Cellular and Molecular Probing of Intact Human Organs.

Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-aged tissues. To overcome these challenges, we developed SHANEL, a method based on a new tissue permeabilization approach to clear and label stiff human organs. We used SHANEL to render the intact adult human brain and kidney transparent and perform 3D histology with antibodies and dyes in centimeters-depth. Thereby, we revealed structural details of the intact human eye, human thyroid, human kidney, and transgenic pig pancreas at the cellular resolution. Furthermore, we developed a deep learning pipeline to analyze millions of cells in cleared human brain tissues within hours with standard lab computers. Overall, SHANEL is a robust and unbiased technology to chart the cellular and molecular architecture of large intact mammalian organs.

Quantification of murine myocardial infarct size using 2-D and 4-D high-frequency ultrasound.

Ischemic heart disease is the leading cause of death in the United States, Canada, and worldwide. Severe disease is characterized by coronary artery occlusion, loss of blood flow to the myocardium, and necrosis of tissue, with subsequent remodeling of the heart wall, including fibrotic scarring. The current study aims to demonstrate the efficacy of quantitating infarct size via two-dimensional (2-D) echocardiographic akinetic length and four-dimensional (4-D) echocardiographic infarct volume and surface area as in vivo analysis techniques. We further describe and evaluate a new surface area strain analysis technique for estimating myocardial infarction (MI) size after ischemic injury. Experimental MI was induced in mice via left coronary artery ligation. Ejection fraction and infarct size were measured through 2-D and 4-D echocardiography. Infarct size established via histology was compared with ultrasound-based metrics via linear regression analysis. Two-dimensional echocardiographic akinetic length (r = 0.76, P = 0.03), 4-D echocardiographic infarct volume (r = 0.85, P = 0.008), and surface area (r = 0.90, P = 0.002) correlate well with histology. Although both 2-D and 4-D echocardiography were reliable measurement techniques to assess infarct, 4-D analysis is superior in assessing asymmetry of the left ventricle and the infarct. Strain analysis performed on 4-D data also provides additional infarct sizing techniques, which correlate with histology (surface strain: r = 0.94, P < 0.001, transmural thickness: r = 0.76, P = 0.001). Two-dimensional echocardiographic akinetic length, 4-D echocardiography ultrasound, and strain provide effective in vivo methods for measuring fibrotic scarring after MI.NEW & NOTEWORTHY Our study supports that both 2-D and 4-D echocardiographic analysis techniques are reliable in quantifying infarct size though 4-D ultrasound provides a more holistic image of LV function and structure, especially after myocardial infarction. Furthermore, 4-D strain analysis correctly identifies infarct size and regional LV dysfunction after MI. Therefore, these techniques can improve functional insight into the impact of pharmacological interventions on the pathophysiology of cardiac disease.

Three-Dimensional Imaging Provides Detailed Atherosclerotic Plaque Morphology and Reveals Angiogenesis After Carotid Artery Ligation.

RATIONALE: Remodeling of the vessel wall and the formation of vascular networks are dynamic processes that occur during mammalian embryonic development and in adulthood. Plaque development and excessive neointima formation are hallmarks of atherosclerosis and vascular injury. As our understanding of these complex processes evolves, there is a need to develop new imaging techniques to study underlying mechanisms. OBJECTIVE: We used tissue clearing and light-sheet microscopy for 3-dimensional (3D) profiling of the vascular response to carotid artery ligation and induction of atherosclerosis in mouse models. METHODS AND RESULTS: Adipo-Clear and immunolabeling in combination with light-sheet microscopy were applied to image carotid arteries and brachiocephalic arteries, allowing for 3D reconstruction of vessel architecture. Entire 3D neointima formations with different geometries were observed within the carotid artery and scored by volumetric analysis. Additionally, we identified a CD31-positive adventitial plexus after ligation of the carotid artery that evolved and matured over time. We also used this method to characterize plaque extent and composition in the brachiocephalic arteries of ApoE-deficient mice on high-fat diet. The plaques exhibited inter-animal differences in terms of plaque volume, geometry, and ratio of acellular core to plaque volume. A 3D reconstruction of the endothelium overlying the plaque was also generated. CONCLUSIONS: We present a novel approach to characterize vascular remodeling in adult mice using Adipo-Clear in combination with light-sheet microscopy. Our method reconstructs 3D neointima formation after arterial injury and allows for volumetric analysis of remodeling, in addition to revealing angiogenesis and maturation of a plexus surrounding the carotid artery. This method generates complete 3D reconstructions of atherosclerotic plaques and uncovers their volume, geometry, acellular component, surface, and spatial position within the brachiocephalic arteries. Our approach may be used in a number of mouse models of cardiovascular disease to assess vessel geometry and volume. Visual Overview: An online visual overview is available for this article.

Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting.

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.

Deep representation learning for domain adaptable classification of infrared spectral imaging data.

MOTIVATION: Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes. RESULTS: We investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch. AVAILABILITY AND IMPLEMENTATION: Our implementation can be downloaded from https://github.com/arnrau/SCAE_IR_Spectral_Imaging. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

External validation of automated focal cortical dysplasia detection using morphometric analysis.

OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric analysis of T1-weighted images, for example, using the Morphometric Analysis Program (v2018; MAP18), which was introduced in 2005, independently validated for its clinical benefits, and successfully integrated in standard presurgical workflows of numerous epilepsy centers worldwide. Here we aimed to develop an artificial neural network (ANN) classifier for robust automated detection of FCDs based on these morphometric maps and probe its generalization performance in a large, independent data set. METHODS: In this retrospective study, we created a feed-forward ANN for FCD detection based on the morphometric output maps of MAP18. The ANN was trained and cross-validated on 113 patients (62 female, mean age ± SD =29.5 ± 13.6 years) with manually segmented FCDs and 362 healthy controls (161 female, mean age ± SD =30.2 ± 9.6 years) acquired on 13 different scanners. In addition, we validated the performance of the trained ANN on an independent, unseen data set of 60 FCD patients (28 female, mean age ± SD =30 ± 15.26 years) and 70 healthy controls (42 females, mean age ± SD = 40.0 ± 12.54 years). RESULTS: In the cross-validation, the ANN achieved a sensitivity of 87.4% at a specificity of 85.4% on the training data set. On the independent validation data set, our method still reached a sensitivity of 81.0% at a comparably high specificity of 84.3%. SIGNIFICANCE: Our method shows a robust automated detection of FCDs and performance generalizability, largely independent of scanning site or MR-sequence parameters. Taken together with the minimal input requirements of a standard T1 image, our approach constitutes a clinically viable and useful tool in the presurgical diagnostic routine for drug-resistant focal epilepsy.

Evaluation of 3-Dimensional Magnetic Resonance Imaging (3D MRI) in Diagnosing Anterior Talofibular Ligament Injury.

BACKGROUND It is challenging to entirely show the anterior talofibular ligament (ATFL) and accurately diagnose ATFL injury with traditional 2-dimensional (2D) magnetic resonance imaging (MRI). With the introduction of 3.0T MRI, a 3-dimensional (3D) MRI sequence can achieve images with high spatial resolution. This study aimed to evaluate the accuracy of 3D MRI and compare it with 2D MRI in diagnosing ATFL injury. MATERIAL AND METHODS This was a prospective study in which 45 patients with clinically suspected ATFL injury underwent 2D MRI, 3D MRI, and 3D model reconstruction followed by arthroscopic surgery between February 2018 and April 2019. Two radiologists who had over 11 and 13 years of musculoskeletal experience assessed the injury of ATFL in consensus without any clinical clues. Arthroscopic surgery results were the standard reference of MRI accuracy. RESULTS The 3D MRI results of ATFL injury showed the sensitivity of diagnosis of complete tears of 83% and specificity of 82%. The partial tears diagnosis sensitivity was 78%, and specificity was 100%. The sensitivity of diagnosis of sprains was 100%, and the specificity was 97%. The 3D MRI accuracy of diagnosis was 98% for no injury, 98% for sprain, 91% for partial tear, and 82% for complete tear. The difference in the diagnosis of sprain and partial tears by 3D MRI and 2D MRI was statistically significant (P<0.05). A 3D reconstruction model was successfully created for all patients. CONCLUSIONS 3D MRI may be a reliable and accurate method to detect ATFL injury. The 3D reconstruction model using 3D MRI sequences has excellent prospects in application.

Navigated Deep Brain Stimulation Surgery: Evaluating the Combined Use of a Frame-Based Stereotactic System and a Navigation System.

