by
Gus Iversen, Editor in Chief | December 14, 2022
From the November 2022 issue of HealthCare Business News magazine
Deep learning reconstruction sufficient at reducing dose in pediatric CT scans
In April, using deep learning-based reconstruction, Japanese researchers reduced the dosage in pediatric CT exams while still maintaining the same or even improving on the image quality of those using interactive reconstruction algorithms.
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Because children are more sensitive to ionizing radiation, clinicians use the lowest possible dose when scanning them. One way to do that is to decrease tube voltage with iterative reconstruction. But lowering voltage increases image noise and impairs detection of low-contrast objects, especially when using reduced slice thickness to assess a child’s small anatomic structures.
Hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR) can reduce noise and artifacts but do not preserve noise texture, low-contrast spatial resolution and low-contrast object detectability as well when decreasing dose.
An emerging technique for CT image reconstruction, DLR uses a convolutional neural network to generate low-noise, high-quality images in short time frames, reports Physicsworld. The team applied the application to low-tube-voltage exams and reduced noise without degrading the noise texture and image sharpness.
The findings were published in the American Journal of Roentgenology.
