The University of Texas MD Anderson Cancer Center
Department of Radiation Physics
Dr. Mackin applies high performance computing and machine learning methods problems in radiation therapy and medical imaging, focusing on two research areas: radiomics and prompt gamma imaging.
Radiomics uses quantifiable image features to describe and classify tumor phenotypes. Dr. Mackin and his collaborators attempt to improve the effectiveness of radiomics studies by understanding the features, especially how the features are effected by noise. Opportunities for students in this research area include radiomics texture phantom development, robustness optimization of radiomics features using digital signal processing, and full radiomics analyses of diseases or complications.
Prompt gamma imaging attempts to provide real-time, in vivo verification of particle therapy treatment delivery. Particle beams delivery a large amount of dose at the end of their range, enabling highly conformal dose distributions. Accurate treatment delivery is crucial to prevent underdosing of the tumor or overdosing of healthy tissue. Fortunately, nature provides a signal that may allow us to verify the treatment. Nuclear scattering of the beam particles in the patient produces high energy, prompt gamma rays. Dr. Mackin and his collaborators are developing a system to produce images of this gamma ray emission, which has been shown to be a proxy for the delivered dose. Students working with Dr. Mackin could design novel algorithms to produce images from Compton camera gamma detectors, write GPU software to produce images in real time, or simulate proton therapy treatments using Geant4 Monte Carlo.
Education & Training
PhD, Rice University, 2010