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Tumor Treating Fields (TTFields) is an FDA approved treatment for specific types of cancer and significantly extends patients’ life. The intensity of the TTFields within the tumor was associated with the treatment outcomes: the larger the intensity the longer the patients are likely to survive. Therefore, it was suggested to optimize TTFields transducer array location such that their intensity is maximized. Such optimization requires multiple computations of TTFields in a simulation framework. However, these computations are typically performed using finite element methods or similar approaches that are time consuming. Therefore, only a limited number of transducer array locations can be examined in practice. To overcome this issue, we have developed a method for fast estimation of TTFields intensity. We have designed and implemented a method that inputs a segmentation of the patient’s head, a table of tissues’ electrical properties and the location of the transducer array. The method outputs a spatial estimation of the TTFields intensity by incorporating a few relevant parameters in a random-forest regressor. The method was evaluated on 10 patients (20 TA layouts) in a leave-one-out framework. The computation time was 1.5 min using the suggested method, and 180–240 min using the commercial simulation. The average error was 0.14 V/cm (SD = 0.06 V/cm) in comparison to the result of the commercial simulation. These results suggest that a fast estimation of TTFields based on a few parameters is feasible. The presented method may facilitate treatment optimization and further extend patients’ life.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T(2)W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Purpose/objective(s) Tumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM). Moreover, TTFields therapy is currently investigated in a phase III clinical trial for the treatment of advanced Non-Small Cell Lung Cancer (NSCLC). Recent studies have shown that larger TTFields dose was associated with longer patients' survival. Therefore, personalized simulations to estimate the dose are performed as part of the patient treatment planning. For MPM and NSCLC treatment, these simulations require the segmentation of all upper torso tissues. A manual segmentation of the torso requires a few dozens of hours per patient and is impractical. Therefore, we have developed a computational method for semi-automatic segmentation of all upper torso tissues that are relevant to TTFields treatment planning. Materials/methods We have incorporated a dataset of 40 CT images of NSCLC patients that underwent TTFields treatment in the lungs for this study. We have utilized threshold-based methods combined with morphological operations, region growing methods and known anatomical spatial relations to automatically identify and segment the lungs, bones, skin, muscle, fat, spinal cavity, costal cartilage, trachea and bronchi. Other structures such as the heart, blood vessels, liver, stomach, spleen, esophagus, diaphragm and intervertebral discs were semi-automatically segmented by using a few reference points that were provided by a human rater. Since there is no gold standard segmentation of the whole torso, an experienced radiation oncologist that is highly familiar with TTFields treatment inspected the results of the algorithm on top of the original CT images. Results The radiation oncologist has confirmed that the semi-automatic segmentation of the torso provides an adequate quality result for TTFields treatment planning for all cases. The segmentation time was reduced to one hour on a typical patient, compared to 20 hours that is the estimated time required for a fully manual segmentation. Conclusion We have presented a method for adequate quality segmentation of the upper torso in a reasonable time to facilitate TTFields treatment planning. In addition, this method facilitates the creation of a dataset for the development of state-of-the-art segmentation methods that utilize deep learning methods.

