Optimization of Photon-Counting CT Myelography for the Detection of CSF-Venous Fistulas Using Convolutional Neural Network Denoising: A Comparative Analysis of Reconstruction Techniques.

Abstract

Background and purpose: Photon-counting detector CT myelography (PCD-CTM) is a recently described technique used for detecting spinal CSF leaks, including CSF-venous fistulas. Various image reconstruction techniques, including smoother-versus-sharper kernels and virtual monoenergetic images, are available with photon-counting CT. Moreover, denoising algorithms have shown promise in improving sharp kernel images. No prior studies have compared image quality of these different reconstructions on photon-counting CT myelography. Here, we sought to compare several image reconstructions using various parameters important for the detection of CSF-venous fistulas.

Materials and methods: We performed a retrospective review of all consecutive decubitus PCD-CTM between February 1, 2022, and August 1, 2024, at 1 institution. We included patients whose studies had the following reconstructions: Br48-40 keV virtual monoenergetic reconstruction, Br56 low-energy threshold (T3D), Qr89-T3D denoised with quantum iterative reconstruction, and Qr89-T3D denoised with a convolutional neural network algorithm. We excluded patients who had extradural CSF on preprocedural imaging or a technically unsatisfactory myelogram-. All 4 reconstructions were independently reviewed by 2 neuroradiologists. Each reviewer rated spatial resolution, noise, the presence of artifacts, image quality, and diagnostic confidence (whether positive or negative) on a 1-5 scale. These metrics were compared using the Friedman test. Additionally, noise and contrast were quantitatively assessed by a third reviewer and compared.

Results: The Qr89 reconstructions demonstrated higher spatial resolution than their Br56 or Br48-40keV counterparts. Qr89 with convolutional neural network denoising had less noise, better image quality, and improved diagnostic confidence compared with Qr89 with quantum iterative reconstruction denoising. The Br48-40keV reconstruction had the highest contrast-to-noise ratio quantitatively.

Conclusions: In our study, the sharpest quantitative kernel (Qr89-T3D) with convolutional neural network denoising demonstrated the best performance regarding spatial resolution, noise level, image quality, and diagnostic confidence for detecting or excluding the presence of a CSF-venous fistula.