INTRODUCTION:This study aimed to develop and evaluate a deep learning (DL) deblurring algorithm for computed tomography (CT). Phantom testing assessed whether DL deblurring could improve CT image quality and coronary artery calcium (CAC) detectability. MATERIALS AND METHODS:The algorithm consisted of 2 sequential convolutional neural networks (CNNs): one for denoising and one for deblurring. After deblurring, the estimated noise from the first model was reintroduced to preserve image texture. The CNN denoising model was trained to map high-noise images to routine low-noise ones. The CNN deblurring model was trained using natural images-blurred inputs paired with sharp targets. To assess performance, an anthropomorphic phantom with 100 small calcifications was scanned on a CT system using a CAC scoring protocol. Data were acquired at clinical and high doses, then reconstructed with and without DL deblurring, using 25% of the maximum denoising strength. Detectability was defined as the ability to calculate an Agatston score (≥3 adjacent voxels >130 HU). RESULTS:At high dose, CAC detectability increased from 39% (standard reconstruction) to 48% with deblurring. Similar detectability (39%) was achieved at the routine dose with deblurring, representing a 6% improvement compared with the original routine dose data. DISCUSSION:Quantitative results from phantom testing demonstrate that DL deblurring can improve image quality, based on a clinically relevant, task-based CAC detection metric at clinical dose levels.

Deep neural networks have emerged as the preferred method for semantic segmentation of CT images in recent years. However, understanding their limitations and generalization properties remains an active area of research and a relevant topic for clinical applications. One crucial factor among many is the X-ray radiation dose, which is always kept as low as reasonably possible during CT acquisition. Therefore, potential dose reductions may pose a challenge for existing segmentation models. In this paper, we investigate robustness of the recently proposed TotalSegmentator model for anatomical segmentation with respect to dose reduction. TotalSegmentator combines a large CT dataset and the well-established nnU-Net framework to train deep learning models, resulting in state-of-the-art performance for anatomical segmentation. Our method relies on accurate low-dose simulations derived from acquired full-dose projections. For a set of registered low- and full-dose CT images, we measure the Dice score between the corresponding segmentations. Our results reveal a high level of robustness in the segmentation outcomes. Comprehensive quantitative comparisons demonstrate that at a 20% dose level, the Dice score declines by at most 3%. Visual comparisons reveal only minor differences at the boundaries of the segmented organs. These findings may have a large potential for dose reduction when CT data are acquired predominantly for segmentation purposes, such as for the planning of interventional or surgical procedures.

We investigated the impact of a CNN-based deep-learning (DL) image de-blurring algorithm on coronary artery calcium (CAC) detection performance in conventional CT imaging. Our approach comprises first de-noising the image with a state-of-the-art CNN-based image de-noising algorithm. With improved SNR, it is then possible to sharpen the image with a CNN-based image de-blurring algorithm. We train such networks using natural images, i.e., a large set of diverse photographs. The de-noising strength in the final image can be adjusted by blending back the estimated noise from the first step to the desired degree. To assess the impact of the de-blurring algorithm, we scanned an anthropomorphic phantom containing 100 small calcifications on a CT system using a CAC scoring protocol. Data were acquired at clinical and high dose, and subsequently reconstructed with and without the DL de-blurring algorithm, using 25% of the maximum de-noising strength. For each small CAC, detectability was defined as the ability to calculate an Agatston score (at least 3 adjacent voxels exceeding 130 HU). For the high dose scans, CAC detectability increased from 39% for the standard reconstruction to 49% with de-blurring. The same 39% CAC detectability at high dose without de-blurring was obtained with routine dose with de-blurring. In this work, we also show some visual impressions of applying our DL de-blurring method to clinical cardiac data.

Purpose: The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations. Approach: We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibility with existing denoising methods is considered. Results: The results reveal the surprising robustness of the TotalSegmentator approach, showing minimal differences at the pixel level even without denoising. Less robust models show good compatibility with the denoising methods, which help to improve robustness in almost all cases. With denoising based on a convolutional neural network (CNN), the median Dice between low- and full-dose data does not fall below 0.9 (12 for the Hausdorff distance) for all but one model. We observe volatile results for labels with effective radii less than 19 mm and improved results for contrasted CT acquisitions. Conclusion: The proposed approach facilitates clinically relevant analysis of dose robustness for human organ segmentation models. The results outline the robustness properties of a diverse set of models. Further studies are needed to identify the robustness of approaches for lesion segmentation and to rank the factors contributing to dose robustness.

