Research Opportunities

All students have the opportunity to conduct a thesis research project based on their own interests, ranging broadly across radiation therapy, imaging, and other fields. Faculty advisors from Duke Kunshan University and Duke University will provide sufficient guidance during their research project. Click here to read about faculty and their research fields. Advisors are not assigned but instead chosen by mutual agreement by the student and the faculty member(s).

MS thesis and scholarship projects are components of our medical physics graduate education that are popular among students. The skills and talents that these efforts develop include critical thinking, problem-solving, intellectual courage, confidence, research skills, presentation skills, writing, collaboration, project management, research aptitude, and scholarship. As these skills and talents evolve, students gain great depth of understanding in the medical physics of their chosen project area.

DKU Medical Physics students work on thesis or scholarship projects variously at Duke University in the United States, at Duke Kunshan University in China, and at other locations in China.

At DKU, the medical physics laboratory has 11 advanced radiation-therapy treatment-planning systems and other radiation-oncology software donated by Varian Medical Systems, one of the world’s leading global companies for radiation therapy. Also available to students is the DKU virtual computing system that provides powerful computation capabilities for research projects. At hospitals in the region surrounding DKU, many students take internships and/or perform thesis or scholarship-project research. These experiences are important in developing scientific and clinical ability and in understanding career paths for medical physicists in China. Students also intern at companies in this medical-technology-rich region of China.

Some students pursue thesis and scholarship projects at Duke University. Located in Durham, North Carolina, USA, Duke University includes one of the world’s leading university medical research centers. It is for example a top recipient of research funding from the U.S. National Institutes of Health (NIH). DKU medical physics students have worked on projects with many different faculty members of the Medical Physics Graduate Program at Duke University, which is one of the largest medical physics graduate programs in the United States and which interacts closely with the DKU Medical Physics Graduate Program.

Duke University and Duke Kunshan University enjoy a close collaboration, which is enhanced by a high-speed internet connection between the two universities and by frequent interaction of faculty, staff, and students at the two universities.

The DKU medical physics program works with students to help them develop a thesis or scholarship project that is a good fit to their career interests. These projects range broadly across radiation therapy, imaging, and other fields. Advisors are not assigned but are instead determined by mutual agreement of the student and the faculty member, in consultation with the Director of Graduate Studies.

Class of 2019

Student

Advisor

Thesis title

Haonan Xiao

JIM ZHENG CHANG, PH.D.

Predicting Isocitrate Dehydrogenase 1 (IDH1) Mutation in Patients with Gliomas using a Novel Deep Learning Network

Xinzhi Teng

LEI REN, PH.D.

 

Respiratory Motion Prediction Based on 4DCT/CBCT Images Using Deep Learning

Jiang Zhang

JACKIE WU, PH.D.

Knowledge-Based Statistical Inference Method for Plan Quality Quantification

Pedro Cardoso

QIUWEN WU, PH.D.

Development and Evaluation of a Perpendicular Frame-by-frame Patient-specific QA Method for Large VMAT Fields Using the True Beam Electronic Portal Imaging System

Qi Zeng

QIUWEN WU, PH.D.

Evaluation of the Total Body Irradiation Using Eclipse

Yao Zhao

LEI REN, PH.D.

Cone Beam CT Image Quality Augmentation using Novel Deep Learning Networks

Chibuike Umeh

JUSTUS ADAMSON, PH.D.

Investigating the Dosimetry Potential of the NIPAM Polymer Gel

Obed Laryea

JUSTUS ADAMSON, PH.D.

Conformal Arc Informed Volumetric Modulated Arc for Stereotactic-Radiosurgery

Chenyang Liu

FANG-FANG YIN, PH.D.

Deep Learning for Automatic Real-time Pulmonary Nodule Detection and Quantitative Analysis

Shen-Chiang (Rosie) Hu

FANG-FANG YIN, PH.D.

Automatic Pulmonary Nodule Detection and Localization from Biplanar Chest Radiographs Using Convolutional Neural Network

Li-Ting Chan

GREG PALMER, PH.D.

Validating the Use of Boron Nanoparticles to Quantify Tumor Oxygen Tension in Irradiated Breast Tumors

 

 

Deqi Chen

 

MARK OLDHAM, PH.D.