Deep brain stimulation (DBS) is a complex surgical procedure that requires detailed anatomical knowledge. In many fields of neurosurgery navigation systems are used to display anatomical structures during an operation to aid performing these surgeries. In frame-based DBS, the advantage of visualization has not yet been evaluated during the procedure itself. In this study, we added live visualization to a frame-based DBS system, using a standard navigation system and investigated its accuracy and potential use in DBS surgery. As a first step, a phantom study was conducted to investigate the accuracy of the navigation system in conjunction with a frame-based approach. As a second step, 5 DBS surgeries were performed with this combined approach. Afterwards, 3 neurosurgeons and 2 neurologists with different levels of experience evaluated the potential use of the system with a questionnaire. Moreover, the additional personnel, costs and required set up time were noted and compared to 5 consecutive standard procedures. In the phantom study, the navigation system showed an inaccuracy of 2.1 mm (mean SD 0.69 mm). In the questionnaire, a mean of 9.4/10 points was awarded for the use of the combined approach as a teaching tool, a mean of 8.4/10 for its advantage in creating a 3-dimensional (3-D) map and a mean of 8/10 points for facilitating group discussions. Especially neurosurgeons and neurologists in training found it useful to better interpret clinical results and side effects (mean 9/10 points) and neurosurgeons appreciated its use to better interpret microelectrode recordings (mean 9/10 points). A mean of 6/10 points was awarded when asked if the benefits were worth the additional efforts. Initially 2 persons, then one additional person was required to set up the system with no relevant added time or costs. Using a navigation system for live visualization during frame-based DBS surgery can improve the understanding of the complex 3-D anatomy and many aspects of the procedure itself. For now, we would regard it as an excellent teaching tool rather than a necessity to perform DBS surgeries.

Time Efficiency in Stereotactic Robot-Assisted Surgery: An Appraisal of the Surgical Procedure and Surgeon's Learning Curve.

BACKGROUND: Frame-based stereotactic procedures are still the gold standard in neurosurgery. However, there is an increasing interest in robot-assisted technologies. Introducing these increasingly complex tools in the clinical setting raises the question about the time efficiency of the system and the essential learning curve of the surgeon. METHODS: This retrospective study enrolled a consecutive series of patients undergoing a robot-assisted procedure after first system installation at one institution. All procedures were performed by the same neurosurgeon to capture the learning curve. The objective read-out were the surgical procedure time (SPT), the skin-to-skin time, and the intraoperative registration time (IRT) after laser surface registration (LSR), bone fiducial registration (BFR), and skin fiducial registration (SFR), as well as the quality of the registration (as measured by the fiducial registration error [FRE]). The time measures were compared to those for a patient group undergoing classic frame-based stereotaxy. RESULTS: In the first 7 months, we performed 31 robot-assisted surgeries (26 biopsies, 3 stereotactic electroencephalography [SEEG] implantations, and 2 endoscopic procedures). The SPT was depending on the actual type of surgery (biopsies: 85.0 ± 36.1 min; SEEG: 154.9 ± 75.9 min; endoscopy: 105.5 ± 1.1 min; p = 0.036). For the robot-assisted biopsies, there was a significant reduction in SPT within the evaluation period, reaching the level of frame-based surgeries (58.1 ± 17.9 min; p < 0.001). The IRT was depending on the applied registration method (LSR: 16.7 ± 2.3 min; BFR: 3.5 ± 1.1 min; SFR: 3.5 ± 1.6 min; p < 0.001). In contrast to BFR and SFR, there was a significant reduction in LSR time during that period (p = 0.038). The FRE differed between the applied registration methods (LSR: 0.60 ± 0.17 mm; BFR: 0.42 ± 0.15 mm; SFR: 2.17 ± 0.78 mm; p < 0.001). There was a significant improvement in LSR quality during the evaluation period (p = 0.035). CONCLUSION: Introducing stereotactic, robot-assisted surgery in an established clinical setting initially necessitates a prolonged intraoperative preparation time. However, there is a steep learning curve during the first cases, reaching the time level of classic frame-based stereotaxy. Thus, a stereotactic robot can be integrated into daily routine within a decent period of time, thereby expanding the neurosurgeons' armamentarium, especially for procedures with multiple trajectories.

Accuracy and Safety of Customized Stereotactic Fixtures for Stereoelectroencephalography in Pediatric Patients.

