Abstract
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.