Application of Convolutional Neural Network Denoising to Improve Conebeam CT Myelographic Images

Conebeam CT (CBCT) is an imaging technique that provides high-resolution, cross-sectional imaging in the fluoroscopy suite. In neuroradiology, CBCT has been used for various applications including temporal bone imaging and during spinal and cerebral angiography. Furthermore, CBCT has been shown to improve imaging of spinal CSF leaks during myelography. One drawback of CBCT 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 conebeam 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 CBCT applications.