Cochlear implant (CI) surgery requires manual or robotic insertion of an electrode array into the patient’s cochlea. At the vast majority of institutions including ours, preoperative CT scans are acquired and used to plan the procedure because they permit to visualize the bony anatomy of the temporal bone. However, CT images involve ionizing radiation, and some institutions and surgeons prefer preoperative MRI, especially for children. To expand the number of patients who can benefit from a computer-assisted CT-based planning system we are developing without additional radiation exposure, we propose to use a conditional generative adversarial network (cGAN)-based method to generate synthetic CT (sCT) images from multi-sequence MR images. We use image quality-based, segmentation-based, and planning-based metrics to compare the sCTs with the corresponding real CTs (rCTs). Loss terms were used to improve the quality of the overall image and of the local regions containing critical structures used for planning. We found very good agreement between the segmentations of structures in the sCTs and the corresponding rCTs with Dice values equal to 0.94 for the labyrinth, 0.79 for the ossicles, and 0.81 for the facial nerve. Such a high Dice value for the ossicles is noteworthy because they cannot be seen in the MR images. Furthermore, we found that the mean errors for quantities used for preoperative insertion plans were smaller than what is humanly perceivable. Our results strongly suggest that potential CI recipients who only have MR scans can benefit from CT-based preoperative planning through sCT generation.
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