Stereoelectroencephalography (SEEG) in children with intractable epilepsy presents particular challenges. Their thin and partially ossified cranium, specifically in the temporal area, is prone to fracture while attaching stereotactic systems to the head or stabilizing the head in robot's field of action. Postponing SEEG in this special population of patients can have serious consequences, reducing their chances of becoming seizure-free and impacting their social and cognitive development. This study demonstrates the safety and accuracy offered by a frameless personalized 3D printed stereotactic implantation system for SEEG investigations in children under 4 years of age. SEEG was carried out in a 3-year-old patient with drug-resistant focal epilepsy, based on a right temporal-perisylvian epileptogenic zone hypothesis. Fifteen intracerebral electrodes were placed using a StarFix patient-customized stereotactic fixture. The median lateral entry point localization error of the electrodes was 0.90 mm, median lateral target point localization error was 1.86 mm, median target depth error was 0.83 mm, and median target point localization error was 1.96 mm. There were no perioperative complications. SEEG data led to a tailored right temporal-insular-opercular resection, with resulting seizure freedom (Engel IA). In conclusion, patient-customized stereotactic fixtures are a safe and accurate option for SEEG exploration in young children.

Evaluation of Ductal Tissue in Coarctation of the Aorta Using X-Ray Phase-Contrast Tomography.

We assessed the histological accuracy of X-ray phase-contrast tomography (XPCT) and investigated three-dimensional (3D) ductal tissue distribution in coarctation of the aorta (CoA) specimens. We used nine CoA samples, including the aortic isthmus, ductus arteriosus (DA), and their confluences. 3D images were obtained using XPCT. After scanning, the samples were histologically evaluated using elastica van Gieson (EVG) staining and transcription factor AP-2 beta (TFAP2B) immunostaining. XPCT sectional images clearly depicted ductal tissue distribution as low-density areas. In comparison with EVG staining, the mass density of the aortic wall positively correlated with elastic fiber formation (R = 0.69, P < 0.001). TFAP2B expression was consistent with low-density area including intimal thickness on XPCT images. On 3D imaging, the distances from the DA insertion to the distal terminal of the ductal media and to the intima on the ductal side were 1.63 ± 0.22 mm and 2.70 ± 0.55 mm, respectively. In the short-axis view, the posterior extension of the ductal tissue into the aortic lumen was 79 ± 18% of the diameter of the descending aorta. In three specimens, the aortic wall was entirely occupied by ductal tissue. The ductal intima spread more distally and laterally than the ductal media. The contrast resolution of XPCT images was comparable to that of histological assessment. Based on the 3D images, we conclude that complete resection of intimal thickness, including the opposite side of the DA insertion, is required to eliminate residual ductal tissue and to prevent postoperative re-coarctation.

Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation.

Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain's fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 â€‹Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.

A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features.

BACKGROUND: To evaluate radiomics analysis in neuro-oncologic studies according to a radiomics quality score (RQS) system to find room for improvement in clinical use. METHODS: Pubmed and Embase were searched up the terms radiomics or radiogenomics and gliomas or glioblastomas until February 2019. From 189 articles, 51 original research articles reporting the diagnostic, prognostic, or predictive utility were selected. The quality of the methodology was evaluated according to the RQS. The adherence rates for the six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, a high level of evidence, and open science. Subgroup analyses for journal type (imaging vs. clinical) and biomarker (diagnostic vs. prognostic/predictive) were performed. RESULTS: The median RQS was 11 out of 36 and adherence rate was 37.1%. Only 29.4% performed external validation. The adherence rate was high for reporting imaging protocol (100%), feature reduction (94.1%), and discrimination statistics (96.1%), but low for conducting test-retest analysis (2%), prospective study (3.9%), demonstrating potential clinical utility (2%), and open science (5.9%). None of the studies conducted a phantom study or cost-effectiveness analysis. Prognostic/predictive studies received higher score than diagnostic studies in comparison to gold standard (P < .001), use of calibration (P = .02), and cut-off analysis (P = .001). CONCLUSIONS: The quality of reporting of radiomics studies in neuro-oncology is currently insufficient. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, demonstrating clinical utility, pursuits of a higher level of evidence, and open science.

Ultrasound Based Wall Stress Analysis of Abdominal Aortic Aneurysms using Multiperspective Imaging.

