Noctopus: a novel device and method for patient registration and navigation in image-guided cranial surgery.

PURPOSE: A patient registration and real-time surgical navigation system and a novel device and method (Noctopus) is presented. With any tracking system technology and a patient/target-specific registration marker configuration, submillimetric target registration error (TRE), high-precise application accuracy for single or multiple anatomical targets in image-guided neurosurgery or ENT surgery is realized. METHODS: The system utilizes the advantages of marker-based registration technique and allows to perform automatized patient registration using on the device attached and with patient scanned four fiducial markers. The best possible sensor/marker positions around the patient's head are determined for single or multiple region(s) of interest (target/s) in the anatomy. Once brought at the predetermined positions the device can be operated with any tracking system for registration purposes. RESULTS: Targeting accuracy was evaluated quantitatively at various target positions on a phantom skull. The target registration error (TRE) was measured on individual targets using an electromagnetic tracking system. The overall averaged TRE was 0.22 ± 0.08 mm for intraoperative measurements. CONCLUSION: An automatized patient registration system using optimized patient-/target-specific marker configurations is proposed. High-precision and user-error-free intraoperative surgical navigation with minimum number of registration markers and sensors is realized. The targeting accuracy is significantly improved in minimally invasive neurosurgical and ENT interventions.

Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification.

In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data ("features"). The data-driven technique to extract, process, and analyze features from contrast-CTs is termed "radiomics". Extracted features from contrast-CTs at various levels are typically redundant and correlated. Current sets of features for LN-classification are too complex for clinical application. Effective eliminative feature selection (EFS) is a crucial preprocessing step to reduce the complexity of sets identified. We aimed at exploring EFS-algorithms for their potential to identify sets of features, which were as small as feasible and yet retained as much accuracy as possible for LN-classification. In this retrospective cohort-study, which adhered to the STROBE guidelines, in total 252 LNs were classified as "non-pathologic" (n = 70), "pathologic" (n = 182) or "pathologic with extracapsular spread" (n = 52) by two experienced head-and-neck radiologists based on established criteria which served as a reference. The combination of sparse discriminant analysis and genetic optimization retained up to 90% of the classification accuracy with only 10% of the original numbers of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified EFS-algorithm and the identified features need further exploration to assess their potential to prospectively classify LNs in HNSCC.

Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning.

Purpose: Automating fiducial detection and localization in the patient's pre-operative images can lead to better registration accuracy, reduced human errors, and shorter intervention time. Most current approaches are optimized for a single marker type, mainly spherical adhesive markers. A fully automated algorithm is proposed and evaluated for screw and spherical titanium fiducials, typically used in high-accurate frameless surgical navigation. Approach: The algorithm builds on previous approaches with morphological functions and pose estimation algorithms. A 3D convolutional neural network (CNN) is proposed for the fiducial classification task and evaluated for both traditional closed-set and emerging open-set classifiers. A proposed digital ground-truth experiment, with cone-beam computed tomography (CBCT) imaging software, is performed to determine the localization accuracy of the algorithm. The localized fiducial positions in the CBCT images by the presented algorithm were compared to the actual known positions in the virtual phantom models. The difference represents the fiducial localization error (FLE). Results: A total of 241 screws, 151 spherical fiducials, and 1550 other structures are identified with the best true positive rate 95.9% for screw and 99.3% for spherical fiducials at 8.7% and 3.4% false positive rate, respectively. The best achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) and 14 ( 6 ) µ m , respectively. Conclusions: Accurate marker detection and localization were achieved, with spherical fiducials being superior to screws. Large marker volume and smaller voxel size yield significantly smaller FLEs. Attenuating noise by mesh smoothing has a minor effect on FLE. Future work will focus on expanding the CNN for image segmentation.

Visual display for surgical targeting: concepts and usability study.

PURPOSE: Interactive image-guided surgery technologies enable accurate target localization while preserving critical nearby structures in many surgical interventions. Current state-of-the-art interfaces largely employ traditional anatomical cross-sectional views or augmented reality environments to present the actual spatial location of the surgical instrument in preoperatively acquired images. This work proposes an alternative, simple, minimalistic visual interface intended to assist during real-time surgical target localization. METHODS: The estimated 3D pose of the interventional instruments and their positional uncertainty are intuitively presented in a visual interface with respect to the target point. A usability study with multidisciplinary participants evaluates the proposed interface projected in surgical microscope oculars against cross-sectional views. The latter was presented on a screen both stand-alone and combined with the proposed interface. The instruments were electromagnetically navigated in phantoms. RESULTS: The usability study demonstrated that the participants were able to detect invisible targets marked in phantom imagery with significant enhancements for localization accuracy and duration time. Clinically experienced users reached the targets with shorter trajectories. The stand-alone and multi-modal versions of the proposed interface outperformed cross-sectional views-only navigation in both quantitative and qualitative evaluations. CONCLUSION: The results and participants' feedback indicate potential to accurately navigate users toward the target with less distraction and workload. An ongoing study evaluates the proposed system in a preclinical setting for auditory brainstem implantation.

respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking.

