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.
During cochlear implant (CI) surgical procedures, the surgeon inserts an electrode array into the patient’s cochlea to restore hearing sensation through auditory nerve stimulation. Due to the significant variability in cochlear anatomy from person to person, patient-customized CI surgery planning has the potential to improve the outcome of the CI procedure. For presurgery planning, accurate segmentation of intra-cochlear structures is essential. In this work, we investigate the performance of intra-cochlear segmentation algorithms as a function of variations in image acquisition parameters (i.e., resolution, blurring effect and noise level) that exist in clinical CT images. A dataset of preoperative μCT images of 11 cadaveric temporal bone specimens was used to generate 110 synthetic pseudo-CT images with varying resolution and filter parameters. An active shape model (ASM) based method was evaluated to segment the intra-cochlear structures in those pseudo-CT images. Our results show that the volume of the segmented structures is significantly and strongly correlated with both the resolution and the reconstruction filter strength of the synthetic pseudo-CT images. Recognizing this bias is important for clinicians who use these segmentations or take manual measurements of the cochlea from CT images for pre-surgical planning of CI procedures.
Purpose: Cochlear implants (CIs) use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons toward more optimal placement of the array to reduce trauma and preserve the residual hearing. In addition to the electrode array geometry, the base insertion depth (BID) and the cochlear size could impact the overall array insertion depth.
Approach: We investigated using these measurements to develop a linear regression model that can make preoperative or intraoperative predictions of the insertion depth of lateral wall CI electrodes. Computed tomography (CT) images of 86 CI recipients were analyzed. Using previously developed automated algorithms, the relative electrode position inside the cochlea was measured from the CT images.
Results: A linear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and BID. The model is able to accurately predict angular insertion depths with a standard deviation of 41 deg and absolute deviation error of 32 deg.
Conclusions: Surgeons may use this model for patient-customized selection of electrode array and/or to plan a BID for a given array that minimizes the likelihood of causing trauma to regions of the cochlea where residual hearing exists.
Cochlear implants (CI) use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons towards more optimal placement of the array to reduce trauma and preserve the residual hearing. After analyzing the CT images of 86 lateral wall positioned straight electrode array CI recipients, a linear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and base insertion depth (BID). The model is able to accurately predict angular insertion depths with standard deviation of 41 degrees.
Cochlear implants (CI) use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons towards more optimal placement of the array in order to reduce trauma and preserve the residual hearing of the patient. In addition to the electrode array geometry (length and diameter), both the base insertion depth (BID) and the cochlear scale impact the overall array insertion depth. In this paper, we investigated the influence of these parameters on overall insertion depth with the purpose of developing a model which can make preoperative predictions of insertion depth of lateral wall cochlear implant electrode arrays. CT images of 86 lateral wall positioned straight electrode array CI recipients were analyzed. Using previously developed automated algorithms, relative electrode position inside the cochlea as well as the cochlea scale was measured from the CT images. A linear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and BID. The model is able to accurately predict angular insertion depths with standard deviation of 41 degrees. Surgeons may use this model for patient-customized selection of the electrode array and/or to plan a base insertion depth for a given array that minimizes the likelihood of causing trauma to regions of the cochlea where residual hearing exists.
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