88. Generation of CT myelograms using noncontrast CT with machine learning

Abstract

<h3>BACKGROUND CONTEXT</h3> Preoperative imaging is essential in surgical planning for spinal deformity. While MRIs have reduced the need for invasive imaging in most cases, some patients, especially patients with prior hardware, still require CT myelograms, which involve injection of contrast into the thecal sac. Creating an algorithm that can generate a "predicted CT myelogram" may help reduce the risks associated with invasive imaging while providing valuable clinical information. <h3>PURPOSE</h3> Our study aims to develop an algorithm that utilizes the recent advances in computer vision, GANs in particular, to generate predicted CT myelograms from noncontrast CT images of the spine to reduce the need for unnecessary invasive imaging. <h3>STUDY DESIGN/SETTING</h3> Cross-sectional database review study. <h3>PATIENT SAMPLE</h3> Routine imaging data from 32 patients aged 18 or older from 01/01/2005 to 11/01/2020 that underwent both noncontrast CT and CT myelogram at a major academic center. <h3>OUTCOME MEASURES</h3> Qualitative assessment of predicted CT myelograms compared to actual CT myelograms on the 10-patient hold-out dataset. <h3>METHODS</h3> Using routine imaging data from 22 patients, we trained a GAN to generate predicted CT myelograms from noncontrast CTs. The GAN's effectiveness was then evaluated on a hold-out dataset of 10 additional patients. <h3>RESULTS</h3> We successfully trained a GAN using 1875 slices of noncontrast CT and CT myelogram data from 22 patients. When evaluated on data from a 10 patient hold-out dataset, the GAN was able to enhance the thecal sac on some of the patient images. <h3>CONCLUSIONS</h3> GANs can enhance presence of the thecal sac using information present in noncontrast CT images, but may require additional optimizations to generate complete contrast. Further optimizations and training with more data may allow GANs to reduce resource utilization and patient harms by decreasing the need for invasive imaging in the future. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.