The researches find that the technique that uses image post-processing to rapidly convert low-dose computed tomography (CT) scans to images of superior quality, compared to low-dose scans that do not use the AI technique. Therefore CT has become a commonly prescribed imaging service in modern medicine, but providing a non-invasive, detailed; and close-up view of internal anatomy and pathology. Low-dose CT minimizes x-ray radiation to a patient.
Compared to low-dose scans
With its growing use; therefore CT scanning contributes to 62% of the radiation dosage that people in the United States incur from all imaging modalities. While the risk of developing cancer from such radiation exposure is small; public concern has risen with the growing use of CT scans; making CT dose reduction a clinical goal. Because Medical imaging engineers are working to develop technologies that reduce radiation dose from CT without compromising its diagnostic performance.
CT scans are reconstructed from combinations of many X-rays taken from different angles. In their study published in the June 10, 2019, Nature Machine Intelligence, the team led by Ge Wang, Ph.D., Clark & Crossan Endowed Chair Professor in the RPI Department of Biomedical Engineering, and Mannudeep Kalra, M.D., associate professor of radiology at Harvard Medical School and radiologist at Massachusetts General Hospital; compared standard image reconstruction methods from commercial CT machines with a new method, called a modularized neural network.
The radiation dosage
The new method is a type of AI that researchers refer to as machine learning; or deep learning. Therefore The modularized neural network for CT image; reconstruction progressively reduces data noise in a way that radiologists can interactively participate in the optimization of the reconstruction workflow. Each small increment of improved image quality can be evaluated by radiologists according to the medical diagnosis they want to make.
The researchers obtained low-dose CT scans of 60 patients; 30 which depicted abdominal anatomy and the other 30 that depicted chest anatomy. The scans represented three commercial CT scanner products; all that already use iterative image reconstruction algorithms the conventional approach-;to reduce image noise. The noise causes decreased image quality as a result of low radiation dose CT scanning.
Depicted abdominal anatomy
The iterative reconstruction approach refers to the repeated steps that medical images attempt towards generating the CT images consistent to some prior knowledge about imaging physics and image content. Because The researchers compared image reconstruction with currently used iterative methods and their novel deep neural network for image post-processing.
Three radiologists evaluated and scored images for two features: structural fidelity and image noise suppression. Structural fidelity is the ability of the image to accurately depict the anatomical structures in the field of view, which can be diminished by noise. Image noise shows up as random patterns on the image that detract from its clarity.