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Background: Chronic subdural hematoma evacuation can be achieved in select patients through bedside placement of the Subdural Evacuation Port System (SEPS; Medtronic, Inc., Dublin, Ireland). This procedure involves drilling a burr hole at the thickest part of the hematoma. Identifying this location is often difficult, given the variable tilt of available imaging and distant anatomic landmarks. This paper evaluates the feasibility and accuracy of a bedside navigation system that relies on visible light-based 3-dimensional (3D) scanning and image registration to a pre-procedure computed tomography scan. The information provided by this system may increase accuracy of the burr hole location., Methods: In Part 1, the accuracy of this system was evaluated using a rigid 3D printed phantom head with implanted fiducials. In Part 2, the navigation system was tested on 3 patients who underwent SEPS placement., Results: The error in registration of this system was less than 2.5 mm when tested on a rigid 3D printed phantom head. Fiducials located in the posterior aspect of the head were difficult to reliably capture. For the 3 patients who underwent 5 SEPS placements, the distance between anticipated SEPS burr hole location based on registration and actual burr hole location was less than 1cm., Conclusions: A bedside cranial navigation system based on 3D scanning and image registration has been introduced. Such a system may increase the success rate of bedside procedures, such as SEPS placement. However, technical challenges such as the ability to scan hair and practical challenges such as minimization of patient movement during scans must be overcome., (Copyright © 2020 Elsevier Inc. All rights reserved.)

This study investigated the effects of cochlear implant (CI) electrode array type and scalar location on the number of channels available to CI recipients for maximum speech understanding and sound quality. Eighteen post-lingually deafened adult CI recipients participated, including 11 recipients with straight electrode arrays entirely in scala tympani and 7 recipients with translocated precurved electrode arrays. Computerized tomography was used to determine electrode placement and scalar location. In each condition, the number of channels varied from 4 to 22 with equal spatial distribution across the array. Speech recognition (monosyllables, sentences in quiet and in noise), subjective speech sound quality, and closed-set auditory tasks (vowels, consonants, and spectral modulation detection) were measured acutely. Recipients with well-placed straight electrode arrays and translocated precurved electrode arrays performed similarly, demonstrating asymptotic speech recognition scores with 8-10 channels, consistent with the classic literature. This finding contrasts with recent work [Berg, Noble, Dawant, Dwyer, Labadie, and Gifford. (2019). J. Acoust. Soc. Am. 145, 1556-1564] that found precurved electrode arrays well-placed in scala tympani demonstrate continuous performance gains beyond 8-10 channels. Given these results, straight and translocated precurved electrode arrays are theorized to have less channel independence secondary to their placement farther away from neural targets.

Cochlear implants (CIs) are used to treat subjects with hearing loss. In a CI surgery, an electrode array is inserted into the cochlea to stimulate auditory nerves. After surgery, CIs need to be programmed. Studies have shown that the cochlea-electrode spatial relationship derived from medical images can guide CI programming and lead to significant improvement in hearing outcomes. We have developed a series of algorithms to segment the inner ear anatomy and localize the electrodes. But, because clinical head CT images are acquired with different protocols, the field of view and orientation of the image volumes vary greatly. As a consequence, visual inspection and manual image registration to an atlas image are needed to document their content and to initialize intensity-based registration algorithms used in our processing pipeline. For large-scale evaluation and deployment of our methods these steps need to be automated. In this article we propose to achieve this with a deep convolutional neural network (CNN) that can be trained end-to-end to classify a head CT image in terms of its content and to localize landmarks. The detected landmarks can then be used to estimate a point-based registration with the atlas image in which the same landmark set's positions are known. We achieve 99.5% classification accuracy and an average localization error of 3.45 mm for 7 landmarks located around each inner ear. This is better than what was achieved with earlier methods we have proposed for the same tasks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Imaging of cochlear implant (CI) electrode arrays (EAs) consists of intraoperative fluoroscopy to rule out tip fold-over and/or post-operative computerized tomography (CT) if concern exists regarding extrusion or misplacement of the EA. Intraoperative CT (iCT) can satisfy these current needs and enables specification of final intracochlear position. Aims/objectives: To describe iCT scanning of CI recipients at an academic medical center. Materials and methods: iCT was used to scan CI recipients within the operating room before recovering from general anesthesia. Results: In fiscal year 2019, 301 CI were placed (83 children, 218 adult). One hundred, seventy-five iCTs were performed (58% of total CIs) of which 52 were children (63% of pediatric CIs) and 123 were adult (57% of adult CIs). Of 7 CI surgeons, use of iCT ranged from 14% to 100% (mean 60%). Four tip fold-overs were identified and corrected intraoperatively. Surgeons reported using the images to improve technique (i.e. pulling back precurved EAs to improve perimodiolar positioning). Conclusion and significance: The current standard of care for CI is to insert EAs without feedback as to final location. iCT provides surgeons with rapid post-insertion feedback which allows detection and correction of suboptimally placed EAs as well as refinement of surgical technique.

Patients with Parkinson's disease (PD) often experience reductions in the proficiency to inhibit actions. The motor symptoms of PD can be effectively treated with deep brain stimulation (DBS) of the subthalamic nucleus (STN), a key structure in the frontal-striatal network that may be directly involved in regulating inhibitory control. However, the precise role of the STN in stopping control is unclear. The STN consists of functional subterritories linked to dissociable cortical networks, although the boundaries of the subregions are still under debate. We investigated whether stimulating the dorsal and ventral subregions of the STN would show dissociable effects on ability to stop. We studied 12 PD patients with STN DBS. Patients with two adjacent contacts positioned within the bounds of the dorsal and ventral STN completed two testing sessions (OFF medication) with low amplitude stimulation (0.4 mA) at either the dorsal or ventral contacts bilaterally, while performing the stop task. Ventral, but not dorsal, DBS improved stopping latencies. Go reactions were similar between dorsal and ventral DBS STN. Stimulation in the ventral, but not dorsal, subregion of the STN improved stopping speed, confirming the involvement of the STN in stopping control and supporting the STN functional subregions., (© The Author(s) 2020. Published by Oxford University Press.)

