Abstract #4250

Towards higher accuracy mapping of MRI to electron density using a 3D deep CNN for MRI-only radiotherapy treatment planning

Jessica E Scholey¹, Abhejit Rajagopal², Elena Grace Vasquez³, Atchar Sudhyadhom⁴, and Peder Eric Zufall Larson²

¹Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States, ²Department of Radiology, University of California, San Francisco, San Francisco, CA, United States, ³Department of Physics, University of California, Berkeley, Berkeley, CA, United States, ⁴Department of Radiation Oncology, Harvard Medical School, Boston, MA, United States

We implemented a novel approach of using MRI to synthesize CT datasets acquired at MV photon energies for more accurate electron density mapping in radiotherapy treatment planning. We used a 3D deep convolutional neural network and demonstrated clinical proof-of-concept by evaluating the dosimetric impact of using synthetic datasets in a test radiotherapy treatment plan. The proposed method produced mean MAE of 72.8±17.3 HU and SSIM of 0.82 in the test dataset. The dose distributions computed on the test case produced 100% gamma passing rate (computed at 3%/3mm) indicating that synthetic MV images may be used for clinical treatment planning.

This abstract and the presentation materials are available to members only; a login is required.

Join Here