PT  - JOURNAL ARTICLE
AU  - Madhavan, Ajay A.
AU  - Zhou, Zhongxing
AU  - Farnsworth, Paul J.
AU  - Thorne, Jamison
AU  - Amrhein, Timothy J.
AU  - Kranz, Peter G.
AU  - Brinjikji, Waleed
AU  - Cutsforth-Gregory, Jeremy K.
AU  - Kodet, Michelle L.
AU  - Weber, Nikkole M.
AU  - Thompson, Grace
AU  - Diehn, Felix E.
AU  - Yu, Lifeng
TI  - Optimization of Photon-Counting CT Myelography for the Detection of CSF-Venous Fistulas Using Convolutional Neural Network Denoising: A Comparative Analysis of Reconstruction Techniques
AID  - 10.3174/ajnr.A8695
DP  - 2025 Jun 19
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2025/06/19/ajnr.A8695.short
4100  - http://www.ajnr.org/content/early/2025/06/19/ajnr.A8695.full
AB  - 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.CNNconvolutional neural networkCNRcontrast-to-noise ratioCVFCSF-venous fistulaEIDenergy-integrating detectorPCDphoton-counting detectorPCD-CTMphoton-counting detector CT myelographyQIRquantum iterative reconstructionSIHspontaneous intracranial hypotensionT3Dlow-energy thresholdUHRultra-high-resolutionVMIvirtual monoenergetic imaging