Optimization of Photon-Counting CT Myelography for the Detection of CSF-Venous Fistulas Using Convolutional Neural Network Denoising: A Comparative Analysis of Reconstruction Techniques
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.
Authors
- Ajay A. Madhavan (ORCID: https://orcid.org/0000-0003-1794-4502)
- Jamison E. Thorne (ORCID: https://orcid.org/0000-0003-3993-6423)
- Jeremy K. Cutsforth‐Gregory (ORCID: https://orcid.org/0000-0002-5689-4424)
- Nikkole M. Weber (ORCID: https://orcid.org/0000-0001-6198-2958)
- Lifeng Yu (ORCID: https://orcid.org/0000-0002-4267-1326)
- Waleed Brinjikji (ORCID: https://orcid.org/0000-0001-5271-5524)
- Grace Thompson (ORCID: https://orcid.org/0000-0002-7501-5325)
- Paul J. Farnsworth (ORCID: https://orcid.org/0000-0002-3955-6722)
- Felix E. Diehn (ORCID: https://orcid.org/0000-0001-9458-8017)
- Timothy J. Amrhein (ORCID: https://orcid.org/0000-0002-9354-9486)
- Peter George Kranz (ORCID: https://orcid.org/0000-0001-5410-7135)
- Zhongxing Zhou (ORCID: https://orcid.org/0000-0003-1935-2762)
- Michelle L. Kodet (ORCID: https://orcid.org/0000-0003-4317-1697)
Institutions
- Mayo Clinic (US)
- Duke University (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-02-10
- DOI
- https://doi.org/10.3174/ajnr.a8695
- Citations
- 3
- Primary Topic
- Cerebrospinal fluid and hydrocephalus
- Type
- article
- Field-Weighted Citation Impact
- 2.73