Newswise — A team of bioengineers at Rensselaer Polytechnic Institute (RPI), with funding from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), have developed an artificial intelligence (AI) 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. CT has become a commonly prescribed imaging service in modern medicine, providing a non-invasive, detailed, and close-up view of internal anatomy and pathology. Low-dose CT minimizes x-ray radiation to a patient.
“This deep learning, hybrid, image-reconstruction technique integrates low-radiation dose CT images with emerging neural network methods and offers comparable images at much higher speed as those produced with iterative reconstruction methods,” said Behrouz Shabestari, Ph.D., director of the NIBIB program in Artificial Intelligence, Machine Learning, and Deep Learning. “Dr. Wang’s team has advanced deep learning techniques for tomographic imaging and pursued this research with NIH grant support to improve image quality and computational efficiency for low-dose dose CT.”
With its growing use, 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. Medical imaging engineers are working to develop technologies that reduce radiation dose from CT without compromising its diagnostic performance.
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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 new method is a type of AI that researchers refer to as machine learning, or deep learning.
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.