The thalamus serves as the central relay station for the brain. It processes and relays sensory and motor signals between different subcortical regions and the cerebral cortex and it can be divided into several neuronal clusters referred to as nuclei. Each of these can possibly be subdivided into sub-nuclei. Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is however a challenging task because the small size of the nuclei and the lack of contrast over the thalamus region in clinically acquired images does not permit the visualization of their boundaries. A number of methods have been developed for thalamus parcellation but the vast majority of these relies on diffusion imaging or functional imaging. The low resolution of these images only permit localizing the largest nuclei. In this work we propose a method to segment smaller nuclei. We first present a protocol to build histological-like atlases from a series of high-field (7 Tesla) MR images acquired with different pulse sequences that each permits to visualize the boundaries of a subset of the nuclei. We use this protocol to scan 9 subjects and we manually delineate 23 thalamic nuclei following the Morel atlas naming convention for each of these subjects. Manual contours for the nuclei are subsequently utilized to create statistical shape models. With these data, we compare four methods for the segmentation of thalamic nuclei in 3 T images we have also acquired for the 9 subjects included in the study: (1) single atlas, (2) multi atlas, (3) statistical shape, and (4) hierarchical statistical shape in which thalamic nuclei are hierarchically fitted to the images, starting from the largest ones. Results of a leave-one-out validation study conducted on the nine image sets we have acquired show that the multi atlas approach improves upon the single atlas approach for most nuclei. Segmentations obtained with the hierarchical statistical shape model yield the highest accuracy, with dice coefficients ranging from 0.53 to 0.90, mean surface errors from 0.27 mm to 0.64 mm, and maximum surface errors from 1.31 mm to 2.52 mm for all nuclei averaged across test cases. This suggests the feasibility of using such approach for localizing thalamic substructures in clinically acquired MR volumes. It may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Cochlear implants (CIs) are surgically implanted neural prosthetic devices that are used to treat severe-to-profound hearing loss. These devices are programmed post implantation and precise knowledge of the implant position with respect to the intra cochlear anatomy (ICA) can help the programming audiologists. Over the years, we have developed algorithms that permit determining the position of implanted electrodes relative to the ICA using pre- and post-implantation CT image pairs. However, these do not extend to CI recipients for whom pre-implantation CT (Pre-CT) images are not available. This is so because post-operative images are affected by strong artifacts introduced by the metallic implant. To overcome this issue, we have proposed two methods to segment the ICA in post-implantation CT (Post-CT) images, but they lead to segmentation errors that are substantially larger than errors obtained with Pre-CT images. Recently, we have proposed an approach that uses 2D-conditional generative adversarial nets (cGANs) to synthesize pre-operative images from post-operative images. This permits to use segmentation algorithms designed to operate on Pre-CT images even when these are not available. We have shown that it substantially and significantly improves the results obtained with methods designed to operate directly on post-CT images. In this article, we expand on our earlier work by moving from a 2D architecture to a 3D architecture. We perform a large validation and comparative study that shows that the 3D architecture improves significantly the quality of the synthetic images measured by the commonly used MSSIM (Mean Structural SIMilarity index). We also show that the segmentation results obtained with the 3D architecture are better than those obtained with the 2D architecture although differences have not reached statistical significance., (Copyright © 2019. Published by Elsevier B.V.)

The dentate nucleus (DN) is a gray matter structure deep in the cerebellum involved in motor coordination, sensory input integration, executive planning, language, and visuospatial function. The DN is an emerging biomarker of disease, informing studies that advance pathophysiologic understanding of neurodegenerative and related disorders. The main challenge in defining the DN radiologically is that, like many deep gray matter structures, it has poor contrast in T1-weighted magnetic resonance (MR) images and therefore requires specialized MR acquisitions for visualization. Manual tracing of the DN across multiple acquisitions is resource-intensive and does not scale well to large datasets. We describe a technique that automatically segments the DN using deep learning (DL) on common imaging sequences, such as T1-weighted, T2-weighted, and diffusion MR imaging. We trained a DL algorithm that can automatically delineate the DN and provide an estimate of its volume. The automatic segmentation achieved higher agreement to the manual labels compared to template registration, which is the current common practice in DN segmentation or multiatlas segmentation of manual labels. Across all sequences, the FA maps achieved the highest mean Dice similarity coefficient (DSC) of 0.83 compared to T1 imaging ( DSC = 0.76 ), T2 imaging ( DSC = 0.79 ), or a multisequence approach ( DSC = 0.80 ). A single atlas registration approach using the spatially unbiased atlas template of the cerebellum and brainstem template achieved a DSC of 0.23, and multi-atlas segmentation achieved a DSC of 0.33. Overall, we propose a method of delineating the DN on clinical imaging that can reproduce manual labels with higher accuracy than current atlas-based tools., (© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).)

The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson's disease. In order to manually trace these structures, a combination of high-resolution and specialized sequences at 7 T are used, but it is not feasible to routinely scan clinical patients in those scanners. Targeted imaging sequences at 3 T have been presented to enhance contrast in a select group of these structures. In this work, we show that a series of atlases generated at 7 T can be used to accurately segment these structures at 3 T using a combination of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the thalamus and putamen, a median Dice Similarity Coefficient (DSC) over 0.88 and a mean surface distance <1.0 mm were achieved using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus pallidus a DSC over 0.75 and a mean surface distance <1.2 mm were achieved using a combination of T1 and inversion recovery imaging sequences. In the substantia nigra and sub-thalamic nucleus a DSC of over 0.6 and a mean surface distance of <1.0 mm were achieved using the inversion recovery imaging sequence. On average, using T1 and optimized inversion recovery together significantly improved segmentation results than over individual modality (p < 0.05 Wilcoxon sign-rank test)., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Conventional optical tracking systems use cameras sensitive to near-infrared (NIR) light and NIR illuminated/active-illuminating markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely used within the neurosurgical theater and is a staple in the standard of care for craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure in the OR with the patient in a fixed head orientation. We propose a framework to achieve this in the OR without conventional tracking technology, i.e., a "trackerless" approach. Briefly, we investigate an extension of the 3D Slicer which combines surgical planning and craniotomy designation. While taking advantage of the well-developed 3D Slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with an intraoperative magnetic resonance imaging-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without the use of a conventional optical tracking technology. To test this approach, four experienced neurosurgeons performed experiments on five different surgical cases using our 3D Slicer module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this craniotomy planning approach and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental realization of an integrated trackerless surgical guidance platform.

Background: Estimating the extent of affected skin is an important unmet clinical need both for research and practical management in many diseases. In particular, cutaneous burden of chronic graft-vs-host disease (cGVHD) is a primary outcome in many trials. Despite advances in artificial intelligence and 3D photography, progress toward reliable automated techniques is hindered by limited expert time to delineate cGVHD patient images. Crowdsourcing may have potential to provide the requisite expert-level data., Materials and Methods: Forty-one three-dimensional photographs of three cutaneous cGVHD patients were delineated by a board-certified dermatologist. 410 two-dimensional projections of the raw photos were each annotated by seven crowd workers, whose consensus performance was compared to the expert., Results: The consensus delineation by four of seven crowd workers achieved the highest agreement with the expert, measured by a median Dice index of 0.7551 across all 410 images, outperforming even the best worker from the crowd (Dice index 0.7216). For their internal agreement, crowd workers achieved a median Fleiss's kappa of 0.4140 across the images. The time a worker spent marking an image had only weak correlation with the surface area marked, and very low correlation with accuracy. Percent of pixels selected by the consensus exhibited good correlation (Pearson R = 0.81) with the patient's affected surface area., Conclusion: Crowdsourcing may be an efficient method for obtaining demarcations of affected skin, on par with expert performance. Crowdsourced data generally agreed with the current clinical standard of percent body surface area to assess cGVHD severity in the skin., (Published 2019. This article is a U.S. Government work and is in the public domain in the USA.)

