Resilience of synthetic CT DL network to varying ZTE-MRI input SNR

Many recent works have proposed methods to convert MRI into synthetic CT (sCT). While they have demonstrated a certain level of accuracy, not many have studied the robustness of those methods. In this work, we study the robustness of a multi-task deep learning (DL) model that computes sCT images from fast ZTE MR images under different levels of image noise. We evaluate its impact on radiation therapy planning. The proposed method demonstrates resilience against input noise variations. It makes way for a clinically acceptable dose calculation with a fast input image acquisition.