Purpose/objective(s) Tumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM), and is currently being investigated in a phase III trial for the treatment of brain metastases from non-small cell lung cancer (NSCLC). Increased TTFields dose density at the tumor is associated with longer patient survival. Therefore, estimating dose at the tumor utilizing computational simulations with patient-specific computational models are integral for treatment planning. NSCLC brain metastases are often observed in infratentorial brain regions. In these cases, TTFields arrays may be placed on the head, neck, and upper back. Manual segmentation of this large area is impractical and time consuming. Therefore, we developed a computational method for semi-automatic segmentation of infratentorial tissues relevant to TTFields treatment planning. Materials/methods We incorporated a dataset of 20 head T1w Gad MRIs of patients treated with TTFields with segmentation of the infratentorial area, and 20 CT images that incorporate head, neck and upper torso from a public database for segmentation of extracranial structures. For the segmentation of the infratentorial zone we developed an atlas-based method that deforms a predefined statistical atlas to best fit the patient's MRI. Our method then revises the initial fitting to ensure the segmented structures adhere to known anatomical relations. For supratentorial brain areas, we incorporate a custom atlas-based method. Extra-cranial zones (head, neck and upper torso) were segmented with threshold-based methods for the skin, muscle, fat, bones, and bronchi. User-defined landmarks were used to segment the esophagus and the artery. The spine and CSF were segmented automatically by identifying the relevant area and defining a constant ratio between their diameters. To evaluate the segmentation of the infratentorial regions, a trained annotator manually segmented the infratentorial area of the 20 head MRIs. We compared manual and automatic segmentations and measured the Dice overlap score. Results The average Dice coefficient between the human annotator and the algorithms' segmentation was 0.84 (SD = 0.05) for cerebellum and 0.67 (SD = 0.05) for brainstem. These results are comparable to those observed in supratentorial regions with a method that was verified previously. The radiation oncologist confirmed that the semi-automatic segmentation of the infratentorial, and extracranial head, neck and upper torso provides a quality result for TTFields treatment planning in a reasonable amount of time for all cases. The infratentorial segmentation is fully automatic and typically lasts a few minutes. Segmentation of extracranial structures is semi-automatic and typically lasts less than an hour. Conclusion We have developed a fast method for the segmentation of infratentorial structures for TTFields planning.

Introduction: Tumor Treating Fields (TTFields) is a treatment modality for glioblastoma multiforme (GBM) and other malignant tumors, utilizing low-intensity, intermediate frequency (100-300kHz) alternating electric field delivered through noninvasive transducer arrays. TTFields are currently being tested in a phase III clinical trial for the treatment of advanced Non-Small Cell Lung Cancer (NSCLC). Previous studies have demonstrated that the treatment efficacy increases when the dose of TTFields delivered to the tumor is at least 1 V/cm. Thus, personalized simulations to estimate the dose are performed as part of the patient treatment planning. For NSCLC treatment, these simulations require the segmentation of torso tissues. We are currently developing a semi-automatic software for the segmentation of these tissues. Achieving highly accurate segmentation is time consuming and not always necessary. Therefore, in this study we assess the level of segmentation accuracy that is required for TTFields dose estimation. Methods: We introduce a novel pipeline for evaluating the sensitivity of TTFields treatment planning errors in the segmentation of torso tissues. To this end, we incorporated an expert-segmented torso volume and artificially placed NSCLC tumors of different sizes in the vicinity of the tested torso tissue. We employed relaxations to the segmentation algorithm and derived the TTFields distribution with and without the simulated segmentation errors. We then computed the treatment planning error as the per-voxel average deviation of the TTFields dose computed over the erroneous segmentation from the reference one. The dose was calculated over the simulated tumor location and over the entire lung volume. Results: We have evaluated our sensitivity analysis pipeline on 9 different test cases. Based on previous analysis, we have set our tolerable target error to be 0.1 V/cm. Our results show that 6 out of the 9 tests were within the target, whereas the other 3 exceeded the target, requiring algorithmic refinements. Conclusions: We have presented a method for estimating the sensitivity of lung cancer TTFields treatment planning to segmentation errors. This method is used to evaluate the required accuracy of the semi-automatic torso segmentation tool that we are currently developing. Our method can be further extended to other types of tumors in different regions of the body. Citation Format: Hadas Ben Atya, Oren Peles, Reuben Shamir, Zeev Bomzon. Lung cancer TTFields treatment planning sensitivity to errors in torso segmentation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3070.