Background: Postoperative imaging studies by numerous groups have revealed that final cochlear implant (CI) electrode position impacts audiological outcomes with scalar location consistently shown to be a significant factor. Modiolar proximity has been less extensively studied, and findings regarding the effect of insertion depth have been inconsistent., Methods: Using previously developed automated algorithms, we determined CI electrode position in an Institutional Review Board-approved database of 220 CI ears. Generalized linear models (GLM) were used to analyze the relationship between audiological outcomes and factors including age, duration of CI use, device type, and electrode position., Results: For precurved arrays, GLM revealed that scalar position, modiolar proximity, base insertion depth, and sex were significant factors for Consonant-Nucleus-Consonant (CNC) words (R = 0.43, p < 0.001, n = 92 arrays), while scalar position, modiolar proximity, age, and postlingual onset of deafness were significant for Bamford-Kawal-Bench Sentences in Noise (BKB-SIN) (R = 0.51, p < 0.001, n = 85) scores. Other factors were not significant in the final model after controlling for these variables. For straight arrays, we found the insertion depth, postlingual deafness, and length of CI use to be highly significant (R = 0.47, p < 0.001) factors for CNC words (91 arrays), while for BKB-SIN scores the most significant (R = 0.47, p < 0.001) factors were insertion depth, younger age, and postlingual deafness (89 arrays)., Conclusion: Our results confirm the significance of electrode positioning in audiological outcomes. The most significant positional predictors of outcome for precurved arrays were full scala tympani (ST) insertion and the modiolar distance, while for the lateral wall arrays the depth of insertion was the most significant factor.

Hypothesis: Electrocochleography (ECochG) recorded during cochlear implant (CI) insertion from the apical electrode in conjunction with postinsertion ECochG can identify electrophysiologic differences that exist between groups with and without a translocation of the array from the scala tympani (ST) into the scala vestibuli (SV)., Background: Translocation of the CI electrode from ST into SV can limit performance postoperatively. ECochG markers of trauma may be able to aid in the ability to detect electrode array-induced trauma/scalar translocation intraoperatively., Methods: Twenty-one adult CI patients were included. Subjects were postoperatively parsed into two groups based on analysis of postoperative imaging: 1) ST (n = 14) insertion; 2) SV (n = 7) insertion, indicating translocation of the electrode. The ECochG response elicited from a 500 Hz acoustic stimulus was recorded from the lead electrode during insertion when the distal electrode marker was at the round window, and was compared to the response recorded from a basal electrode (e13) after complete insertion., Results: No statistically significant change in mean ECochG magnitude was found in either group between recording intervals. There was a mean loss of preoperative pure-tone average of 52% for the nontranslocation group and 94% for the translocation group., Conclusions: Intraoperative intracochlear ECochG through the CI array provides a unique opportunity to explore the impact of the CI electrode on the inner ear. Specifically, a translocation of the array from ST to SV does not seem to change the biomechanics of the cochlear region that lies basal to the area of translocation in the acute period.

Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: Laser interstitial thermal therapy (LITT) for mesial temporal lobe epilepsy (mTLE) has reported seizure freedom rates between 36% and 78% with at least 1 year of follow-up. Unfortunately, the lack of robust methods capable of incorporating the inherent variability of patient anatomy, the variability of the ablated volumes, and clinical outcomes have limited three-dimensional quantitative analysis of surgical targeting and its impact on seizure outcomes. We therefore aimed to leverage a novel image-based methodology for normalizing surgical therapies across a large multicenter cohort to quantify the effects of surgical targeting on seizure outcomes in LITT for mTLE., Methods: This multicenter, retrospective cohort study included 234 patients from 11 centers who underwent LITT for mTLE. To investigate therapy location, all ablation cavities were manually traced on postoperative magnetic resonance imaging (MRI), which were subsequently nonlinearly normalized to a common atlas space. The association of clinical variables and ablation location to seizure outcome was calculated using multivariate regression and Bayesian models, respectively., Results: Ablations including more anterior, medial, and inferior temporal lobe structures, which involved greater amygdalar volume, were more likely to be associated with Engel class I outcomes. At both 1 and 2 years after LITT, 58.0% achieved Engel I outcomes. A history of bilateral tonic-clonic seizures decreased chances of Engel I outcome. Radiographic hippocampal sclerosis was not associated with seizure outcome., Significance: LITT is a viable treatment for mTLE in patients who have been properly evaluated at a comprehensive epilepsy center. Consideration of surgical factors is imperative to the complete assessment of LITT. Based on our model, ablations must prioritize the amygdala and also include the hippocampal head, parahippocampal gyrus, and rhinal cortices to maximize chances of seizure freedom. Extending the ablation posteriorly has diminishing returns. Further work is necessary to refine this analysis and define the minimal zone of ablation necessary for seizure control., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

Minimally invasive image-guided cochlear implantation (CI) research continues to progress. We previously performed the procedure in nine patients. Herein, we describe the first revision operation for device failure following minimally invasive image-guided CI. It was possible to reuse the original drill channel, obviating the need to convert to a wide-field mastoidectomy. Revision surgery, if required, can therefore be performed safely after minimally invasive image-guided CI. LEVEL OF EVIDENCE: NA Laryngoscope, 129:1458-1461, 2019., (© 2018 The American Laryngological, Rhinological and Otological Society, Inc.)

This study investigated the number of channels needed for maximum speech understanding and sound quality in 30 adult cochlear implant (CI) recipients with perimodiolar electrode arrays verified via imaging to be completely within scala tympani (ST). Performance was assessed using a continuous interleaved sampling (CIS) strategy with 4, 8, 10, and 16 channels and n-of-m with 16 maxima. Listeners were administered auditory tasks of speech understanding [monosyllables, sentences (quiet and +5 dB signal-to-noise ratio, SNR), vowels, consonants], spectral modulation detection, as well as subjective estimates of sound quality. Results were as follows: (1) significant performance gains were observed for speech in quiet (monosyllables and sentences) with 16- as compared to 8-channel CIS, (2) 16 channels in a 16-of-m strategy yielded significantly higher outcomes than 16-channel CIS for sentences in noise (percent correct and subjective sound quality) and spectral modulation detection, (3) 16 channels in a 16-of-m strategy yielded significantly higher outcomes as compared to 8- and 10-channel CIS for monosyllables, sentences (quiet and noise), consonants, spectral modulation detection, and subjective sound quality, (4) 16 versus 8 maxima yielded significantly higher speech recognition for monosyllables and sentences in noise using an n-of-m strategy, and (5) the degree of benefit afforded by 16 versus 8 maxima was inversely correlated with mean electrode-to-modiolus distance. These data demonstrate greater channel independence with perimodiolar electrode arrays as compared to previous studies with straight electrodes and warrant further investigation of the minimum number of maxima and number of channels needed for maximum auditory outcomes.

Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a variety of neurological diseases. A key challenge in DBS is in the placement of a stimulation electrode in the anatomical location that maximizes efficacy and minimizes side effects. Pre-operative localization of the optimal stimulation zone can reduce surgical times and morbidity. Current methods of producing efficacy probability maps follow an anatomical guidance on magnetic resonance imaging (MRI) to identify the areas with the highest efficacy in a population. In this work, we propose to revisit this problem as a classification problem, where each voxel in the MRI is a sample informed by the surrounding anatomy. We use a patch-based convolutional neural network to classify a stimulation coordinate as having a positive reduction in symptoms during surgery. We use a cohort of 187 patients with a total of 2,869 stimulation coordinates, upon which 3D patches were extracted and associated with an efficacy score. We compare our results with a registration-based method of surgical planning. We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0.670 compared to a baseline registration-based approach, which achieves an AUC of 0.627 (p < 0.01). Although additional validation is needed, the proposed classification framework and deep learning method appear well-suited for improving pre-surgical planning and personalize treatment strategies.

Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs. Existing methods for CI electrode localization do not generalize well on large-scale datasets of clinical CTs implanted with different brands of CI arrays. In this paper, we propose a novel method for localizing different brands of CI electrodes in clinical CTs. We firstly generate the candidate electrode positions at sub-voxel resolution in a whole head CT by thresholding an up-sampled feature image and voxel-thinning the result. Then, we use a graph-based path-finding algorithm to find a fixed-length path that consists of a subset of the candidates as the localization result. Validation on a large-scale dataset of clinical CTs shows that our proposed method outperforms the state-of-art CI electrode localization methods and achieves a mean error of 0.12 mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to treat patients with hearing loss. For CI recipients, sound bypasses the natural transduction mechanism and directly stimulates the neural regions, thus creating a sense of hearing. Post-operatively, CIs need to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and only relies on the subjective response of the patient. Multiple programming sessions are usually needed, which can take a frustratingly long time. We have developed an image-guided cochlear implant programming (IGCIP) system to facilitate the process. In IGCIP, we segment the intra-cochlear anatomy and localize the electrode arrays in the patient's head CT image. By utilizing their spatial relationship, we can suggest programming settings that can significantly improve hearing outcomes. To segment the intra-cochlear anatomy, we use an active shape model (ASM)-based method. Though it produces satisfactory results in most cases, sub-optimal segmentation still happens. As an alternative, herein we explore using a deep learning method to perform the segmentation task. Large image sets with accurate ground truth (in our case manual delineation) are typically needed to train a deep learning model for segmentation but such a dataset does not exist for our application. To tackle this problem, we use segmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for which accurate manual delineation is available. Using this method, we achieve better results than the ASM-based method.

Objective: To assess white matter integrity in patients with essential tremor (ET) and Parkinson disease (PD) with moderate to severe motor impairment., Methods: Sedated participants with ET (n = 57) or PD (n = 99) underwent diffusion tensor imaging (DTI) and fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity values were computed. White matter tracts were defined using 3 well-described atlases. To determine candidate white matter regions that differ between ET and PD groups, a bootstrapping analysis was applied using the least absolute shrinkage and selection operator. Linear regression was applied to assess magnitude and direction of differences in DTI metrics between ET and PD populations in the candidate regions., Results: Fractional anisotropy values that differentiate ET from PD localize primarily to thalamic and visual-related pathways, while diffusivity differences localized to the cerebellar peduncles. Patients with ET exhibited lower fractional anisotropy values than patients with PD in the lateral geniculate body ( p < 0.01), sagittal stratum ( p = 0.01), forceps major ( p = 0.02), pontine crossing tract ( p = 0.03), and retrolenticular internal capsule ( p = 0.04). Patients with ET exhibited greater radial diffusivity values than patients with PD in the superior cerebellar peduncle ( p < 0.01), middle cerebellar peduncle ( p = 0.05), and inferior cerebellar peduncle ( p = 0.05)., Conclusions: Regionally, distinctive white matter microstructural values in patients with ET localize to the cerebellar peduncles and thalamo-cortical visual pathways. These findings complement recent functional imaging studies in ET but also extend our understanding of putative physiologic features that account for distinctions between ET and PD., (© 2018 American Academy of Neurology.)

Purpose: Cochlear implants (CIs) are neural prosthetic devices that provide a sense of sound to people who experience profound hearing loss. Recent research has indicated that there is a significant correlation between hearing outcomes and the intracochlear locations of the electrodes. We have developed an image-guided cochlear implant programming (IGCIP) system based on this correlation to assist audiologists with programming CI devices. One crucial step in our IGCIP system is the localization of CI electrodes in postimplantation CTs. Existing methods for this step are either not fully automated or not robust. When the CI electrodes are closely spaced, it is more difficult to identify individual electrodes because there is no intensity contrast between them in a clinical CT. The goal of this work is to automatically segment the closely spaced CI electrode arrays in postimplantation clinical CTs., Methods: The proposed method involves firstly identifying a bounding box that contains the cochlea by using a reference CT. Then, the intensity image and the vesselness response of the VOI are used to segment the regions of interest (ROIs) that may contain the electrode arrays. For each ROI, we apply a voxel thinning method to generate the medial axis line. We exhaustively search through all the possible connections of medial axis lines. For each possible connection, we define CI array centerline candidates by selecting two points on the connected medial axis lines as the array endpoints. For each CI array centerline candidate, we use a cost function to evaluate its quality, and the one with the lowest cost is selected as the array centerline. Then, we fit an a priori known geometric model of the array to the centerline to localize the individual electrodes. The method was trained on 28 clinical CTs of CI recipients implanted with three models of closely spaced CI arrays. The localization results are compared with the ground truth localization results manually generated by an expert., Results: A validation study was conducted on 129 clinical CTs of CI recipients implanted with three models of closely spaced arrays. Ninety-eight percent of the localization results generated by the proposed method had maximum localization errors lower than one voxel diagonal of the CTs. The mean localization error was 0.13 mm, which was close to the rater's consistency error (0.11 mm). The method also outperformed the existing automatic electrode localization methods in our validation study., Conclusion: Our validation study shows that our method can localize closely spaced CI arrays with an accuracy close to what is achievable by an expert on clinical CTs. This represents a crucial step toward automating IGCIP and translating it from the laboratory to the clinical workflow., (© 2018 American Association of Physicists in Medicine.)

OBJECTIVEStereotactic electroencephalography (SEEG) is being used with increasing frequency to interrogate subcortical, cortical, and multifocal epileptic foci. The authors describe a novel technique for SEEG in patients with suspected epileptic foci refractory to medical management.METHODSIn the authors' technique, standard epilepsy evaluation and neuroimaging are used to create a hypothesis-driven SEEG plan, which informs the 3D printing of a novel single-path, multiple-trajectory, omnidirectional platform. Following skull-anchor platform fixation, electrodes are sequentially inserted according to the preoperative plan. The authors describe their surgical experience and technique based on a review of all cases, adult and pediatric, in which patients underwent invasive epilepsy monitoring via SEEG during an 18-month period at Vanderbilt University Medical Center. Platform and anatomical variables influencing localization error were evaluated using multivariate linear regression.RESULTSUsing this novel technology, 137 electrodes were inserted in 15 patients with focal epilepsy with favorable recording results and no clinical complications. The mean entry point localization error was 1.42 mm (SD 0.98 mm), and the mean target point localization error was 3.36 mm (SD 2.68 mm). Platform distance, electrode trajectory angle, and intracranial distance, but not skull thickness, were independently associated with localization error.CONCLUSIONSThe multiple-trajectory, single-path, omnidirectional platform offers satisfactory accuracy and favorable clinical results, while avoiding cumbersome frames and prohibitive up-front costs associated with other SEEG technologies.

