Modern external beam radiation therapy techniques allow the design of highly conformal radiation treatment plans that permit high doses of ionsing radition to be delivered to the tumour in order to eradicate cancer cells while sparing surrounding normal tissue. However, since it is difficult to avoid irradiation of normal tissue altogether and ionising radiation also damages normal cells, patients may develop radiation-induced toxicity following treatment. Furthermore, the highly conformal nature of the radiation treatment plans makes them particularly susceptible to geometric or targeting uncertainties in treatment delivery. Geometric uncertainties may result in under-dosage of the tumour leading to local tumour recurrence or unacceptable morbidity from over-dosage of neighbouring healthy tissue.

I will present work in three areas that bear directly on treatment accuracy and safety in radiation oncology. The first area addresses the development of automated image registration algorithms for image-guided radiation therapy with the aim of improving the accuracy and precision of treatment delivery. The registration methods I will present are based on statistical and spectral models of signal and noise in CT and x-ray images. The second part of my talk addresses the identification of predictors of normal tissue toxicity after radiation therapy and the study of the spatial sensitivity of normal tissue to dose. I will address the development of innovative methods to accurately model the spatial characteristics of radiation dose distributions in 3D and results of the analysis of this important, but heretofore lacking, information as a contributing factor in the development of radiation-induced toxicity. Finally, given the increasing complexity of modern radiation treatment plans and a trend towards an escalation in prescribed doses, it is important to implement a safety system to reduce the risk of adverse events arising during treatment and improve clinical efficiency. I will describe ongoing efforts to formalise and automate quality assurance processes in radiation oncology.

Biography
Reshma Munbodh is currently an Assistant Professor in the Department of Diagnostic Imaging and Therapeutics at UConn Health. She received her undergraduate degree in Computer Science and Electronics from the University of Edinburgh and her PhD in medical image processing and analysis applied to cancer from Yale University. Following her PhD, she performed research and underwent clinical training in Therapeutic Medical Physics at the Memorial Sloan-Kettering Cancer Center. She is interested in the development and application of powerful analytical and computational approaches towards improving the diagnosis, understanding and treatment of cancer. Her current projects include the development of image registration algorithms for image-guided radiation therapy, the study of normal tissue toxicity following radiation therapy, longitudinal studies of brain gliomas to monitor tumour progression and treatment response using quantitative MRI analysis and the formalisation and automation of quality assurance processes in radiation oncology.