Cardiac motion artifacts hinder the assessment of coronary arteries in coronary computed tomography angiography (CCTA). We investigated the impact of motion compensation reconstruction (MCR) on motion artifacts in CCTA at various heart rates (HR) using a dynamic phantom. An artificial hollow coronary artery (5-mm diameter lumen) filled with iodinated contrast agent (400 HU at 120 kVp), positioned centrally in an anthropomorphic chest phantom, was scanned using a dual-layer spectral detector CT. The artery was translated at constant horizontal velocities (0–80 mm/s, increment of 10 mm/s). For each velocity, five CCTA scans were repeated using a clinical protocol. Motion artifacts were quantified using the in-plane motion area. Regression analysis was performed to calculate the reduction in motion artifacts provided by MCR, by division of the slopes of non-MCR and MCR fitted lines. Reference mean (95

Spontaneous patient motion occurring in head imaging can lead to severe image artifacts. In this study, we introduce a simulation framework for two different types of head motion and investigate the severity of the resulting artifacts under the assumption of perfectly known rigid motion parameters during motion compensated reconstruction for circular head CT. Error measures such as the mean absolute error and the structural similarity index are evaluated as a function of the rotation angle and speed during the data acquisition. Targeting mean absolute errors of 5 HU in the brain tissue of clinical data sets, motion up to 1.3° (6°) nodding motion amplitude can be tolerated in the normal (motion compensated) reconstruction.

Purpose: Implanting stents to re-open stenotic lesions during percutaneous coronary interventions is considered a standard treatment for acute or chronic coronary syndrome. Intravascular ultrasound (IVUS) can be used to guide and assess the technical success of these interventions. Automatically segmenting stent struts in IVUS sequences improves workflow efficiency but is non-trivial due to a challenging image appearance entailing manifold ambiguities with other structures. Manual, ungated IVUS pullbacks constitute a challenge in this context. We propose a fully data-driven strategy to first longitudinally detect and subsequently segment stent struts in IVUS frames. Approach: A cascaded deep learning approach is presented. It first trains an encoder model to classify frames as "stent," "no stent," or "no use." A segmentation model then delineates stent struts on a pixel level only in frames with a stent label. The first stage of the cascade acts as a gateway to reduce the risk for false positives in the second stage, the segmentation, which is trained on a smaller and difficult-to-annotate dataset. Training of the classification and segmentation model was based on 49,888 and 1826 frames of 74 sequences from 35 patients, respectively. Results: The longitudinal classification yielded Dice scores of 92.96%, 82.35%, and 94.03% for the classes stent, no stent, and no use, respectively. The segmentation achieved a Dice score of 65.1% on the stent ground truth (intra-observer performance: 75.5%) and 43.5% on all frames (including frames without stent, with guidewires, calcium, or without clinical use). The latter improved to 49.5% when gating the frames by the classification decision and further increased to 57.4% with a heuristic on the plausible stent strut area. Conclusions: A data-driven strategy for segmenting stents in ungated, manual pullbacks was presented-the most common and practical scenario in the time-critical clinical workflow. We demonstrated a mitigated risk for ambiguities and false positive predictions.

Angiography-derived physiological assessment of coronary lesions has emerged as an alternative to wire-based assessment aiming at less-invasiveness and shorter procedural time as well as cost effectiveness in physiology-guided decision making. However, current available image-derived physiology software have limitations including the requirement of multiple projections and are time consuming. METHODS/DESIGN: The ReVEAL iFR (Radiographic imaging Validation and EvALuation for Angio-iFR) trial is a multicenter, multicontinental, validation study which aims to validate the diagnostic accuracy of the Angio-iFR medical software device (Philips, San Diego, US) in patients undergoing angiography for Chronic Coronary Syndrome (CCS). The Angio-iFR will enable operators to predict both the iFR and FFR value within a few seconds from a single projection of cine angiography by using a lumped parameter fluid dynamics model. Approximately 440 patients with at least one de-novo 40% to 90% stenosis by visual angiographic assessment will be enrolled in the study. The primary endpoint is the sensitivity and specificity of the iFR and FFR for a given lesion compared to the corresponding invasive measures. The enrollment started in August 2019, and was completed in March 2021. SUMMARY: The Angio-iFR system has the potential of simplifying physiological evaluation of coronary stenosis compared with available systems, providing estimates of both FFR and iFR. The ReVEAL iFR study will investigate the predictive performance of the novel Angio-iFR software in CCS patients. Ultimately, based on its unique characteristics, the Angio-iFR system may contribute to improve adoption of functional coronary assessment and the workflow in the catheter laboratory.

