Performance assessment of surgical tracking systems based on statistical process control and longitudinal QA.

A system for performance assessment and quality assurance (QA) of surgical trackers is reported based on principles of geometric accuracy and statistical process control (SPC) for routine longitudinal testing. A simple QA test phantom was designed, where the number and distribution of registration fiducials was determined drawing from analytical models for target registration error (TRE). A tracker testbed was configured with open-source software for measurement of a TRE-based accuracy metric ε and Jitter (J). Six trackers were tested: 2 electromagnetic (EM - Aurora); and 4 infrared (IR - 1 Spectra, 1 Vega, and 2 Vicra) - all NDI (Waterloo, ON). Phase I SPC analysis of Shewhart mean (x¯) and standard deviation (s) determined system control limits. Phase II involved weekly QA of each system for up to 32 weeks and identified Pass, Note, Alert, and Failure action rules. The process permitted QA in <1 min. Phase I control limits were established for all trackers: EM trackers exhibited higher upper control limits than IR trackers in ε (EM: x¯Îµ âˆ¼2.8-3.3 mm, IR: x¯Îµ âˆ¼1.6-2.0 mm) and Jitter (EM: x¯jitter âˆ¼0.30-0.33 mm, IR: x¯jitter âˆ¼0.08-0.10 mm), and older trackers showed evidence of degradation - e.g. higher Jitter for the older Vicra (p-value < .05). Phase II longitudinal tests yielded 676 outcomes in which a total of 4 Failures were noted - 3 resolved by intervention (metal interference for EM trackers) - and 1 owing to restrictive control limits for a new system (Vega). Weekly tests also yielded 40 Notes and 16 Alerts - each spontaneously resolved in subsequent monitoring.

Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance.

Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.

Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance.

PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance. METHOD: The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks. RESULTS: The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s. CONCLUSION: The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.

Deformable 3D-2D registration for high-precision guidance and verification of neuroelectrode placement.

Purpose.Accurate neuroelectrode placement is essential to effective monitoring or stimulation of neurosurgery targets. This work presents and evaluates a method that combines deep learning and model-based deformable 3D-2D registration to guide and verify neuroelectrode placement using intraoperative imaging.Methods.The registration method consists of three stages: (1) detection of neuroelectrodes in a pair of fluoroscopy images using a deep learning approach; (2) determination of correspondence and initial 3D localization among neuroelectrode detections in the two projection images; and (3) deformable 3D-2D registration of neuroelectrodes according to a physical device model. The method was evaluated in phantom, cadaver, and clinical studies in terms of (a) the accuracy of neuroelectrode registration and (b) the quality of metal artifact reduction (MAR) in cone-beam CT (CBCT) in which the deformably registered neuroelectrode models are taken as input to the MAR.Results.The combined deep learning and model-based deformable 3D-2D registration approach achieved 0.2 ± 0.1 mm accuracy in cadaver studies and 0.6 ± 0.3 mm accuracy in clinical studies. The detection network and 3D correspondence provided initialization of 3D-2D registration within 2 mm, which facilitated end-to-end registration runtime within 10 s. Metal artifacts, quantified as the standard deviation in voxel values in tissue adjacent to neuroelectrodes, were reduced by 72% in phantom studies and by 60% in first clinical studies.Conclusions.The method combines the speed and generalizability of deep learning (for initialization) with the precision and reliability of physical model-based registration to achieve accurate deformable 3D-2D registration and MAR in functional neurosurgery. Accurate 3D-2D guidance from fluoroscopy could overcome limitations associated with deformation in conventional navigation, and improved MAR could improve CBCT verification of neuroelectrode placement.

Computer Assisted Planning for Curved Laser Interstitial Thermal Therapy.

Laser interstitial thermal therapy (LiTT) isa minimally invasive alternative to conventional open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Recent studies suggest that higher seizure freedom rates are correlated with maximal ablation of the mesialhippocampal head, whilst sparing of the parahippocampal gyrus (PHG) may reduce neuropsychological sequelae. Current commercially available laser catheters are inserted following manually planned straight-line trajectories, which cannot conform to curved brain structures, such as the hippocampus, without causing collateral damage or requiring multiple insertions. OBJECTIVES: The clinical feasibility and potential of curved LiTT trajectories through steerable needles has yet to be investigated. This is the focus of our work. METHODS: We propose a GPU-accelerated computer-assisted planning (CAP) algorithm for steerable needle insertions that generates optimized curved 3D trajectories with maximal ablation of the amygdalohippocampal complex and minimal collateral damage to nearby structures, while accounting for a variable ablation diameter ( 5-15 mm). RESULTS: Simulated trajectories and ablations were performed on 5 patients with mesial temporal sclerosis (MTS), which were identified from a prospectively managed database. The algorithm generated obstacle-free paths with significantly greater target area ablation coverage and lower PHG ablation variance compared to straight line trajectories. CONCLUSIONS: The presented CAP algorithm returns increased ablation of the amygdalohippocampal complex, with lower patient risk scores compared to straight-line trajectories. SIGNIFICANCE: This is the first clinical application of preoperative planning for steerable needle based LiTT. This study suggests that steerableneedles have the potential to improve LiTT procedure efficacy whilst improving the safety and should thus be investigated further.

