by providing a user-friendly graphical user interface that facilitates the planner interactions with the TPS via the Eclipse Application Programming Interface. To harness all the available computational resources of multi-core CPUs, highly vectorized and threaded linear algebra functionalities have been added to IMRT ROCO. Faster and more robust design enabled verifying the hypothesis that IMRT ROCO significantly enhances planning speed in a clinical setup. [Talk and poster on ROCO's integration in Eclipse TPS.]

Voxel-based reduced-order constrained optimization method

Developed a new iterative voxel-based ROCO method to automatically accommodate clinical constraints in an IMRT planning problem. Control system theory has been used to design PID controller that automatically navigates the solution space until convergence.

Multi-Agent Orbit Design for Visual Perception Enhancement Purpose

We have developed a robust optimization-based method to design orbits on which the sensory perception of the desired physical quantities are maximized. The method provides a convenient way to incorporate various constraints imposed by many spacecraft missions such as collision avoidance, co-orbital configuration, altitude and frozen orbit constraints along with Sun-Synchronous orbit. The study has specifically investigated designing orbits for constrained visual sensor planning applications as the case study. For this purpose, the key elements to form an image in such vision systems are considered and effective factors are taken into account to define a metric for perception quality. The effectiveness of the proposed method is confirmed for several scenarios on low and medium Earth orbits as well as a challenging Space-Based Space Surveillance program application.