Researchers 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.
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 to reduce image noise. They compared image reconstruction with currently used iterative methods and their novel deep neural network for image post-processing.
Overall, the modularized neural network performed favorably or comparably relative to the iterative method when the radiologists evaluated structural fidelity and noise suppression. The new method is much faster than the current commercial methods. Institutions with current CT scanners of various brands can utilize their technique to produce similar image results.
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