Data-Driven Deformable 3D-2D Registration for Guiding Neuroelectrode Placement in Deep Brain Stimulation.

Purpose: Deep brain stimulation is a neurosurgical procedure used in treatment of a growing spectrum of movement disorders. Inaccuracies in electrode placement, however, can result in poor symptom control or adverse effects and confound variability in clinical outcomes. A deformable 3D-2D registration method is presented for high-precision 3D guidance of neuroelectrodes. Methods: The approach employs a model-based, deformable algorithm for 3D-2D image registration. Variations in lead design are captured in a parametric 3D model based on a B-spline curve. The registration is solved through iterative optimization of 16 degrees-of-freedom that maximize image similarity between the 2 acquired radiographs and simulated forward projections of the neuroelectrode model. The approach was evaluated in phantom models with respect to pertinent imaging parameters, including view selection and imaging dose. Results: The results demonstrate an accuracy of (0.2 ± 0.2) mm in 3D localization of individual electrodes. The solution was observed to be robust to changes in pertinent imaging parameters, which demonstrate accurate localization with ≥20° view separation and at 1/10th the dose of a standard fluoroscopy frame. Conclusions: The presented approach provides the means for guiding neuroelectrode placement from 2 low-dose radiographic images in a manner that accommodates potential deformations at the target anatomical site. Future work will focus on improving runtime though learning-based initialization, application in reducing reconstruction metal artifacts for 3D verification of placement, and extensive evaluation in clinical data from an IRB study underway.

Surgineering: curriculum concept for experiential learning in upper-level biomedical engineering.

PURPOSE: A strong foundation in the fundamental principles of medical intervention combined with genuine exposure to real clinical systems and procedures will improve engineering students' capability for informed innovation on clinical problems. To help build such a foundation, a new course (dubbed Surgineering) was developed to convey fundamental principles of surgery, interventional radiology (IR), and radiation therapy, with an emphasis on experiential learning, hands-on with real clinical systems, exposure to clinicians, and visits to real operating theaters. The concept, structure, and outcomes of the course of the first run of the first semester of the course are described. METHOD: The course included six segments spanning fundamental concepts and cutting-edge approaches in a spectrum of surgical specialties, body and neurological IR, and radiation therapy. Each class involved a minimum of didactic content and an emphasis on hands-on experience with instrumentation, equipment, surgical approaches, anatomical models, dissection, and visits to clinical theaters. Outcomes on the quality of the course and areas for continuing improvement were assessed by student surveys (5-point Likert scores and word-cloud representations of free response) as well as feedback from clinical collaborators. RESULT: Surveys assessed four key areas of feedback on the course and were analyzed quantitatively and in word-cloud representations of: (1) best aspects (hands-on experience with surgeons); (2) worst aspects (quizzes and reading materials); (3) areas for improvement (projects, quizzes, and background on anatomy); and (4) what prospective students should know (a lot background reading for every class). Five-point Likert scores from survey respondents (16/19 students) indicated: overall quality of the course 4.63 ± 0.72 (median 5.00); instructor teaching effectiveness 4.06 ± 1.06 (median 4.00); intellectual challenge 4.19 ± 0.40 (median 4.00); and workload somewhat heavier (62.5%) compared to other courses. Novel elements of the course included the opportunity to engage with clinical faculty and participate in realistic laboratory exercises, work with clinical instruments and equipment, and visit real operating theaters. An additional measure of the success of the course was evidenced by surveys and a strong escalation in enrollment in the following year. CONCLUSIONS: The Surgineering course presents an important addition to upper-level engineering curricula and a valuable opportunity for engineering students to gain hands-on experience and interaction with clinical experts. Close partnership with clinical faculty was essential to the schedule and logistics of the course as well as to the continuity of concepts delivered over the semester. The knowledge and experience gained provides stronger foundation for identification of un-met clinical needs and ideation of new engineering approaches in medicine. The course also provides a valuable prerequisite to higher-level coursework in systems engineering, human factors, and data science applied to medicine.

Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule.

Interictal spikes (IIS) are bursts of neuronal depolarization observed electrographically between periods of seizure activity in epilepsy patients. However, IISs are difficult to characterize morphologically and their effects on neurophysiology and cognitive function are poorly understood. Currently, IIS detection requires laborious manual assessment and marking of electroencephalography (EEG/iEEG) data. This practice is also subjective as the clinician has to select the mental threshold that EEG activity must exceed in order to be considered a spike. The work presented here details the development and implementation of a simple automated IIS detection algorithm. This preliminary study utilized intracranial EEG recordings collected from 7 epilepsy patients, and IISs were marked by a single physician for a total of 1339 IISs across 68 active electrodes. The proposed algorithm implements a simple threshold rule that scans through iEEG data and identifies IISs using various normalization techniques that eliminate the need for a more complex detector. The efficacy of the algorithm was determined by evaluating the sensitivity and specificity of the detector across a range of thresholds, and an approximate optimal threshold was determined using these results. With an average true positive rate of over 98% and a false positive rate of below 2%, the accuracy of this algorithm speaks to its use as a reliable diagnostic tool to detect IISs, which has direct applications in localizing where seizures start, detecting when seizures start, and in understanding cognitive impairment due to IISs. Furthermore, due to its speed and simplicity, this algorithm can be used for real-time detection of IIS that will ultimately allow physicians to study their clinical implications with high temporal resolution and individual adaptation.

Functional role of induced gamma oscillatory responses in processing noxious and innocuous sensory events in humans.

Gamma time-frequency responses (TFRs) induced by painful laser in the contralateral primary somatosensory (SI) cortex have been shown to correlate with perceived pain-intensity in human. Given the functional roles of gamma TFRs in the cortical spaces, it remains unclear whether such a relationship is sustained for other brain regions where the laser-evoked potentials (LEPs) are presented. In this study, we delivered the painful laser pluses at random pain-intensity levels (i.e. strong, medium and weak) in a single train to the dorsal hand of six patients with uncontrolled epilepsy. The laser stimulus produced a painful pinprick sensation by activating nociceptors located in the superficial layers of the skin. For each patient, arrays of >64 subdural electrodes were implanted directly covering the contralateral SI, parasylvian (PS) and medial frontal (MF) cortices to study the stimulus related gamma (TFRs) in the neocortex. In addition, using the same stimulation paradigm, the modality specificity of gamma TFRs was further examined by applying innocuous vibrotactile stimuli to the same regions of the dorsal hand in a separated group of five patients. Our results showed that gamma TFRs are not modality specific, but the largest gamma TFRs were consistently found within the SI region and noxious laser elicited significantly stronger gamma TFRs than innocuous nonpainful vibratory stimuli. Furthermore, stronger pain induced stronger gamma TFRs in the cortices of SI (r=0.4, p<0.001) and PS (r=0.29, p=0.005). Given that potentially harmful noxious stimulus would automatically capture greater attention than the innocuous ones, our results support the hypothesis that the degree of SI and PS gamma TFRs is associated with an attentional drive provoked by painful stimuli.

Cross-frequency coupling in deep brain structures upon processing the painful sensory inputs.

Cross-frequency coupling has been shown to be functionally significant in cortical information processing, potentially serving as a mechanism for integrating functionally relevant regions in the brain. In this study, we evaluate the hypothesis that pain-related gamma oscillatory responses are coupled with low-frequency oscillations in the frontal lobe, amygdala and hippocampus, areas known to have roles in pain processing. We delivered painful laser pulses to random locations on the dorsal hand of five patients with uncontrolled epilepsy requiring depth electrode implantation for seizure monitoring. Two blocks of 40 laser stimulations were delivered to each subject and the pain-intensity was controlled at five in a 0-10 scale by adjusting the energy level of the laser pulses. Local-field-potentials (LFPs) were recorded through bilaterally implanted depth electrode contacts to study the oscillatory responses upon processing the painful laser stimulations. Our results show that painful laser stimulations enhanced low-gamma (LH, 40-70 Hz) and high-gamma (HG, 70-110 Hz) oscillatory responses in the amygdala and hippocampal regions on the right hemisphere and these gamma responses were significantly coupled with the phases of theta (4-7 Hz) and alpha (8-1 2 Hz) rhythms during pain processing. Given the roles of these deep brain structures in emotion, these findings suggest that the oscillatory responses in these regions may play a role in integrating the affective component of pain, which may contribute to our understanding of the mechanisms underlying the affective information processing in humans.