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.

Low Dose Computed Tomography Denoising Using Deep Learning

CT (Computed Tomography) scans are widely used medical images used to diagnose disease such as cancer. CT Scanners pass x-rays through the body in order to generate cross-sectional images. Unfortunately pro-longed exposure to radiation (via x-rays) can damage the body, and thus one aims to minimize the x-ray dose they receive. However, modern CT scanners produce lower quality images when using low x-ray dose which defeats their purpose as a diagnostic tool. We propose a post-processing algorithm to enhance the quality of CT images produced at low radiation dose.

Developing advanced techniques for denoising Low-dose CT Images

Computed Tomography (CT) is one of the most widespread non-invasive imaging modalities in medical diagnostics. Recent concerns regarding radiation induced cancer, has drawn a lot of attention to reduce the radiation dose used during CT scanning. However, the signal to noise ratio of scans taken at lower radiation dose is considerably lower than at higher dosages, resulting in poorer diagnostic accuracy. Hence post processing of low-dose scans has become a major concern in medical image processing.