Introduction: Tumor treating fields (TTFields) is an FDA approved treatment for the management of glioblastoma multiform (GBM) and mesothelioma (MPM) and is associated with a significant extension to patients' survival. We have developed software to optimize the location of the TTFields transducer arrays (TAs) on GBM patients' head to maximize the field in the tumor region. To that end, we first segment the patients' head MRIs into five normal and three abnormal tissues. However, the current method for head segmentation was found to be less accurate at the infratentorial regions, mark some sinuses with cerebrospinal fluid (CSF), and does not identify the cerebellum and brain-stem. Therefore, it is assuming the infratentorial structures have the same electrical properties as in the cerebrum. Methods: We have developed a new method for segmentation of the head MRI that overcomes these limitations. As in the previous method, we have incorporated an atlas that is composed of an MRI image and tissue probability maps (TPM). The TPM assign each voxel in the atlas MRI with a value in the range of 0-1. This value reflects the probability that the specific tissue resides in that voxel. We have generated new customized TPMs for the cerebellum, brain stem and sinuses. Moreover, we have carefully revised the other TPMs to ensure best results. Last, we have incorporated these TPMs in a new atlas-based segmentation method. We have validated the method on 10 GBM patients T1w +gad head MRIs. Results: The average Dice coefficient between gold-standard and algorithms' segmentation was 68.4% (SD=11.2%) that is similar to the value observed with the current method implemented in the treatment-planning software (67.8%; SD=10.6%). However, the new method presented here extends the current method with the cerebellum (average Dice = 84%) and brainstem (average Dice = 67%). Moreover, the sinuses are marked with air, and erroneous CSF segments in the sinuses were removed. Conclusions: We have presented a novel method for the segmentation of the head that facilitates accurate infratentorial and supratentorial structures' segmentation. Moreover, the method eliminates erroneous CSF segmentation in the sinuses. These improvements pave the way for TTFields planning for infratentorial tumors. Citation Format: Yana Glozman, Reuben R. Shamir, Zeev Bomzon. A method for infratentorial structures segmentation for tumor treating fields treatment planning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3071.

Purpose: To compare four visual acuity (VA) scoring termination rules.Methods: A computer simulation generated 30,000 virtual patients who underwent 10 repetitions for each of four termination rule...

ObjectiveOverlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure’s size. Addressing these challenges is the objective of this work.MethodsWe extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC ≤1 and cDC = 1 if-and-only-if the structures’ overlap is complete, and; 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brain-stimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus.ResultsPartial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively.ConclusionThe cDC is well defined for probabilistic segmentation, less biased to structure’s size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

The ability of glioma cells to invade adjacent brain tissue remains a major obstacle to therapeutic disease management. Therefore, the development of novel treatment modalities that disrupt glioma cell motility could facilitate greater disease control. Tumor Treating Fields (TTFields), encompassing alternating electric fields within the intermediate frequency range, is an anticancer treatment delivered to the tumor region through transducer arrays placed non-invasively on the skin. This novel loco-regional treatment has demonstrated efficacy and safety and is FDA-approved in patients with glioblastoma and malignant pleural mesothelioma. TTFields are currently being investigated in other solid tumors in ongoing trials, including the phase 3 METIS trial (brain metastases from NSCLC; NCT02831959). Although established as an anti-mitotic treatment, the anti-metastatic potential of TTFields and its effects on cytoskeleton rapid dynamics during cellular motility warrant further investigation. Previous studies have demonstrated that TTFields inhibits metastatic properties of cancer cells. Identification of a unifying mechanism connecting the versatile TTFields-induced molecular responses is required to optimize the therapeutic potential of TTFields. In this study, confocal microscopy, computational tools, and biochemical analyses were utilized to show that TTFields disrupt glioma cellular polarity by interfering with microtubule assembly and directionality. Under TTFields application, changes in microtubule organization resulted in activation of GEF-H1, which led to an increase in active RhoA levels and consequent focal adhesion formation with actin cytoskeleton architectural changes. Furthermore, the optimal TTFields frequency for inhibition of invasion in glioma cells was 300 kHz, which differed from the optimal anti-mitotic frequency leading to glioma cell death of 200 kHz. The inhibitory effect of TTFields on migration was observed at fields intensities of 0.6 V/cm RMS (below the threshold of 1 V/cm RMS previously reported for cytotoxic effects). Together, these data identify discrete TTFields effects that disrupt processes crucial for glioma cell motility.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). OBJECTIVE: To validate the clinical application accuracy of the 7 T-ML method by comparing it with identification of the STN based on intraoperative microelectrode recordings. METHODS: Sixteen PD patients who underwent microelectrode-recordings guided STN DBS were included in this study (30 implanted leads and electrode trajectories). The length of the STN along the electrode trajectory and the position of its contacts to dorsal, inside, or ventral to the STN were compared using microelectrode-recordings and the 7 T-ML method computed based on the patient's clinical 3T MRI. RESULTS: All 30 electrode trajectories that intersected the STN based on microelectrode-recordings, also intersected it when visualized with the 7 T-ML method. STN trajectory average length was 6.2 ± 0.7 mm based on microelectrode recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We observed a 93% agreement regarding contact location between the microelectrode-recordings and the 7 T-ML method. CONCLUSION: The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.

Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of glioblastoma multiform (GBM), malignant pleural mesothelioma (MPM), and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient’s shaved scalp. The positions of the TAs are associated with treatment outcomes via simulations of the electric fields. Therefore, we are currently developing a method for recommending optimal placement of TAs. A key step to achieve this goal is to correctly segment the head into tissues of similar electrical properties. Visual inspection of segmentation quality is invaluable but time-consuming. Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user and indicate if the segmented method is of sufficient accuracy for TTFields simulation. As a first step in this direction, we identified a set of features that are relevant to atlas-based segmentation and show that these are significantly correlated (p < 0.05) with a similarity measure between gold-standard and automatically computed segmentations. Furthermore, we incorporated these features in a decision tree regressor to predict the similarity of the gold-standard and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average absolute difference 3% (SD = 3%); r = 0.92, p < 0.001). We conclude that automatic quality estimation of segmentations is feasible by incorporating segmentation-relevant features with statistical and machine learning methods, such as decision tree regressor.

Deep brain stimulation (DBS) has evolved into a powerful clinical therapy for a range of neurological disorders, but even with impressive clinical growth, DBS technology has been relatively stagnant over its history. However, enhanced collaborations between neural engineers, neuroscientists, physicists, neurologists, and neurosurgeons are beginning to address some of the limitations of current DBS technology. These interactions have helped to develop novel ideas for the next generation of clinical DBS systems. This review attempts collate some of that progress and with two goals in mind. First, provide a general description of current clinical DBS practices, geared toward educating biomedical engineers and computer scientists on a field that needs their expertise and attention. Second, describe some of the technological developments that are currently underway in surgical targeting, stimulation parameter selection, stimulation protocols, and stimulation hardware that are being directly evaluated for near term clinical application.

Aim To compare accuracy, reproducibility and test duration for the Snellen and the Early Treatment Diabetic Retinopathy Study (ETDRS) charts, two main tools used to measure visual acuity (VA). Methods A computer simulation was programmed to run multiple virtual patients, each with a unique set of assigned parameters, including VA, false-positive and false-negative error values. For each virtual patient, assigned VA was randomly chosen along a continuous scale spanning the range between 1.0 to 0.0 logMAR units (equivalent to 20/200 to 20/20). Each of 30 000 virtual patients were run ten times on each of the two VA charts. Results Average test duration (expressed as the total number of characters presented during the test ±SD) was 12.6±11.1 and 31.2±14.7 characters, for the Snellen and ETDRS, respectively. Accuracy, defined as the absolute difference (± SD) between the assigned VA and the measured VA, expressed in logMAR units, was superior in the ETDRS charts: 0.12±0.14 and 0.08±0.08, for the Snellen and ETDRS charts, respectively. Reproducibility, expressed as test-retest variability, was superior in the ETDRS charts: 0.23±0.17 and 0.11±0.09 logMAR units, for the Snellen and ETDRS charts, respectively. Conclusion A comparison of true (assigned) VA to measured VA, demonstrated, on average, better accuracy and reproducibility of the ETDRS chart, but at the penalty of significantly longer test duration. These differences were most pronounced in the low VA range. The reproducibility using a simulation approach is in line with reproducibility values found in several clinical studies.

