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. The iterative reconstruction approach refers to the repeated steps that medical imagers attempt towards generating the CT images consistent to some prior knowledge about imaging physics and image content. 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.
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For abdominal imaging, the radiologists gave higher scores to images produced with the modularized neural network method on two of the three scanning devices and considered the images from the third device as of comparable quality with the iterative reconstruction method. For chest imaging, the experts found the image quality comparable between the two methods for all devices. Overall, the modularized neural network performed favorably or comparably relative to the iterative method when the radiologists evaluated structural fidelity and noise suppression.
The researchers add that their new method is much faster than the current commercial methods and that institutions with current CT scanners of various brands can utilize their technique to produce similar image results. Wang said that the study results confirm that deep learning could help to produce high quality CT images at lower dosages, and at the same time, this novel approach much more efficient than the iterative process, which is time consuming and subject to image noise artifacts.
The research was supported, in part, by a grant from NIBIB (EB017140) for research to develop systems for low-dose CT.
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