BACKGROUND: Current clinical guidelines for surgical repair of abdominal aortic aneurysms (AAAs) are primarily based on maximum diameter assessment. From a biomechanical point of view, not only the diameter but also peak wall stresses will play an important role in rupture risk assessment. These methods require patient specific geometry which typically uses computed tomography (CT) or magnetic resonance imaging. Recently, wall stress analysis based on 3D ultrasound (US) has been proposed, and shows promising results. However, the major limitations in these studies were the use of manual segmentation and the limiting field of view of US. Therefore in this study, the AAA is imaged with multiperspective 3D ultrasound, merged to obtain a large field of view, and afterwards automatically segmented. Geometry and wall stress results were validated using CT imaging. METHODS: Three dimensional US and CT data were available for 40 AAA patients (maximum diameter 34-61 mm). The full US based AAA geometry was determined using automatic segmentation, and when the aneurysm exceeded a single 3D volume, automatic fusion of multiple 3D US volumes was used. Wall stress analysis was performed for all AAA patients and percentile wall stresses were derived. The accuracy of the US based geometry and wall stress prediction was measured by comparison with CT data. RESULTS: Estimated geometries derived from 3D US and CT data showed good similarity, with an overall median similarity index (SI) of 0.89 and interquartile range of 0.87-0.92, whereas the median Hausdorff distances (HD), a measure for the maximum local mismatch, was 4.6 (4.0-5.9) mm for all AAA geometries. Thereby, the wall stress results based on merged multiperspective 3D US data revealed a greater similarity to CT than single 3D US data. CONCLUSION: This study showed that large volume geometry assessment of AAAs using multiperspective 3D ultrasound, segmentation and fusion, and wall stress analysis is feasible in a robust and labour efficient manner.

3D ultrasound to quantify lateral hip displacement in children with cerebral palsy: a validation study.

AIM: To assess the validity of a new index, lateral head coverage (LHC), for describing hip dysplasia in a population of children with cerebral palsy (CP). METHOD: LHC is derived from 3D ultrasound assessment. Twenty-two children (15 males, seven females; age 4-15y) with CP undergoing routine hip surveillance were recruited prospectively for the study. Each participant had both a planar radiograph acquired as part of their routine care and a 3D ultrasound assessment within 2 months. Reimer's migration percentage (RMP) and LHC were measured by the same assessor, and the correlation between them calculated using Pearson's correlation coefficient. The repeatability of LHC was investigated with three assessors, analysing each of 10 images three times. Inter- and intra-assessor variation was quantified using intraclass correlation coefficients. RESULTS: LHC was strongly correlated with RMP (Spearman's rank correlation coefficient=-0.86, p<0.001). LHC had similar inter-assessor reliability to that reported for RMP (intraclass correlation coefficient=0.97 and intra-assessor intraclass correlation coefficient=0.98). INTERPRETATION: This is an initial validation of the use of 3D ultrasound in monitoring hip development in children with CP. LHC is comparable with RMP in estimating hip dysplasia with similar levels of reliability that are reported for RMP.

Deep Brain Stimulation Lead Implantation Using a Customized Rapidly Manufactured Stereotactic Fixture with Submillimetric Euclidean Accuracy.

BACKGROUND: The microTargetingTM MicrotableTM Platform is a novel stereotactic system that can be more rapidly fabricated than currently available 3D-printed alternatives. We present the first case series of patients who underwent deep brain stimulation (DBS) surgery guided by this platform and demonstrate its in vivo accuracy. METHODS: Ten patients underwent DBS at a single institution by the senior author and 15 leads were placed. The mean age was 69.1 years; four were female. The ventralis intermedius nucleus was targeted for patients with essential tremor and the subthalamic nucleus was targeted for patients with Parkinson's disease. RESULTS: Nine DBS leads in 6 patients were appropriately imaged to enable measurement of accuracy. The mean Euclidean electrode placement error (EPE) was 0.97 ± 0.37 mm, and the mean radial error was 0.80 ± 0.41 mm (n = 9). In the subset of CT scans performed greater than 1 month postoperatively (n = 3), the mean Euclidean EPE was 0.75 ± 0.17 mm and the mean radial error was 0.69 ± 0.17 mm. There were no surgical complications. CONCLUSION: The MicrotableTM platform is capable of submillimetric accuracy in patients undergoing stereotactic surgery. It has achieved clinical efficacy in our patients without surgical complications and has demonstrated the potential for superior accuracy compared to both traditional stereotactic frames and other common frameless systems.

Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm.

