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

Authors

Madhavan AA,Zhou Z,Farnsworth PJ,Thorne J,Amrhein TJ,Kranz PG,Brinjikji W,Cutsforth-Gregory JK,Kodet ML,Weber NM,Thompson G,Diehn FE,Yu L

Affiliations (4)

  • From the Department of Radiology (A.A.M., ZZ., P.J.F., J.T., W.B., M.L.K., N.M.W., G.T., F.E.D., L.Y.), Mayo Clinic, Rochester, Minnesota [email protected].
  • From the Department of Radiology (A.A.M., ZZ., P.J.F., J.T., W.B., M.L.K., N.M.W., G.T., F.E.D., L.Y.), Mayo Clinic, Rochester, Minnesota.
  • Department of Radiology (T.J.A., P.G.K.), Duke Health, Durham, North Carolina.
  • Department of Neurology (J.K.C.-G.), Mayo Clinic, Rochester, Minnesota.

Abstract

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. 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. 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. 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.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.