Our project aims to revolutionize the workflow control of radiation therapy by developing an innovative solution for generating Synthetic Computed Tomography (sCT) images using hybrid machine learning techniques.

 

This solution eliminates the need for additional scans, reducing costs and radiation exposure while improving cancer patient outcomes in terms of survival and quality of life. Our research will develop a robust ML pipeline founded on optimal mass transport theory to generate synthetic CT images and estimate electron density information using only MRI images.