Cochlear implants (CIs) are neural prosthetics which are used to treat patients with hearing loss. CIs use an array of electrodes which are surgically inserted into the cochlea to stimulate the auditory nerve endings. After surgery, CIs need to be programmed. Studies have shown that the spatial relationship between the intra-cochlear anatomy and electrodes derived from medical images can guide CI programming and lead to significant improvement in hearing outcomes. However, clinical head CT images are usually obtained from scanners of different brands with different protocols. The field of view thus varies greatly and visual inspection is needed to document their content prior to applying algorithms for electrode localization and intra-cochlear anatomy segmentation. In this work, to determine the presence/absence of inner ears and to accurately localize them in head CTs, we use a volume-to-volume convolutional neural network which can be trained end-to-end to map a raw CT volume to probability maps which indicate inner ear positions. We incorporate a false positive suppression strategy in training and apply a shape-based constraint. We achieve a labeling accuracy of 98.59% and a localization error of 2.45 mm. The localization error is significantly smaller than a random forest-based approach that has been proposed recently to perform the same task.

We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-affected CTs, the cGAN produces CTs in which the artifacts are removed. As a pre-processing step, we also propose a band-wise normalization method, which splits a CT image into three channels according to the intensity value of each voxel and we show that this method improves the performance of the cGAN. We test our cGAN on post-implantation CTs of 74 ears and the quality of the artifact-corrected images is evaluated quantitatively by comparing the segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with a previously proposed technique.

Hypothesis: Electrocochleography (ECochG) patterns observed during cochlear implant (CI) electrode insertion may provide information about scalar location of the electrode array., Background: Conventional CI surgery is performed without actively monitoring auditory function and potential damage to intracochlear structures. The central hypothesis of this study was that ECochG obtained directly through the CI may be used to estimate intracochlear electrode position and, ultimately, residual hearing preservation., Methods: Intracochlear ECochG was performed on 32 patients across 3 different implant centers. During electrode insertion, a 50-ms tone burst stimulus (500 Hz) was delivered at 110 dB SPL. The ECochG response was monitored from the apical-most electrode. The amplitude and phase changes of the first harmonic were imported into an algorithm in an attempt to predict the intracochlear electrode location (scala tympani [ST], translocation from ST to scala vestibuli [SV], or interaction with basilar membrane). Anatomic electrode position was verified using postoperative computed tomography (CT) with image processing., Results: CT analysis confirmed 25 electrodes with ST position and 7 electrode arrays translocating from ST into SV. The ECochG algorithm correctly estimated electrode position in 26 (82%) of 32 subjects while 6 (18%) electrodes were wrongly identified as translocated (sensitivity = 100%, specificity = 77%, positive predictive value = 54%, and a negative predictive value = 100%). Greater hearing loss was observed postoperatively in participants with translocated electrode arrays (36 ± 15 dB) when compared with isolated ST insertions (28 ± 20 dB HL). This result, however, was not significant (p = 0.789)., Conclusion: Intracochlear ECochG may provide information about CI electrode location and hearing preservation.

Cochlear Implants (CIs) restore hearing using an electrode array that is surgically implanted into the intra-cochlear cavities. Research has indicated that each electrode can lie in one of several cavities and that location is significantly associated with hearing outcomes. However, comprehensive analysis of this phenomenon has not been possible because the cavities are not directly visible in clinical CT images and because existing methods to estimate cavity location are not accurate enough, labor intensive, or their accuracy has not been validated. In this work, a novel graph-based search is presented to automatically identify the cavity in which each electrode is located. We test our approach on CT scans from a set of 34 implanted temporal bone specimens. High resolution μCT scans of the specimens, where cavities are visible, show our method to have 98% cavity classification accuracy. These results indicate that our methods could be used on a large scale to study the link between electrode placement and outcome, which could lead to advances that improve hearing outcomes for CI users.

The dentato-rubro-thalamic tract (DRTT) regulates motor control, connecting the cerebellum to the thalamus. This tract is modulated by deep-brain stimulation in the surgical treatment of medically refractory tremor, especially in essential tremor, where high-frequency stimulation of the thalamus can improve symptoms. The DRTT is classically described as a decussating pathway, ascending to the contralateral thalamus. However, the existence of a nondecussating (i.e. ipsilateral) DRTT in humans was recently demonstrated, and these tracts are arranged in distinct regions of the superior cerebellar peduncle. We hypothesized that the ipsilateral DRTT is connected to specific thalamic nuclei and therefore may have unique functional relevance. The goals of this study were to confirm the presence of the decussating and nondecussating DRTT pathways, identify thalamic termination zones of each tract, and compare whether structural connectivity findings agree with functional connectivity. Diffusion-weighted imaging was used to perform probabilistic tractography of the decussating and nondecussating DRTT in young healthy subjects from the Human Connectome Project (n = 91) scanned using multi-shell diffusion-weighted imaging (270 directions; TR/TE = 5500/89 ms; spatial resolution = 1.25 mm isotropic). To define thalamic anatomical landmarks, a segmentation procedure based on the Morel Atlas was employed, and DRTT targeting was quantified based on the proportion of streamlines arriving at each nucleus. In parallel, functional connectivity analysis was performed using resting-state functional MRI (TR/TE = 720/33 ms; spatial resolution = 2 mm isotropic). It was found that the decussating and nondecussating DRTTs have significantly different thalamic endpoints, with the former preferentially targeting relatively anterior and lateral thalamic nuclei, and the latter connected to more posterior and medial nuclei (p < 0.001). Functional and structural connectivity measures were found to be significantly correlated (r = 0.45, p = 0.031). These findings provide new insight into pathways through which unilateral cerebellum can exert bilateral influence on movement and raise questions about the functional implications of ipsilateral cerebellar efferents., (Copyright © 2018. Published by Elsevier Inc.)