PURPOSE: An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments. METHODS: A custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient's surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient's 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction. RESULTS: Two evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between [Formula: see text] and [Formula: see text]. The overall registration RMS error was [Formula: see text]. The best prediction errors were observed by registrations at half inhaled positions with minimum [Formula: see text], maximum [Formula: see text]. The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine. CONCLUSION: The built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients.

CIGuide: in situ augmented reality laser guidance.

PURPOSE : A robotic intraoperative laser guidance system with hybrid optic-magnetic tracking for skull base surgery is presented. It provides in situ augmented reality guidance for microscopic interventions at the lateral skull base with minimal mental and workload overhead on surgeons working without a monitor and dedicated pointing tools. METHODS : Three components were developed: a registration tool (Rhinospider), a hybrid magneto-optic-tracked robotic feedback control scheme and a modified robotic end-effector. Rhinospider optimizes registration of patient and preoperative CT data by excluding user errors in fiducial localization with magnetic tracking. The hybrid controller uses an integrated microscope HD camera for robotic control with a guidance beam shining on a dual plate setup avoiding magnetic field distortions. A robotic needle insertion platform (iSYS Medizintechnik GmbH, Austria) was modified to position a laser beam with high precision in a surgical scene compatible to microscopic surgery. RESULTS : System accuracy was evaluated quantitatively at various target positions on a phantom. The accuracy found is 1.2 mm ± 0.5 mm. Errors are primarily due to magnetic tracking. This application accuracy seems suitable for most surgical procedures in the lateral skull base. The system was evaluated quantitatively during a mastoidectomy of an anatomic head specimen and was judged useful by the surgeon. CONCLUSION : A hybrid robotic laser guidance system with direct visual feedback is proposed for navigated drilling and intraoperative structure localization. The system provides visual cues directly on/in the patient anatomy, reducing the standard limitations of AR visualizations like depth perception. The custom- built end-effector for the iSYS robot is transparent to using surgical microscopes and compatible with magnetic tracking. The cadaver experiment showed that guidance was accurate and that the end-effector is unobtrusive. This laser guidance has potential to aid the surgeon in finding the optimal mastoidectomy trajectory in more difficult interventions.

Probe versus microscope: a comparison of different methods for image-to-patient registration.

PURPOSE: Computer-aided navigation is widely used in ENT surgery. The position of a surgical instrument is shown in the CT/MR images of the patient and can thus be a good support for the surgeon. The accuracy is highly dependent on the registration done prior to surgery. A microscope and a probe can both be used for registration and navigation, depending on the surgical intervention. A navigation system typically only reports the fiducial registration error after paired-point registration. However, the target registration error (TRE)-a measurement for the accuracy in the surgical area-is much more relevant. The aim of this work was to compare the performance of a microscope relative to a conventional probe-based approach with different registration methods. METHODS: In this study, optical tracking was used to register a plastic skull to its preoperative CT images with paired-point registration. Anatomical landmarks and skin-affixed markers were used as fiducials and targets. With both microscope and probe, four different registration methods were evaluated based on their TREs at 10 targets. For half of the experiments, a surface registration and/or external fiducials were used additionally to paired-point registration to study their influence to accuracy. RESULTS: Overall, probe registration leads to a smaller TRE ([Formula: see text]) than registration with a microscope ([Formula: see text]). Additional surface registration does not result in better accuracy of navigation for microscope and probe. The lowest mean TRE for both pointers can be achieved with paired-point registration only and radiolucent markers. CONCLUSION: Our experiments showed that a probe used for registration and navigation achieves lower TREs compared using a microscope. Neither additional surface registration nor additional fiducials on an external reference element are necessary for improved accuracy of navigated ENT surgery on a plastic skull.

Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration.