STUDY DESIGN: Method development. OBJECTIVES: To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. SETTING: University-based laboratory, Tehran, Iran. METHODS: T2 structural volumes acquired from the spinal cord of 20 uninjured volunteers on a 3T MR scanner. We proposed an automatic method for spinal cord segmentation based on the K-means clustering algorithm in 3D images and compare our results with two available segmentation methods (PropSeg, DeepSeg) implemented in the Spinal Cord Toolbox. Dice and Hausdorff were used to compare the results of our method (K-Seg) with the manual segmentation, PropSeg, and DeepSeg. RESULTS: The accuracy of our automatic segmentation method for T2-weighted images was significantly better or similar to the SCT methods, in terms of 3D DC (p < 0.001). The 3D DCs were respectively (0.81 ± 0.04) and Hausdorff Distance (12.3 ± 2.48) by the K-Seg method in contrary to other SCT methods for T2-weighted images. CONCLUSIONS: The output with similar protocols showed that K-Seg results match the manual segmentation better than the other methods especially on the thoracolumbar levels in the spinal cord due to the low image contrast as a result of poor SNR in these areas.

The accuracy of 3D fluoroscopy (XT) vs computed tomography (CT) registration in deep brain stimulation (DBS) surgery.

BACKGROUND: Stereotactic registration is the most critical step ensuring accuracy in deep brain stimulation (DBS) surgery. 3D fluoroscopy (XT) is emerging as an alternative to CT. XT has been shown to be safe and effective for intraoperative confirmation of lead position following implantation. However, there is a lack of studies evaluating the suitability of XT to be used for the more crucial step of registration and its capability of being merged to a preoperative MRI. This is the first study comparing accuracy, efficiency, and radiation exposure of XT- vs CT-based stereotactic registration and XT/MRI merging in deep brain stimulation. METHODS: Mean absolute differences and Euclidean distance between planned (adjusted for intraoperative testing) and actual lead trajectories were calculated for accuracy of implantation. The radiation dose from each scan was recorded as the dose length product (DLP). Efficiency was measured as the time between the patient entering the operating room and the initial skin incision. A one-way ANOVA compared these parameters between patients that had either CT- or XT-based registration. RESULTS: Forty-one patients underwent DBS surgery-25 in the CT group and 16 in the XT group. The mean absolute difference between CT and XT was not statistically significant in the x (p = 0.331), y (p = 0.951), or z (p = 0.807) directions. The Euclidean distance between patient groups did not differ significantly (p = 0.874). The average radiation exposure with XT (220.0 ± 0.1 mGy*cm) was significantly lower than CT (1269.3 ± 112.9 mGy*cm) (p < 0.001). There was no significant difference in registration time between CT (107.8 ± 23.1 min) and XT (106.0 ± 18.2 min) (p = 0.518). CONCLUSION: XT-based frame registration was shown to result in similar implantation accuracy and significantly less radiation exposure compared with CT. Our results surprisingly showed no significant difference in registration time, but this may be due to a learning curve effect.

Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology.

Based on a recently introduced immunohistochemical panel (Bachmann et al. 2015) for aganglionic megacolon (AM), also known as Hirschsprung disease, histopathological diagnosis, we evaluated whether the use of digital pathology and 'machine learning' could help to obtain a reliable diagnosis. Slides were obtained from 31 specimens of 27 patients immunohistochemically stained for MAP2, calretinin, S100ß and GLUT1. Slides were digitized by whole slide scanning. We used a Definiens Developer Tissue Studios as software for analysis. We configured necessary parameters in combination with 'machine learning' to identify pathological aberrations. A significant difference between AM- and non-AM-affected tissues was found for calretinin (AM 0.55% vs. non-AM 1.44%) and MAP2 (AM 0.004% vs. non-AM 0.07%) staining measurements and software-based evaluations. In contrast, S100ß and GLUT1 staining measurements and software-based evaluations showed no significant differences between AM- and non-AM-affected tissues. However, no difference was found in comparison of suction biopsies with resections. Applying machine learning via an ensemble voting classifier, we achieved an accuracy of 87.5% on the test set. Automated diagnosis of AM by applying digital pathology on immunohistochemical panels was successful for calretinin and MAP2, whereas S100ß and GLUT1 were not effective in diagnosis. Our method suggests that software-based approaches are capable of diagnosing AM. Our future challenge will be the improvement of efficiency by reduction of the time-consuming need for large pre-labelled training data. With increasing technical improvement, especially in unsupervised training procedures, this method could be helpful in the future.

High-dimensional therapeutic inference in the focally damaged human brain.

See Thiebaut de Schotten and Foulon (doi:10.1093/brain/awx332) for a scientific commentary on this article.Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain. The failure to replicate in humans positive interventional effects in experimental animals is thus revealed to have a remediable inferential cause, forcing a radical re-evaluation of therapeutic inference in the human brain.