Cochlear implants (CIs) are standard treatment for patients who experience sensorineural hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare that they permit to achieve natural fidelity and many patients experience poor outcomes. Our group has developed image-guided CI programming techniques (IGCIP), in which image analysis techniques are used to locate the intracochlear position of CI electrodes to determine patient-customized settings for the CI processor. Clinical studies have shown that IGCIP leads to significantly improved outcomes. A crucial step is the localization of the electrodes, and rigorously quantifying the accuracy of our algorithms requires dedicated datasets. We discuss the creation of a ground truth dataset for electrode position and its use to evaluate the accuracy of our electrode localization techniques. Our final ground truth dataset includes 30 temporal bone specimens that were each implanted with one of four different types of electrode array by an experienced CI surgeon. The arrays were localized in conventional CT images using our automatic methods and manually in high-resolution μ CT images to create the ground truth. The conventional and μ CT images were registered to facilitate comparison between automatic and ground truth electrode localization results. Our technique resulted in mean errors of 0.13 mm in localizing the electrodes across 30 cases. Our approach successfully permitted characterizing the accuracy of our methods, which is critical to understand their limitations for use in IGCIP.

Cochlear implants (CIs) are neural prostheses that restore hearing using an electrode array implanted in the cochlea. After implantation, the CI processor is programmed by an audiologist. One factor that negatively impacts outcomes and can be addressed by programming is cross-electrode neural stimulation overlap (NSO). We have proposed a system to assist the audiologist in programming the CI that we call image-guided CI programming (IGCIP). IGCIP permits using CT images to detect NSO and recommend deactivation of a subset of electrodes to avoid NSO. We have shown that IGCIP significantly improves hearing outcomes. Most of the IGCIP steps are robustly automated but electrode configuration selection still sometimes requires manual intervention. With expertise, distance-versus-frequency curves, which are a way to visualize the spatial relationship learned from CT between the electrodes and the nerves they stimulate, can be used to select the electrode configuration. We propose an automated technique for electrode configuration selection. A comparison between this approach and one we have previously proposed shows that our method produces results that are as good as those obtained with our previous method while being generic and requiring fewer parameters.

The pulvinar of the thalamus is a higher-order thalamic nucleus that is responsible for gating information flow to the cortical regions of the brain. It is involved in several cortico-thalamocortical relay circuits and is known to be affected in a number of neurological disorders. Segmenting the pulvinar in clinically acquired images is important to support studies exploring its role in brain function. In recent years, we have proposed an active shape model method to segment multiple thalamic nuclei, including the pulvinar. The model was created by manual delineation of high resolution 7T images and the process was guided by the Morel stereotactic atlas. However, this model is based on a small library of healthy subjects, and it is important to validate the reliability of the segmentation method on a larger population of clinically acquired images. The pulvinar is known to have particularly strong white matter connections to the hippocampus, which allows us to identify the pulvinar from thalamic regions of high hippocampal structural connectivity. In this study, we obtained T1-weighted and diffusion MR data from 43 healthy volunteers using a clinical 3T MRI scanner. We applied the segmentation method to the T1-weighted images to obtain the intrathalamic nuclei, and we calculated the connectivity maps between the hippocampus and thalamus using the diffusion images. Our results show that the shape model segmentation consistently localizes the pulvinar in the region with the highest hippocampal connectivity. The proposed method can be extended to other nuclei to further validate our segmentation method.

Background: Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT)., Purpose: To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of k -means clustering, implemented it on each model, and evaluated the agreement with histology., Method: An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm 2 . K -means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included., Results: Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei., Conclusion: dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to stimulate nerve endings to replace the natural electro-mechanical transduction mechanism and restore hearing for patients with profound hearing loss. Post-operatively, the CI needs to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and relies on the patient's subjective response to stimuli. This is a trial-and-error process that can be frustratingly long (dozens of programming sessions are not unusual). To assist audiologists, we have proposed what we call IGCIP for image-guided cochlear implant programming. In IGCIP, we use image processing algorithms to segment the intra-cochlear anatomy in pre-operative CT images and to localize the electrode arrays in post-operative CTs. We have shown that programming strategies informed by image-derived information significantly improve hearing outcomes for both adults and pediatric populations. We are now aiming at deploying these techniques clinically, which requires full automation. One challenge we face is the lack of standard image acquisition protocols. The content of the image volumes we need to process thus varies greatly and visual inspection and labelling is currently required to initialize processing pipelines. In this work we propose a deep learning-based approach to automatically detect if a head CT volume contains two ears, one ear, or no ear. Our approach has been tested on a data set that contains over 2,000 CT volumes from 153 patients and we achieve an overall 95.97% classification accuracy.

Adult cochlear implant (CI) recipients demonstrate a reliable relationship between spectral modulation detection and speech understanding. Prior studies documenting this relationship have focused on postlingually deafened adult CI recipients-leaving an open question regarding the relationship between spectral resolution and speech understanding for adults and children with prelingual onset of deafness. Here, we report CI performance on the measures of speech recognition and spectral modulation detection for 578 CI recipients including 477 postlingual adults, 65 prelingual adults, and 36 prelingual pediatric CI users. The results demonstrated a significant correlation between spectral modulation detection and various measures of speech understanding for 542 adult CI recipients. For 36 pediatric CI recipients, however, there was no significant correlation between spectral modulation detection and speech understanding in quiet or in noise nor was spectral modulation detection significantly correlated with listener age or age at implantation. These findings suggest that pediatric CI recipients might not depend upon spectral resolution for speech understanding in the same manner as adult CI recipients. It is possible that pediatric CI users are making use of different cues, such as those contained within the temporal envelope, to achieve high levels of speech understanding. Further investigation is warranted to investigate the relationship between spectral and temporal resolution and speech recognition to describe the underlying mechanisms driving peripheral auditory processing in pediatric CI users.

Objectives: 1) Examine angular insertion depths (AID) and scalar location of Med-El (GmbH Innsbruck, Austria) electrodes; and 2) determine the relationship between AID and audiologic outcomes controlling for scalar position., Study Design: Retrospective review., Methods: Postlingually deafened adults undergoing cochlear implantation with Flex 24, Flex 28, and Standard electrode arrays (Med-El) were identified. Patients with preoperative and postoperative computed tomography scans were included so that electrode location and AID could be determined. Outcome measures were 1) speech perception in the cochlear implant (CI)-only condition, and 2) short-term hearing preservation., Results: Forty-eight implants were included; all electrodes (48 of 48) were positioned entirely within the scala tympani. The median AID was 408° (interquartile [IQ] range 373°-449°) for Flex 24, 575° (IQ range 465°-584°) for Flex 28, and 584° (IQ range 368°-643°) for Standard electrodes (Med-El). The mean postoperative CNC score was 43.7% ± 21.9. A positive correlation was observed between greater AID and better CNC performance (r = 0.48, P < 0.001). Excluding patients with postoperative residual hearing, a strong correlation between AID and CNC persisted (r = 0.57, P < 0.001). In patients with preoperative residual hearing, mean low-frequency pure-tone average (PTA) shift was 27 dB ± 14. A correlation between AID and low-frequency PTA shift at activation was noted (r = 0.41, P = 0.04)., Conclusion: Favorable rates of scala tympani insertion (100%) were observed. In the CI-only condition, a direct correlation between greater AID and CNC score was noted regardless of postoperative hearing status. Deeper insertions were, however, associated with worse short-term hearing preservation. When patients without postoperative residual hearing were analyzed independently, the relationship between greater insertion depth and better performance was strengthened., Level of Evidence: 4. Laryngoscope, 127:2352-2357, 2017., (© 2016 The American Laryngological, Rhinological and Otological Society, Inc.)