Purpose: The authors present and evaluate a new preoperative planning method and computer software designed to reduce the risk of candidate trajectories for straight rigid tool insertion in image-guided keyhole neurosurgery. Methods: Trajectories are computed based on the surgeon-defined target and a candidate entry point area on the outer head surface on preoperative CT/MRI scans. A multiparameter risk card provides an estimate of the risk of each trajectory according to its proximity to critical brain structures. Candidate entry points in the outer head surface areas are then color-coded and displayed in 3D to facilitate selection of the most adequate point. The surgeon then defines and/or revised the insertion trajectory using an interactive 3D visualization of surrounding structures. A safety zone around the selected trajectory is also computed to visualize the expected worst-case deviation from the planned insertion trajectory based on tool placement errors in previous surgeries. Results: A retrospective comparative study for ten selected targets on MRI head scans for eight patients showed a significant reduction in insertion trajectory risk. Using the authors’ method, trajectories longer than 30 mm were an average of 2.6 mm further from blood vessels compared to the conventional manual method. Average planning times were 8.4 and 5.9 min for the conventional technique and the authors’ method, respectively. Neurosurgeons reported improved understanding of possible risks and spatial relations for the trajectory and patient anatomy. Conclusions: The suggested method may result in safer trajectories, shorter preoperative planning time, and improved understanding of risks and possible complications in keyhole neurosurgery.

This paper presents new methods for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. These methods allow the surgeon to optimally plan fiducial marker locations on routine diagnostic images before preoperative imaging and to intraoperatively select the set of fiducial markers and anatomical landmarks that minimize the expected target registration error (TRE). The optimization relies on a novel empirical simulation-based TRE estimation method built on actual fiducial localization error (FLE) data. Our methods take the guesswork out of the registration process and can reduce localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduced the TRE. The average TRE values using the usual fiducials setup and using the suggested method were 4.7 mm and 3.2 mm, respectively. We observed a maximum improvement of 4 mm. Reducing the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

Background: Accurate detection of the boundaries of the subthalamic nucleus (STN) in deep brain stimulation (DBS) surgery using microelectrode recording (MER) is considered to refine localization and may therefore improve clinical outcome. However, MER tends to extend operation time and its cost-utility balance has been debated. Objectives: To quantify the tradeoff between accuracy of STN localization and the spatial and temporal parameters of MER that effect the operation time using an automated detection method. Methods: We retrospectively estimated the accuracy of STN detection on data from 100 microelectrode trajectories. Our dense (average step = 0.12 mm) and long (average duration = 22.5 s) MER data was downsampled in the spatial and temporal domains. Then, the STN borders were detected automatically on both the downsampled and original data and compared to each other. Results: With a recording duration of 16 s, average accuracy for detecting STN entry ranged from 0.06 mm for a 0.1-mm step to 0.51 mm for a 1.0-mm step. Smaller effects were found along the temporal axis. For example, a 0.1-mm recording step yielded an STN entry average accuracy ranging from 0.06 mm for a 16-second recording duration to 0.16 mm for 0.1 s. Conclusions: STN entry detection error was about half of the step size. Sampling duration of STN activity can be minimized to 1 s/record without compromising accuracy. We conclude that bilateral DBS surgery time utilizing MER may be significantly shortened without compromising targeting accuracy.

