Abstract #4966
Predicting Stereotactic Radiosurgery Dose Maps from Pre-Therapy MR Images using a Deep Neural Network
Shraddha Pandey1,2, Tugce Kutuk3, Matthew N Mills3, Mahmoud Abdalah4, Olya Stringfield4, Kujtim Latifi3, Timothy J Robinson3, Wilfrido Moreno1, Kamran A Ahmed3, and Natarajan Raghunand2,5
1Electrical Engineering, University of South Florida, Tampa, FL, United States, 2Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States, 3Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States, 4Quantitative Imaging Shared Service, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States, 5Department of Oncologic Sciences, University of South Florida, Tampa, FL, United States
Synopsis
Stereotactic Radiosurgery (SRS) of asymptomatic brain metastases provides lasting tumor control with only minor side effects to healthy brain. An active research area is the development of models to predict tumor response to a given dose of Radiation Treatment (RT) from analysis of pre-RT and post-RT MR images (i.e., the forward problem). Here we propose an approach to train a deep neural net on pre-RT MR images of patients with Breast Cancer Metastases to the Brain (BCMB), for predicting RT dose maps that will yield desired/target tumor voxel intensities on post-RT MR images (i.e., the inverse problem).
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