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to stimulate frequency-mapped nerve endings to treat patients with hearing loss. CIs are programmed postoperatively by audiologists using behavioral tests without information on electrode-cochlea spatial relationship. We have recently developed techniques to segment the intracochlear anatomy and to localize individual contacts in clinically acquired computed tomography (CT) images. Using this information, we have proposed a programming strategy that we call image-guided CI programming (IGCIP), and we have shown that it significantly improves outcomes for both adult and pediatric recipients. One obstacle to large-scale deployment of this technique is the need for manual intervention in some processing steps. One of these is the rough registration of images prior to the use of automated intensity-based algorithms. Although seemingly simple, the heterogeneity of our image set makes this task challenging. We propose a solution that relies on the automated random forest-based localization of multiple landmarks used to estimate an initial transformation with a point-based registration method. Results show that it produces results that are equivalent to a manual initialization. This work is an important step toward the full automation of IGCIP.

Cochlear implants (CIs) are surgically implantable neuroprosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the final intracochlear positioning of the CI electrode arrays and the ultimate hearing outcome of the patient, indicating that further studies to better understand the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods. Postimplantation high-resolution computed tomography (CT) imaging is the best modality for localizing electrodes and provides the resolution necessary to visually identify electrode position, although with an unknown degree of accuracy depending on image acquisition parameters, like the hounsfield unit (HU) range of reconstruction, orientation, radiation dose, and image resolution. We report on the development of a phantom and on its use to study how four acquisition parameters, including image resolution and HU range of reconstruction, affect how accurately the true position of the electrodes can be found in a dataset of CT scans acquired from multiple helical and cone beam scanners. We also show how the phantom can be used to evaluate the effect of acquisition parameters on automatic electrode localization techniques.

Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1) identifying a set of landmarks that can be reliably localized in the images and (2) selecting among them the subset that leads to a good initial transformation. To validate our method, we use it to initialize five well-established deformable registration algorithms that are subsequently used to register an atlas to MR images of the head. We compare our proposed initialization method with a standard approach that involves estimating an affine transformation with an intensity-based approach. We show that for all five registration algorithms the final registration results are statistically better when they are initialized with the method that we propose than when a standard approach is used. The technique that we propose is generic and could be used to initialize nonrigid registration algorithms for other applications.

Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. When comparing our tracked microscope stereo-pair measure of mock vessel displacements to that of the measurement determined by the independent optically tracked stylus marking, the displacement error was [Formula: see text] on average. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to laser range scanners to collect sufficient intraoperative information for brain shift correction.

Objective Precurved electrode arrays (EAs) are commonly used in cochlear implants (CIs). Modiolar placement of such arrays has been shown to lead to better hearing outcomes. In this project, we retrospectively evaluated the modiolar positioning of EAs within a large CI imaging database. We aimed to discover the rate at which perimodiolar placement is successfully achieved and to evaluate a new technique we propose to preoperatively plan patient-customized EA insertion depths to improve perimodiolar placement at the time of surgery. Study Design Retrospective chart review and radiographic analysis. Setting Single tertiary academic referral center. Subjects and Methods Ninety-seven CI ears were evaluated. Perimodiolar positioning of electrodes was quantified using pre- and postimplantation computed tomography scans and automated image analysis techniques. Results Average perimodiolar distance was 0.59 ± 0.18 mm. Disagreement between the actual and our recommended insertion depth was found to be positively correlated with perimodiolar distance ( r = 0.49, P < .0001). Conclusions These results show that the average CI recipient with a precurved EA has a number of electrodes distant to the modiolus where they are not most effective. Our results also indicate the approach we propose for selecting patient-customized EA insertion depth would lead to better perimodiolar placement of precurved EAs.

Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms., Methods: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands., Results: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed., Conclusions: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms., (© 2017 American Association of Physicists in Medicine.)

Frontal-basal ganglia circuitry dysfunction caused by Parkinson's disease impairs important executive cognitive processes, such as the ability to inhibit impulsive action tendencies. Subthalamic Nucleus Deep Brain Stimulation in Parkinson's disease improves the reactive inhibition of impulsive actions that interfere with goal-directed behavior. An unresolved question is whether this effect depends on stimulation of a particular Subthalamic Nucleus subregion. The current study aimed to 1) replicate previous findings and additionally investigate the effect of chronic versus acute Subthalamic Nucleus stimulation on inhibitory control in Parkinson's disease patients off dopaminergic medication 2) test whether stimulating Subthalamic Nucleus subregions differentially modulate proactive response control and the proficiency of reactive inhibitory control. In the first experiment, twelve Parkinson's disease patients completed three sessions of the Simon task, Off Deep brain stimulation and medication, on acute Deep Brain Stimulation and on chronic Deep Brain Stimulation. Experiment 2 consisted of 11 Parkinson's disease patients with Subthalamic Nucleus Deep Brain Stimulation (off medication) who completed two testing sessions involving of a Simon task either with stimulation of the dorsal or the ventral contact in the Subthalamic Nucleus. Our findings show that Deep Brain Stimulation improves reactive inhibitory control, regardless of medication and regardless of whether it concerns chronic or acute Subthalamic Nucleus stimulation. More importantly, selective stimulation of dorsal and ventral subregions of the Subthalamic Nucleus indicates that especially the dorsal Subthalamic Nucleus circuitries are crucial for modulating the reactive inhibitory control of motor actions., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Our objective was to prospectively evaluate implementation of a new cochlear implant (CI) mapping technique, image-guided cochlear implant programming (IGCIP), at a site distant to the site of development. IGCIP consists of identifying the geometric relationship between CI electrodes and the modiolus and deactivating electrodes that interfere with neighboring electrodes. IGCIP maps for 17 ears of 15 adult CI patients were developed at a central image-processing center, Vanderbilt, and implemented at a distant tertiary care center, House Ear Institute. Before IGCIP and again 4 weeks after, qualitative and quantitative measures were made. While there were no statistically significant groupwise differences detected between baseline and IGCIP qualitative or quantitative measures, 11 of the 17 (64.7%) elected to keep the IGCIP map. Computed tomography (CT) image quality appears to be crucial for successful IGCIP, with 100% of those with high-resolution CT scans keeping their maps compared to 53.8% without.

Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR's drilling region using preoperative CT requires expertise and takes about 35 min. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from nine subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 min were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon's planning effort.

