Our research is focused on the development of novel imaging and beam delivery techniques and new machine learning algorithms to improve the efficacy of radiation therapy. We are interested in medical image processing, image reconstruction, signal processing, image-guided radiation therapy (IGRT), and adaptive radiation therapy (ART). We are working on both software and hardware approaches to enhance the quality of different medical imaging modalities (including CT, CBCT, PET, and MRI) for their quantitative applications in image-guided and adaptive radiation therapy. We have also developed a number of machine-learning algorithms for image reconstruction, medical imaging analysis and treatment outcome prediction.
The topics we are currently working on include:
- Low-dose CT and cone-beam CT
- Motion management in radiation therapy
- Quantitative imaging for adaptive radiation therapy
- Machine learning for treatment outcome prediction, classification and image reconstruction
Our research has been supported by NIH National Institute of Biomedical Imaging and Bioengineering (R01 EB020366, R01 EB027898), American Cancer Society (RSG-13-326-01-CCE and ACS-IRG-02-196), Cancer Prevention and Research Institute of Texas (RP130109, RP110562, and RP160661 ), and Elekta Ltd.