Platform for Radiation Therapy to Mouse Model of Head and Neck Cancer

 

 

Gong Wang

JAMES BOWSHER, PH.D.

An Investigation of MR Sequences for Partial Volume Correction in PET Image Reconstruction

Songli (Peter) Sha

MARTIN TORNAI, PH.D.

Evaluation of Prone Breast PET/CT Imaging Using Phantoms

Class of 2018

Student Name

Advisor

Thesis Title

Ge Ren

LEI REN, PH.D.

Assessing the Feasibility of Using Deformable Registration for On-board Multi-modality Based Target Localization in Radiation Therapy

Yushi Chang

FANG-FANG YIN, PH.D.

An Investigation of Machine Learning Methods for Delta-radiomic Feature Analysis

Tingting Yu

JAMES BOWSHER, PH.D.

Seven-pinhole versus Single-pinhole SPECT Imaging for Preablation I-131 Scans in the Treatment of Thyroid Cancer

Wentao Wang

JACKIE WU, PH.D.

Beam Optimization for Whole Breast Radiation Therapy Planning

Huimin Zhang

TRONG-KHA TRUONG, PH.D.

Dynamic shimming of the human brain with a 32-channel integrated parallel reception, excitation, and shimming (iPRES) head coil array

Chenjie Zhang

TIMOTHY TURKINGTON, PH.D.

PET Image Quality in the Vicinity of the Bladder with Fluorine-18 and Gallium-68

Research Outcomes

Research conducted by our students has been published in academic journals. Table 1 summarizes the published papers as of Feb. 1, 2020. In addition, students have the opportunity to present their research at academic conferences. All students receive US$250 in financial support to take part in such academic activities. Table 2 lists the presentations and posters at the 2019 AAPM annual meeting in San Antonio, Texas, U.S.

Author

Report Type

Name

Chenyang Liu,Chunhao Wang, Kyle Lafata, Yushi Chang,Yunfeng Cui, Fang-Fang Yin

Presentation

Dose-Specific PET Image-Based Outcome Prediction: A Deep Learning Study for Oropharyngeal Cancer IMRT Application

Jiang Zhang, Qing-Rong Jackie Wu, Yaorong Ge, Chunhao Wang, Yang Sheng, Jatinder Palta, Joseph Salama,Fang-Fang Yin,Jiahan Zhang

Presentation

A Novel Plan-Quality Quantification Method Using K-Nearest-Neighbors Referencing

Pedro Cardoso, Qiuwen Wu , Bin Liang , Ran Wei

Presentation

Development of a Gantry-Resolved EPID-Based Frame-By-Frame Patient-Specific VMAT QA Method

Yao Zhao, Zhuoran Jiang, Xinzhi Teng, Lei Ren

Presentation

CBCT Image Quality Augmentation Using Deep Learning Models: A Comparison Study

Qi Zeng, Qiuwen Wu

Poster

Evaluation of the Total Body Irradiation (TBI) Treatment Planning Using Eclipse

Li Ting Chan, Hengtao Zhang, Christopher DeRosa, Cassandra Fraser, Gregory Palmer

Poster

Quantifying Tumor Reoxygenation in Irradiated Murine Breast Tumors in vivo Using Dual-Emissive Boron Nanoparticles

Li Ting Chan, Ashlyn Rickard, Meng Zhuang, Cassandra Fraser, Gregory Palmer

Poster

Targeting Nanoparticles to the Folate Receptor in Breast Cancer Cells for Hypoxia Imaging

Pedro Cardoso, Qiuwen Wu , Bin Liang , Ran Wei

Poster

Applying TG-218 Methodology to Large Field VMAT QA Using a Gantry-Resolved EPID-Based Technique

Haonan Xiao, Zheng Chang

Poster

A Comparison of Different Data Augmentation Methods in Isocitrate Dehydrogenase 1 (IDH1) Mutation Prediction

Chenyang Liu, Shen-Chiang Hu, Fang-Fang Yin

Poster

Deep Learning for Automatic Real-Time Pulmonary Nodule Detection and Quantitative Analysis

Xinzhi Teng, Yingxuan Chen, Jiang Zhang ,Yao Zhao, Lei Ren

Poster

Respiratory Deformation Registration in 4D-CT/CBCT Using Deep Learning