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PROJECTS

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.

 

The project will be carried out in collaboration with King Faisal Specialist Hospital & Research Centre, and a multidisciplinary team of experts from four leading institutions will work together to develop and deploy an sCT solution that caters to the needs of radiation oncology patients. By leveraging advanced machine-learning techniques and high-quality data, we aim to create a cutting-edge solution that contributes to the advancement of medical technology for cancer patients

Synthetic CT

CT and MRI are crucial for radiation therapy and diagnosis. CT offers electron density and geometrical accuracy, while MRI provides superior soft tissue contrast and functional information. MRI is better for tumor targeting and organ risk delineation. Generating synthetic CT (sCT) from MRI is a cost-effective method to reduce the need for multiple imaging modalities. This paper reviews techniques for generating sCT from MRI.

Data Engineering

Data Engineering of MRI-only Radiation Therapy for Patients with Mobility Disabilities

This paper describes the novel data engineering pipeline for the acquisition and cleansing of imaging datasets in the context of radiotherapy. The data engineering model is reported in this technical paper to aid research in dose–volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization

Federated Learning

The project uses deep learning to create synthetic CT images from MRI data for radiotherapy planning. It validates the method against traditional CT, integrates it into software, and conducts clinical trials to assess efficacy and safety.

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