PT  - JOURNAL ARTICLE
AU  - Madhavan, Ajay A.
AU  - Zhou, Zhongxing
AU  - Thorne, Jamison
AU  - Kodet, Michelle L.
AU  - Cutsforth-Gregory, Jeremy K.
AU  - Schievink, Wouter I.
AU  - Mark, Ian T.
AU  - Schueler, Beth A.
AU  - Yu, Lifeng
TI  - Application of Convolutional Neural Network Denoising to Improve Cone Beam CT Myelographic Images
AID  - 10.3174/ajnr.A8877
DP  - 2025 Jun 17
TA  - American Journal of Neuroradiology
PG  - ajnr.A8877
4099  - http://www.ajnr.org/content/early/2025/06/17/ajnr.A8877.short
4100  - http://www.ajnr.org/content/early/2025/06/17/ajnr.A8877.full
AB  - ABSTRACT Cone beam CT is an imaging modality that provides high-resolution, cross-sectional imaging in the fluoroscopy suite. In neuroradiology, cone beam CT has been used for various applications including temporal bone imaging and during spinal and cerebral angiography. Furthermore, cone beam CT has been shown to improve imaging of spinal CSF leaks during myelography. One drawback of cone beam CT is that images have a relatively high noise level. In this technical report, we describe the first application of a high-resolution convolutional neural network to denoise cone beam CT myelographic images. We show examples of the resulting improvement in image quality for a variety of types of spinal CSF leaks. Further application of this technique is warranted to demonstrate its clinical utility and potential use for other cone beam CT applications.ABBREVIATIONS: CBCT = cone beam CT; CB-CTM = cone beam CT myelography; CTA = CT angiography; CVF = CSF-venous fistula; DSM = digital subtraction myelography; EID = energy integrating detector; FBP = filtered back-projection; SNR = signal-to-noise ratio