Knowing the shape of vascular calcifications is crucial for appropriate planning and conductance of percutaneous coronary interventions. The clinical workflow can therefore benefit from automatic segmentation of calcified plaques in intravascular ultrasound (IVUS) images. To solve segmentation problems with convolutional neural networks (CNNs), large datasets are usually required. However, datasets are often rather small in the medical domain. Hence, developing and investigating methods for increasing CNN performance on small datasets can help on the way towards clinically relevant results. We compared two state-of-the-art CNN architectures for segmentation, U-Net and DeepLabV3, and investigated how incorporating auxiliary image data with vessel wall and lumen annotations improves the calcium segmentation performance by using these either for pretraining or multi-task training. DeepLabV3 outperforms U-Net with up to 6.3 % by means of the Dice coefficient and 36.5 % by means of the average Hausdorff distance. Using auxiliary data improves the segmentation performance in both cases, whereas the multi-task approach outperforms the pre-training approach. The improvements of the multi-task approach in contrast to not using auxiliary data at all is 5.7 % for the Dice coefficient and 42.9 % for the average Hausdorff distance. Automatic segmentation of calcified plaques in IVUS images is a demanding task due to their relatively small size compared to the image dimensions and due to visual ambiguities with other image structures. We showed that this problem can generally be tackled by CNNs. Furthermore, we were able to improve the performance by a multi-task learning approach with auxiliary segmentation data.

Ischemic heart disease remains one of the leading causes of death worldwide. Percutaneous coronary interventions (PCIs) for implanting coronary stents are preferred for patients with acute myocardial infarction but may also be performed in patients with chronic coronary syndromes to improve symptoms and outcome. During the PCI, the assessment of stent apposition, evaluation of in-stent restenosis or guidance for complex stenting of bifurcation lesions may be improved by intravascular imaging such as intravascular ultrasound (IVUS). However, advanced interpretation of the image often requires expertise and training. To approach this issue, we introduce an automatic delineation of stent struts within the IVUS pullback. We propose a cascaded segmentation based on data-driven learning with a neural encoder-decoder architecture. The learning process uses 80 IVUS sequences from 28 patients which were acquired and partially annotated by the Department of Cardiology, University Heart and Vascular Center Hamburg, Germany. The annotations include 1108, 555 and 355 frames with delineated lumen, stent and calcium as well as 13696 and 10689 frame-wise stent and no-stent indications. The network was pre-trained on lumen segmentation and refined to first identify stent frames using an encoder network and subsequently segment the struts with a decoder. Quantitative evaluation using 3-fold cross-validation revealed 88.3% precision, 92.4% recall and 0.824 Dice for the encoder and 67.0%, 60.3% and 0.611 for the decoder. We conclude that the encoder successfully leverages the larger number of high-level annotations to reject non-stent frames avoiding unnecessary false positives for the decoder trained on much less, but fine-granular annotations.

Using intracoronary imaging modalities like intravascular ultrasound (IVUS) has a positive impact on the results of percutaneous coronary interventions. Efficient extraction of important vessel metrics like lumen diameter, vessel wall thickness or plaque burden via automatic segmentation of IVUS images can improve the clinical workflow. State-of-the-art segmentation results are usually achieved by data-driven methods like convolutional neural networks (CNNs). However, clinical data sets are often rather small leading to extraction of image features which are not very meaningful and thus decreasing performance. This is also the case for some applications which inherently allow for only small amounts of available data, e.g., detection of diseases with extremely small prevalence or online-adaptation of an existing algorithm to individual patients. In this work we investigate how integrating scattering transformations - as special forms of wavelet transformations - into CNNs could improve the extraction of meaningful features. To this end, we developed a novel network module which uses features of a scattering transform for an attention mechanism. We observed that this approach improves the results of calcium segmentation up to 8.2 % (relatively) in terms of the Dice coefficient and 24.8 % in terms of the modified Hausdorff distance. In the case of lumen and vessel wall segmentation, the improvements are up to 2.3 % (relatively) in terms of the Dice coefficient and 30.8 % in terms of the modified Hausdorff distance. Incorporating scattering transformations as a component of an attention block into CNNs improves the segmentation results on small IVUS segmentation data sets. In general, scattering transformations can help in situations where efficient feature extractors can not be learned via the training data. This makes our attention module an interesting candidate for applications like few-shot learning for patient adaptation or detection of rare diseases.

Automatic delineation of relevant structures in intravascular imaging can support percutaneous coronary interventions (PCIs), especially when dealing with rather demanding cases. We found three major error types which occur regularly when segmenting lumen and wall of morphologically complex vessels with convolutional neural networks (CNNs). In order to reduce these three error types, we developed three IVUS-specific methods which are able to improve generalizability of state-of-the-art CNNs for IVUS segmentation tasks. These methods are based on three concepts: speckle statistics, artery shape priors via independent component analysis (ICA) and the concentricity condition of lumen and vessel wall. We found that all three methods outperform the baseline. Since all three concepts can be readily transferred to intravascular optical coherence tomography (IVOCT), we expect these findings can support the segmentation of corresponding images as well.