Background Catheter, needle, and electrode misplacement in navigated neurosurgery can result in ineffective treatment and severe complications. Objective To assess the Ommaya ventricular catheter localization accuracy both along the planned trajectory and at the target. Methods We measured the localization error along the ventricular catheter and on its tip for 15 consecutive patients who underwent insertion of the Ommaya catheter surgery with a commercial neuronavigation system. The preoperative computed tomography/magnetic resonance images and the planned trajectory were aligned with the postoperative computed tomography images showing the Ommaya catheter. The localization errors along the trajectory and at the target were then computed by comparing the preoperative planned trajectory with the actual postoperative catheter position. The measured localization errors were also compared with the error reported by the navigation system. Results The mean localization errors at the target and entry point locations were 5.9 ± 4.3 and 3.3 ± 1.9 mm, respectively. The mean shift and angle between planned and actual trajectories were 1.6 ± 1.9 mm and 3.9 ± 4.7°, respectively. The mean difference between the localization error at the target and entry point was 3.9 ± 3.7 mm. The mean difference between the target localization error and the reported navigation system error was 4.9 ± 4.8 mm. Conclusion The catheter localization errors have significant variations at the target and along the insertion trajectory. Trajectory errors may differ significantly from the errors at the target. Moreover, the single registration error number reported by the navigation system does not appropriately reflect the trajectory and target errors and thus should be used with caution to assess the procedure risk.

Point-based rigid registration is the method of choice for aligning medical datasets in diagnostic and image-guided surgery systems. The most clinically relevant localization error measure is the Target Registration Error (TRE), which is the distance between the image-defined target and the corresponding target defined on another image or on the physical anatomy after registration. The TRE directly depends on the Fiducial Localization Error (FLE), which is the discrepancy between the selected and the actual (unknown) fiducial locations. Since the actual locations of targets usually cannot be measured after registration, the TRE is often estimated by the Fiducial Registration Error (FRE), which is the RMS distance between the fiducials in both datasets after registration, or with Fitzpatrick's TRE (FTRE) formula. However, low FRE-TRE and FTRE-TRE correlations have been reported in clinical practice and in theoretical studies. In this article, we show that for realistic FLE classes, the TRE and the FRE are uncorrelated, regardless of the target location and the number of fiducials and their configuration, and regardless of the FLE magnitude distribution. We use a geometrical approach and classical invariant theory to model the FLE and derive its relation to the TRE and FRE values. We show that, for these FLE classes, the FTRE and TRE are also uncorrelated. Finally, we show with simulations on clinical data that the FRE-TRE correlation is low also in the neighborhood of the FLE-FRE invariant classes. Consequently, and contrary to common practice, the FRE and FTRE may not always be used as surrogates for the TRE.

As part of an effort to improve upon the Snellen chart, we provide a standardized version of the ETDRS chart utilizing five characters in each row. The choice of five characters contradicts the recommended ten characters per row determined by the NAS-NRC, a committee established to provide guidelines for testing visual acuity. We set out to quantify the influence of varying the number of characters per line on the ETDRS chart with respect to the accuracy and reproducibility of visual acuity measurement. Eleven different ETDRS charts were created, each with a different number of characters appearing in each row. A computer simulation was programmed to run 10,000 virtual patients, each with a unique visual acuity, false-positive and false-negative error value. Accuracy and reproducibility were found to roughly correlate with the number of characters present in each row, such that charts with 1, 3, 5, 7, 9, and 11 characters per row provided accuracy of 0.164, 0.094, 0.078, 0.073, 0.071, and 0.070 logMAR, respectively. A non-linear relationship was observed, with little improvement found beyond seven characters per row. In addition, charts with an even number of characters per row provided higher accuracy than their greater-number odd counterparts. In certain instances, accuracy and reproducibility were not well correlated. Increasing the number of characters per row in the ETDRS chart provides a trade-off between accuracy and test duration. An optimized chart layout would take these findings into account, allowing for the use of different chart layouts for clinical versus research settings.

