Artificial Neural Networks for Low-Dose Computed Tomography (CT) Image Denoising

Computed Tomography (CT) scans are commonly used in the medical field to diagnose diseases such as cancer. It uses X-rays to capture human structures without invading the body. Although it has a been widely used and provide great benefit for patient diagnosis, the cumulative exposure to X-ray radiation can cause health risks. For this reason, researchers have been developing ways to minimize the X-ray dose. However, lowering the radiation dosage in commercial CT scanners also affects the quality of CT images which leads to inaccurate diagnosis. Therefore, we utilize the low-dose CT (LDCT) images and proposing a denoising algorithm to enhance their quality. The industry and partner organization will benefit from this by integrating this algorithm into products that can be marketed towards radiologists.

Faculty Supervisor:

Javad Alirezaie

Student:

Partner:

Dr. Paul Babyn Professional Medical Corporation

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program: