Arlyn Blache: Generation of Synthetic CT Images from Brain MRI, Masters Proposal

Masters Proposal presentation by: Arlyn Blache

Supervisors
Dr. Jake Kendrick (School of Physics, Mathematics and Computing, The University of Western
Australia; Centre for Advanced Technologies in Cancer Research, Perth)
Dr. Pejman Rowshanfarzad (School of Physics, Mathematics and Computing, The University of
Western Australia; Centre for Advanced Technologies in Cancer Research, Perth)

Title: Generation of Synthetic CT images from MRI of intracranial radiotherapy patients with brain
metastases using Deep Neural Learning

Aims:
This project aims to enhance radiation therapy planning by developing a robust method for generating synthetic CT (sCT) images from MRI data using advanced artificial intelligence (AI) techniques, specifically deep learning. CT imaging is crucial for accurate dose calculation due to its ability to characterize radiation absorption, while MRI provides superior soft tissue contrast without additional radiation exposure. Recent advancements in AI, particularly in training neural networks to extract complex data patterns, have made MRI-only radiotherapy planning increasingly feasible. This project focuses on creating a neural network model to generate sCT images for intracranial radiotherapy patients with brain metastases, using a large dataset of paired MRI and CT images. The goal is to reduce reliance on traditional CT scans, thus minimizing harmful X-ray exposure, and to potentially extend this approach to other anatomies, like the prostate.

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