Coronary computed tomography angiography (coronary CTA) is a robust and well-established non-invasive diagnostic tool to detect and assess coronary artery disease (CAD). The accurate detection, quantification and characterization of the coronary plaque burden has become an important part of this imaging modality. The quality and performance of modern machine-learning-based data-driven learning approaches is often impacted by either insufficient or inconsistently-labeled training data and is further subject to additional bias from human annotators. To address these shortcomings for coronary plaque characterization, we have developed a synthetic lesion generating framework for CTA applications, which can produce accurate and high-quality labeled training data for data-driven learning approaches. This approach can help to ease the manual annotation burden, which is often the limiting factor in data-driven learning algorithms and instead provides reliable ground truth data for modern deep learning approaches. Furthermore, this framework can easily be used to create custom tailored training data that can be used for pre- or post-training steps of already existing machine learning approaches for CTA applications. We tested this data generation framework by inserting synthetic lesions in 11 clinical CTA scans of healthy patients resulting in a data set of ~7000 annotated 2D slices. With this data we performed several plaque detection experiments using a data-driven machine learning approach with a neural encoder architecture. In this plaque classification task we first demonstrate that the synthetic lesion generation module can consistently perform well in recognizing unseen synthetic test data with an overall classification accuracy of 93%. Next we apply the synthetic lesion framework in a transfer learning experiment, where we demonstrate the feasibility to learn to classify real clinical plaque lesions with a purely synthetic model (overall classification accuracy 84%) that never saw real clinical lesions during model training. Second, we show that using synthetically data for pre-training with a subsequent training on clinical data can enhance the overall classification accuracy (from 91% to 92%) while strongly increasing the true positive count. We conclude that the synthetic plaque lesions model faithfully covers many important image characteristics of real plaque lesions in coronary CTA imaging and can thus help reduce the annotation burden for data-driven predictive vascular systems in this domain. This allows the creation of exhaustively annotated and site-specific customizable training data with a computationally fast forward model.

Algorithms for automated analysis of intravascular ultrasound (IVUS) images can be disturbed by guidewires, which are often encountered when treating bifurcations in percutaneous coronary interventions. Detecting guidewires in advance can therefore help avoiding potential errors. This task is not trivial, since guidewires appear rather small compared to other relevant objects in IVUS images. We employed CNNs with additional multi-task learning as well as different guidewire-specific regularizations to enable and improve guidewire detection. In this context, we developed a network block which generates heatmaps that highlight guidewires without the need of localization annotations. The guidewire detection results reach values of 0.931 in terms of the F1-score and 0.996 in terms of area under curve (AUC). Comparing thresholded guidewire heatmaps with ground truth segmentation masks leads to a Dice score of 23.1 % and an average Hausdorff distance of 1.45 mm. Guidewire detection has proven to be a task that CNNs can handle quite well. Employing multi-task learning and guidewire-specific regularizations further improve detection results and enable generation of heatmaps that indicate the position of guidewires without actual labels.

Poster: ECR 2020 / C-04985 / Deep Learning for Pneumothorax Detection in CT Localizer Scans by: C. Bauer1, M. Grass1, N. Cross2, T. Mabotuwana3, U. van Stevendaal1, A. Saalbach 1; 1Hamburg/DE, 2Seattle/US, 3Bothell/US

Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are not applicable. We propose a fully automatic Dynamic Pacemaker Artifact Reduction (DyPAR+) pipeline which is built of three convolutional neural network (CNN) ensembles. In a first step, pacemaker metal shadows are segmented directly in the raw projection data by the SegmentationNets. Second, resulting metal shadow masks are passed to the InpaintingNets which replace metal-affected line integrals in the sinogram for subsequent reconstruction of a metal-free image volume. Third, the metal locations in a pre-selected motion state are predicted by the ReinsertionNets based on a stack of partial angle back-projections generated from the segmented metal shadow mask. We generate the data required for the supervised learning processes by introducing synthetic, moving pacemaker leads into 14 clinical cases without pacemakers. The SegmentationNets and the ReinsertionNets achieve average Dice coefficients of 94.16% +/- 2.01% and 55.60% +/- 4.79% during testing on clinical data with synthetic metal leads. With a mean absolute reconstruction error of 11.54 HU +/- 2.49 HU in the image domain, the InpaintingNets outperform the hand-crafted approaches PatchMatch and inverse distance weighting. Application of the proposed DyPAR+ pipeline to nine clinical test cases with real pacemakers leads to significant reduction of metal artifacts and demonstrates the transferability to clinical practice. Especially the SegmentationNets and InpaintingNets generalize well to unseen acquisition modes and contrast protocols. (C) 2020 Elsevier B.V. All rights reserved.