Background: Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Objective: Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Methods: Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: 1) information retrieval; 2) visualization of treatment, and; 3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Results: Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P

This paper describes a novel system for precise automatic targeting in minimally invasive neurosurgery. The system consists of a miniature robot fitted with a rigid mechanical guide for needle, catheter, or probe insertion. Intraoperatively, the robot is directly affixed to the patient skull or to a head clamp. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following a three-way anatomical registration with an intraoperative 3D-laser scan of the patient face features. We describe the system architecture, surgical protocol, software modules, and implementation. Registration results on 19 pairs of real MRI and 3D laser scan data show an RMS error of 1.0 mm (std = 0.95 mm) in 2 s.

Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson's disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multilinear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.

Minimal invasion computer-assisted neurosurgical procedures with various tool insertions into the brain may carry hemorrhagic risks and neurological deficits. The goal of this study is to investigate the role of computer-based surgical trajectory planning tools in improving the potential safety of image-based stereotactic neurosurgery.Multi-sequence MRI studies of eight patients who underwent image-guided neurosurgery were retrospectively processed to extract anatomical structures-head surface, ventricles, blood vessels, white matter fibers tractography, and fMRI data of motor, sensory, speech, and visual areas. An experienced neurosurgeon selected one target for each patient. Five neurosurgeons planned a surgical trajectory for each patient using three planning methods: (1) conventional; (2) visualization, in which scans are augmented with overlays of anatomical structures and functional areas; and (3) automatic, in which three surgical trajectories with the lowest expected risk score are automatically computed. For each surgeon, target, and method, we recorded the entry point and its surgical trajectory and computed its expected risk score and its minimum distance from the key structures.A total of 120 surgical trajectories were collected (5 surgeons, 8 targets, 3 methods). The surgical trajectories expected risk scores improved by 76% ([Formula: see text], two-sample student's t test); the average distance of a trajectory from nearby blood vessels increased by 1.6 mm ([Formula: see text]) from 0.6 to 2.2 mm (243%). The initial surgical trajectories were changed in 85% of the cases based on the expected risk score and the trajectory distance from blood vessels.Computer-based patient-specific preoperative planning of surgical trajectories that minimize the expected risk of vascular and neurological damage due to incorrect tool placement is a promising technique that yields consistent improvements.

Some patients with neurological diseases (e.g., a disease that involves abnormal brain function) do not respond well to the available medications and must resort to alternative surgical therapies to manage their symptoms. Parkinson's disease (PD), for example, involves damage to a specific brain area called the basal ganglia and is characterized by reduced levels of a substance called dopamine in the brain. These changes to the brain physiology are associated with motor symptoms like imbalance, tremor, and slowness of motion, which can be very disabling. Fortunately, medications like levodopa can partially return the balance of dopamine in the brain and relieve the motor symptoms of the disease, especially in its early stages. However, as PD progresses, the drug therapy becomes less and less effective, and an additional therapy is needed.

Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson’s disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multi-linear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p

Beta-band synchronous oscillations in the dorsolateral region of the subthalamic nucleus (STN) of human patients with Parkinson's disease (PD) have been frequently reported. However, the correlation between STN oscillations and synchronization has not been thoroughly explored. The simultaneous recordings of 2390 multi-unit pairs recorded by two parallel microelectrodes (separated by fixed distance of 2 mm, n = 72 trajectories with two electrode tracks >4 mm STN span) in 57 PD patients undergoing STN deep brain stimulation surgery were analyzed. Automatic procedures were utilized to divide the STN into dorsolateral oscillatory and ventromedial non-oscillatory regions, and to quantify the intensity of STN oscillations and synchronicity. Finally, the synchronicity of simultaneously vs. non-simultaneously recorded pairs were compared using a shuffling procedure. Synchronization was observed predominately in the beta range and only between multi-unit pairs in the dorsolateral oscillatory region (n = 615). In paired recordings between sites in the dorsolateral and ventromedial (n = 548) and ventromedial-ventromedial region pairs (n = 1227), no synchronization was observed. Oscillation and synchronicity intensity decline along the STN dorsolateral-ventromedial axis suggesting a fuzzy border between the STN regions. Synchronization strength was significantly correlated to the oscillation power, but synchronization was no longer observed following shuffling. We conclude that STN long-range beta oscillatory synchronization is due to increased neuronal coupling in the Parkinsonian brain and does not merely reflect the outcome of oscillations at similar frequency. The neural synchronization in the dorsolateral (probably the motor domain) STN probably augments the pathological changes in firing rate and patterns of subthalamic neurons in PD patients.