PURPOSE: The target registration error (TRE) is a crucial parameter to estimate the potential usefulness of computer-assisted navigation intraoperatively. Both image-to-patient registration on base of rigid-body registration and TRE prediction methods are available for spatially isotropic and anisotropic data. This study presents a thorough validation of data obtained in an experimental operating room setting with CT images. METHODS: Optical tracking was used to register a plastic skull, an anatomic specimen, and a volunteer to their respective CT images. Plastic skull and anatomic specimen had implanted bone fiducials for registration; the volunteer was registered with anatomic landmarks. Fiducial localization error, fiducial registration error, and total target error (TTE) were measured; the TTE was compared to isotropic and anisotropic error prediction models. Numerical simulations of the experiment were done additionally. RESULTS: The user localization error and the TTE were measured and calculated using predictions, both leading to results as expected for anatomic landmarks and screws used as fiducials. TRE/TTE is submillimetric for the plastic skull and the anatomic specimen. In the experimental data a medium correlation was found between TRE and target localization error (TLE). Most of the predictions of the application accuracy (TRE) fall in the 68% confidence interval of the measured TTE. For the numerically simulated data, a prediction of TTE was not possible; TRE and TTE show a negligible correlation. CONCLUSION: Experimental application accuracy of computer-assisted navigation could be predicted satisfactorily with adequate models in an experimental setup with paired-point registration of CT images to a patient. The experimental findings suggest that it is possible to run navigation and prediction of navigation application accuracy basically defined by the spatial resolution/precision of the 3D tracker used.

Estimating FLEimage distributions of manual fiducial localization in CT images.

PURPOSE: The fiducial localization error distribution (FLE) and fiducial configuration govern the application accuracy of point-based registration and drive target registration error (TRE) prediction models. The error of physically localizing patient fiducials ([Formula: see text]) is negligible when a registration probe matches the implanted screws with mechanical precision. Reliable trackers provide an unbiased estimate of the positional error ([Formula: see text]) with cheap repetitions. FLE further contains the localization error in the imaging data ([Formula: see text]), sampling of which in general is expensive and possibly biased. Finding the best techniques for estimating [Formula: see text] is crucial for the applicability of the TRE prediction methods. METHODS: We built a ground-truth (gt)-based unbiased estimator ([Formula: see text]) of [Formula: see text] from the samples collected in a virtual CT dataset in which the true locations of image fiducials are known by definition. Replacing true locations in [Formula: see text] by the sample mean creates a practical difference-to-mean (dtm)-based estimator ([Formula: see text]) that is applicable on any dataset. To check the practical validity of the dtm estimator, ten persons manually localized nine fiducials ten times in the virtual CT and the resulting [Formula: see text] and [Formula: see text] distributions were tested for statistical equality with a kernel-based two-sample test using the maximum mean discrepancy (MMD) (Gretton in J Mach Learn Res 13:723-773, 2012) statistics at [Formula: see text]. RESULTS: [Formula: see text] and [Formula: see text] were found (for most of the cases) not to be statistically significantly different; conditioning them on persons and/or screws however yielded statistically significant differences much more often. CONCLUSIONS: We conclude that [Formula: see text] is the best candidate (within our model) for estimating [Formula: see text] in homogeneous TRE prediction models. The presented approach also allows ground-truth-based numerical validation of [Formula: see text] estimators and (manual/automatic) image fiducial localization methods in phantoms with parameters similar to clinical datasets.

Proof-of-concept of a laser mounted endoscope for touch-less navigated procedures.

BACKGROUND AND OBJECTIVES: During navigated procedures a tracked pointing device is used to define target structures in the patient to visualize its position in a registered radiologic data set. When working with endoscopes in minimal invasive procedures, the target region is often difficult to reach and changing instruments is disturbing in a challenging, crucial moment of the procedure. We developed a device for touch less navigation during navigated endoscopic procedures. MATERIALS AND METHODS: A laser beam is delivered to the tip of a tracked endoscope angled to its axis. Thereby the position of the laser spot in the video-endoscopic images changes according to the distance between the tip of the endoscope and the target structure. A mathematical function is defined by a calibration process and is used to calculate the distance between the tip of the endoscope and the target. The tracked tip of the endoscope and the calculated distance is used to visualize the laser spot in the registered radiologic data set. RESULTS: In comparison to the tracked instrument, the touch less target definition with the laser spot yielded in an over and above error of 0.12 mm. The overall application error in this experimental setup with a plastic head was 0.61 ± 0.97 mm (95% CI -1.3 to +2.5 mm). CONCLUSION: Integrating a laser in an endoscope and then calculating the distance to a target structure by image processing of the video endoscopic images is accurate. This technology eliminates the need for tracked probes intraoperatively and therefore allows navigation to be integrated seamlessly in clinical routine. However, it is an additional chain link in the sequence of computer-assisted surgery thus influencing the application error.