Objective The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI—resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist. Methods We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing “first-in, first-out” (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination. Results The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our “upper limit” substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001). Conclusion Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO. Key Points • Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence. • Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time. • Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).

Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classification have been developed. In this contribution we investigate the usefulness of two advanced image pre-processing techniques, initially developed for image reading by radiologists, for the performance of Deep Learning methods. First, we use bone suppression, an algorithm to artificially remove the rib cage. Secondly, we employ an automatic lung field segmentation to crop the image to the lung area. Furthermore, we consider the combination of both in the context of an ensemble approach.In a five-times re-sampling scheme, we use Receiver Operating Characteristic (ROC) statistics to evaluate the effect of the pre-processing approaches. Using a Convolutional Neural Network (CNN), optimized for X-ray analysis, we achieve a good performance with respect to all pathologies on average. Superior results are obtained for selected pathologies when using pre-processing, i.e. for mass the area under the ROC curve increased by 9.95%. The ensemble with pre-processed trained models yields the best overall results.

BACKGROUND:Vasodilator stress computed tomography perfusion (sCTP) imaging is complementary to coronary CT angiography (CCTA), used to determine the hemodynamic significance of coronary artery disease. However, it requires a separate image acquisition due to motion artifacts caused by higher heart rates during stress, resulting in increased iodine contrast dose and radiation. We sought to determine whether a novel motion correction algorithm applied to stress images would improve the visualization of the coronary arteries to potentially allow CCTA + sCTP evaluation in a single scan. METHODS:28 patients referred for clinically indicated CCTA (iCT, Philips) underwent sCTP imaging (retrospective-gating with dose modulation; 100 kVp and 250 mA; 5.2 ± 4.3 mSv) after regadenoson (0.4 mg, Astellas). Stress images were reconstructed using standard filtered back-projection (FBP) and also processed to generate interaction-free coronary motion-compensated back-projection reconstructions (MCR). Each coronary artery from standard FBP and MCR images was viewed side-by-side by a reader blinded to the reconstruction technique, who graded severity of motion artifact by segment (scale 0-5, with 3 as the threshold for diagnostic quality) and to measure signal-to-noise and contrast-to-noise ratios (SNR, CNR). RESULTS:Visualization scores were higher with MCR for all coronary segments, including 14/86 (16%) segments deemed as non-diagnostic on FBP images. SNR (7 ± 2) and CNR (15 ± 8) were unchanged by motion-correction (7 ± 3, p = 0.88 and 15 ± 5, p = 0.94, respectively). CONCLUSIONS:MCR improves the visualization of coronary anatomy on sCTP images without degrading image characteristics. This algorithm is an important step towards the combined assessment of coronary anatomy and myocardial perfusion in a single scan, which will reduce study time, radiation exposure and contrast dose.

OBJECTIVE: The goal of this study was to develop and validate a system for automatic segmentation of the spine, pedicle identification, and screw path suggestion for use with an intraoperative 3D surgical navigation system.METHODS: Cone-beam CT (CBCT) images of the spines of 21 cadavers were obtained. An automated model-based approach was used for segmentation. Using machine learning methodology, the algorithm was trained and validated on the image data sets. For measuring accuracy, surface area errors of the automatic segmentation were compared to the manually outlined reference surface on CBCT. To further test both technical and clinical accuracy, the algorithm was applied to a set of 20 clinical cases. The authors evaluated the system's accuracy in pedicle identification by measuring the distance between the user-defined midpoint of each pedicle and the automatically segmented midpoint. Finally, 2 independent surgeons performed a qualitative evaluation of the segmentation to judge whether it was adequate to guide surgical navigation and whether it would have resulted in a clinically acceptable pedicle screw placement.RESULTS: The clinically relevant pedicle identification and automatic pedicle screw planning accuracy was 86.1%. By excluding patients with severe spinal deformities (i.e., Cobb angle > 75° and severe spinal degeneration) and previous surgeries, a success rate of 95.4% was achieved. The mean time (± SD) for automatic segmentation and screw planning in 5 vertebrae was 11 ± 4 seconds.CONCLUSIONS: The technology investigated has the potential to aid surgeons in navigational planning and improve surgical navigation workflow while maintaining patient safety.