Hypothesis: Cochlear duct length (CDL) can be automatically measured for custom selection of cochlear implant (CI) electrode arrays., Background: CI electrode array selection can be influenced by measuring the CDL, which is estimated based on the length of the line that connects the round window and the lateral wall of the cochlea when passing through the modiolus. CDL measurement remains time consuming and inter-observer variability has not been studied., Methods: We evaluate an automatic approach to directly measure the two-turn (2T) CDL using existing algorithms for localizing cochlear anatomy in computed tomography (CT). Pre-op CT images of 309 ears were evaluated. Two fellowship-trained neurotologists manually and independently measured CDL. Inter-observer variability between measurements across expert and automatic observers is assessed. Inter-observer differences for choice of electrode type are also investigated., Results: Manual measurement of CDL by experts tends to underestimate cochlea size and has high inter-observer variability, with mean absolute differences between expert CDL estimations of 1.15 mm. Our results show that this can lead to a large number of cochleae for which a different electrode array type would be selected by different observers, depending on the specific threshold value of CDL used to decide between array type., Conclusion: Choosing the best CI electrode array is an important task for optimizing hearing outcomes. Manual cochleae length measurements are user-dependent, and errors impact upon the CI electrode array choice for certain patients. Measuring cochlea length automatically is less time consuming and generates more repeatable results. Our automatic approach could make use of CDL for patient-customized treatment more clinically adoptable.

The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson's disease. In order to manually trace these structures, a combination of high-resolution and specialized sequences at 7T are used, but it is not feasible to scan clinical patients in those scanners. Targeted imaging sequences at 3T such as F-GATIR, and other optimized inversion recovery sequences, have been presented which enhance contrast in a select group of these structures. In this work, we show that a series of atlases generated at 7T can be used to accurately segment these structures at 3T using a combination of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the thalamus and putamen, a median Dice coefficient over 0.88 and a mean surface distance less than 1.0mm was achieved using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus pallidus a Dice over 0.75 and a mean surface distance less than 1.2mm was achieved using a combination of T1 and F-GATIR imaging sequences. In the substantia nigra and sub-thalamic nucleus a Dice coefficient of over 0.6 and a mean surface distance of less than 1.0mm was achieved using the optimized inversion recovery imaging sequence. On average, using T1 and optimized inversion recovery together produced significantly improved segmentation results than any individual modality (p<0.05 wilcox sign-rank test).

Objective: To describe the incidence, clinical presentation, and performance of cochlear implant (CI) recipients with tip fold-over., Study Design: Retrospective case series., Setting: Tertiary referral center., Patients: CI recipients who underwent postoperative computed tomography (CT) scanning., Intervention(s): Tip fold-over was identified tomographically using previously validated software that identifies the electrode array. Electrophysiologic testing including spread of excitation or electric field imaging (EFI) was measured on those with fold-over., Main Outcome Measure(s): Location of the fold-over; audiological performance pre and postselective deactivation of fold-over electrodes., Results: Three hundred three ears of 235 CI recipients had postoperative CTs available for review. Six (1.98%) had tip fold-over with 5/6 right-sided ears. Tip fold-over occurred predominantly at 270 degrees and was associated with precurved electrodes (5/6). Patients did not report audiological complaints during initial activation. In one patient, the electrode array remained within the scala tympani with preserved residual hearing despite the fold-over. Spread of excitation supported tip fold-over, but the predictive value was not clear. EFI predicted location of the fold-over with clear predictive value in one patient. At an average follow-up of 11 months, three subjects underwent deactivation of the overlapping electrodes with two of them showing marked audiological improvement., Conclusion: In a large academic center with experienced surgeons, tip fold-over occurred at a rate of 1.98% but was not immediately identifiable clinically. CT imaging definitively showed tip fold-over. Deactivating involved electrodes may improve performance possibly avoiding revision surgery. EFI may be highly predictive of tip fold-over and can be run intraoperatively, potentially obviating the need for intraop fluoroscopy.

Objective: To investigate the accuracy of rigid cochlear models in measuring intra-cochlear positions of cochlear implant (CI) electrodes., Patients: Ninety three adults who had undergone CI and pre- and postoperative computed tomographic (CT) imaging., Main Outcome Measures: Seven rigid models of cochlear anatomy were constructed using micro-CTs of cochlear specimens. Using each of the seven models, the position of each electrode in each of the 98 ears in our dataset was measured as its depth along the length of the cochlea, its distance to the basilar membrane, and its distance to the modiolus. Cochlear duct length was also measured using each model., Results: Standard deviation (SD) across rigid cochlear models in measures of electrode depth, distance to basilar membrane, distance to modiolus, and length of the cochlear duct at two turns were 0.68, 0.11, 0.15, and 1.54 mm. Comparing the estimated position of the electrodes with respect to the basilar membrane, i.e., deciding whether an electrode was located within the scala tympani (ST) or the scala vestibuli (SV), there was not a unanimous agreement between the models for 19% of all the electrodes. With respect to the modiolus, each electrode was classified into one of the three groups depending on its modiolar distance: close, medium, and far. Rigid models did not unanimously agree on modiolar distance for approximately 50% of the electrodes tested., Conclusions: Inter-model variance of rigid cochlear models exists, demonstrating that measurements made using rigid cochlear models are limited in terms of accuracy because of non-rigid inter-subject variations in cochlear anatomy., Competing Interests: Conflicts of Interest/Disclosures: Dr. Labadie is a consultant for Advanced Bionics, and Ototronix.

Objectives: 1) Investigate the impact of electrode type and surgical approach on scalar electrode location; and 2) examine the relation between electrode location and postoperative audiologic performance., Setting: Tertiary academic hospital., Patients: Two hundred twenty post-lingually deafened adults undergoing cochlear implant (CI)., Main Outcome Measures: Primary outcome measures of interest were scalar electrode location and postoperative audiologic performance., Results: In 68% of implants, electrodes were observed to be located solely in the scala tympani (ST). Multivariate analysis demonstrated perimodiolar (PM) and mid-scala (MS) electrodes were 22.4 (95% CI: 6.3-80.0, p < 0.001) and 55.0 (95% CI: 9.7-312.8, p < 0.001) times more likely to have at least one electrode in the scala vestibuli (SV) compared with lateral wall (LW) electrodes, respectively. Compared with cochleostomy (C), round window (RW) and extended round window (ERW) approaches demonstrated 70% reduction in SV insertion (RW: OR 0.28, 95% CI: 0.1-0.8, p = 0.01; ERW: OR 0.28, 95% CI: 0.1-0.7, p = 0.005). Examining postoperative audiometric performance, consonant-nucleus-consonant (CNC) score increased 0.6% with every 10 degrees increase in angular insertion depth beyond the group minimum of 208 degrees (coefficient 0.0006, 95% CI: 0.0001-0.001, p = 0.03). SV insertion was associated with a 12% decrease in CNC score (coefficient -0.12, 95% CI: -0.22 to -0.02, p = 0.02). CNC score decreased 0.3% for every 1 year increase in age (coefficient -0.003, 95% CI: -0.006 to -0.0006, p = 0.02)., Conclusions: Electrode design and surgical approach were predictors of scalar electrode location. Specifically, LW electrodes showed higher rates of ST insertion compared with PM or MS. RW and ERW approaches showed higher rates of ST insertion when compared with C. In regards to performance, ST insertion, younger age, and greater angular insertion depth were predictors of improved CNC scores.