The current state of the art for identification of motor related neural activity during deep brain stimulation (DBS) surgery utilizes manual movements of the patient's joints while observing the recorded raw data of a single electrode. Here we describe an intra-operative method for detection of the motor territory of the subthalamic nucleus (STN) during DBS surgery. The method incorporates eight goniometers that continuously monitor and measure the angles of the wrist, elbow, knee and ankle, bilaterally. The joint movement data and microelectrode recordings from the STN are synchronized thus enabling objective intra-operative assessment of movement-related STN activity. This method is now used routinely in DBS surgery at our institute. Advantages include objective identification of motor areas, simultaneous detection of movement for all joints, detection of movement at a joint that is not under examination, shorter surgery time, and continuous monitoring of STN activity for patients with tremor.

The authors present and evaluate a new preoperative planning method and computer software designed to reduce the risk of candidate trajectories for straight rigid tool insertion in image-guided keyhole neurosurgery.Trajectories are computed based on the surgeon-defined target and a candidate entry point area on the outer head surface on preoperative CT/MRI scans. A multiparameter risk card provides an estimate of the risk of each trajectory according to its proximity to critical brain structures. Candidate entry points in the outer head surface areas are then color-coded and displayed in 3D to facilitate selection of the most adequate point. The surgeon then defines and/or revised the insertion trajectory using an interactive 3D visualization of surrounding structures. A safety zone around the selected trajectory is also computed to visualize the expected worst-case deviation from the planned insertion trajectory based on tool placement errors in previous surgeries.A retrospective comparative study for ten selected targets on MRI head scans for eight patients showed a significant reduction in insertion trajectory risk. Using the authors' method, trajectories longer than 30 mm were an average of 2.6 mm further from blood vessels compared to the conventional manual method. Average planning times were 8.4 and 5.9 min for the conventional technique and the authors' method, respectively. Neurosurgeons reported improved understanding of possible risks and spatial relations for the trajectory and patient anatomy.The suggested method may result in safer trajectories, shorter preoperative planning time, and improved understanding of risks and possible complications in keyhole neurosurgery.

We present a new preoperative planning method for reducing the risk associated with the insertion of straight tools in image-guided keyhole neurosurgery. The method quantifies the risks of multiple candidate trajectories and presents them on a physical model of a head using Augmented Reality (AR) to assist the neurosurgeon in selecting the safest path. The surgeon can then define and/or revise the trajectory in the physical space with AR visualization of risk structures such as blood vessels and ventricles, tool placement uncertainty, and quantitative risk measurements. Then, the neurosurgeon can revise the selected path on the 2D MRI image slices to incorporate all relevant information. Finally, a simulation of the surgery can be performed on the physical head model for a more detailed examination of the possible risks. Our preliminary results on clinical data show that in complex situations the method can improve risk assessment.

We present a new preoperative planning method for reducing the risk associated with insertion of straight tools in image-guided keyhole neurosurgery. The method quantifies the risks of multiple candidate trajectories and presents them on the outer head surface to assist the neurosurgeon in selecting the safest path. The surgeon can then define and/or revise the trajectory, add a new one using interactive 3D visualization, and obtain a quantitative risk measures. The trajectory risk is evaluated based on the tool placement uncertainty, on the proximity of critical brain structures, and on a predefined table of quantitative geometric risk measures. Our results on five targets show a significant reduction in trajectory risk and a shortening of the preoperative planning time as compared to the current routine method.