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RT Journal Article
SR Electronic
T1 Application of a Denoising High-Resolution Deep Convolutional Neural Network to Improve Conspicuity of CSF-Venous Fistulas on Photon-Counting CT Myelography
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 96
OP 99
DO 10.3174/ajnr.A8097
VO 45
IS 1
A1 Madhavan, Ajay A.
A1 Cutsforth-Gregory, Jeremy K.
A1 Brinjikji, Waleed
A1 Benson, John C.
A1 Diehn, Felix E.
A1 Mark, Ian T.
A1 Verdoorn, Jared T.
A1 Zhou, Zhongxing
A1 Yu, Lifeng
YR 2024
UL http://www.ajnr.org/content/45/1/96.abstract
AB SUMMARY: Photon-counting detector CT myelography is a recently described technique that has several advantages for the detection of CSF-venous fistulas, one of which is improved spatial resolution. To maximally leverage the high spatial resolution of photon-counting detector CT, a sharp kernel and a thin section reconstruction are needed. Sharp kernels and thin slices often result in increased noise, degrading image quality. Here, we describe a novel deep-learning-based algorithm used to denoise photon-counting detector CT myelographic images, allowing the sharpest and thinnest quantitative reconstruction available on the scanner to be used to enhance diagnostic image quality. Currently, the algorithm requires 4–6 hours to create diagnostic, denoised images. This algorithm has the potential to increase the sensitivity of photon-counting detector CT myelography for detecting CSF-venous fistulas, and the technique may be valuable for institutions attempting to optimize photon-counting detector CT myelography imaging protocols.CVFCSF-venous fistulaCTMCT myelographyHR-CNNhigh-resolution deep convolutional neural networkPCCTphoton-counting detector CTPC-CTMphoton-counting CT myelographyQIRquantum iterative reconstructionT3Dlow-energy threshold