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Rationale and Objectives: Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters.Materials and Methods: Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters Ktrans, ve, and kep was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology.Results: Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for kep, 28% for ve, and 83% for Ktrans . The best p values for discrimination between tissues were p <10-5 for kep and Ktrans, and p = 0.11 for ve. Addition of the BAT correction to the models did not improve repeatability.Conclusion: The parameter kep, using an arterial input functions directly measured from blood signals, was more repeatable than Ktrans. Both Ktrans and kep values were highly discriminatory between healthy and diseased tissues in all cases. The parameter ve had high repeatability but could not distinguish the two tissue types. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Anatomy is an essential component of medical education as it is critical for the accurate diagnosis in organs and human systems. The mental representation of the shape and organization of different anatomical structures is a crucial step in the learning process. The purpose of this pilot study is to demonstrate the feasibility and benefits of developing innovative teaching modules for anatomy education of first-year medical students based on three-dimensional (3D) reconstructions from actual patient data.Materials and Methods: A total of 196 models of anatomical structures from 16 anonymized computed tomography datasets were generated using the 3D Slicer open-source software platform. The models focused on three anatomical areas: the mediastinum, the upper abdomen, and the pelvis. Online optional quizzes were offered to first-year medical students to assess their comprehension in the areas of interest. Specific tasks were designed for students to complete using the 3D models.Results: Scores of the quizzes confirmed a lack of understanding of 3D spatial relationships of anatomical structures despite standard instruction including dissection. Written task material and qualitative review by students suggested that interaction with 3D models led to a better understanding of the shape and spatial relationships among structures, and helped illustrate anatomical variations from one body to another.Conclusions: The study demonstrates the feasibility of one possible approach to the generation of 3D models of the anatomy from actual patient data. The educational materials developed have the potential to supplement the teaching of complex anatomical regions and help demonstrate the anatomical variation among patients. [ABSTRACT FROM AUTHOR]

Abstract: Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community''s attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome. [Copyright &y& Elsevier]

Purpose: To report the use of a semiburied curvilinear distraction device, with a 3-dimensional (3D) computed tomography treatment planning system, for correction of mandibular deformities. Materials and Methods: This was a retrospective evaluation of 13 consecutive patients, with syndromic and nonsyndromic micrognathia, who underwent correction by curvilinear distraction osteogenesis. A 3D computed tomography scan was obtained for each patient and imported into a 3D treatment planning system (Slicer/Osteoplan). Surgical guides were constructed to localize the osteotomy and to drill holes to secure the distractor''s proximal and distal footplates to the mandible. Postoperatively, patients were followed by clinical examination and plain radiographs to ensure the desired vector of movement. At end distraction, when possible, a 3D computed tomography scan was obtained to document the final mandibular position. Results: Of the 13 patients, 8 were females and 5 were males, with a mean age of 11.9 years (range 15 months to 39 years). All 13 underwent bilateral mandibular curvilinear distraction. Of the 13 patients, 8 were 16 years old or younger and 5 were younger than 6 years of age. The diagnoses included Treacher Collins syndrome (n = 3), Nager syndrome (n = 3), craniofacial microsomia (n = 2), post-traumatic ankylosis (n = 1), and micrognathia (syndromic, n = 3; nonsyndromic, n = 1). The correct distractor placement, vector of movement, and final mandibular position were achieved in 10 of 13 patients. In the other 3 patients, the desired jaw position was achieved by “molding” the regenerate. Conclusions: The use of a semiburied curvilinear distraction device, with 3D treatment planning, is a potentially powerful tool to correct complex mandibular deformities. [Copyright &y& Elsevier]

Summary: Background: Surgical navigation requires registration of the pre-operative image dataset with the patient in the operation theatre. Various marker and marker-free registration techniques are available, each bearing an individual level of precision and clinical practicability. In this study the precision of four different registration methods in a maxillofacial surgical setting is analyzed. Materials and methods: A synthetic full size human skull model was registered with its computer tomography-dataset using (a) a dentally mounted occlusal splint, (b) the laser surface scanning, (c) five facial bone implants and (d) a combination of dental splint and two orbital bone implants. The target registration error was computed for 170 landmarks spread over the entire viscero- and neurocranium in 10 repeats using the VectorVision2® (BrainLAB AG, Heimstetten, Germany) navigation system. Statistical and graphical analyses were performed by anatomical region. Results: An average precision of 1mm was found for the periorbital region irrespective of registration method (range 0.6–1.1mm). Beyond the mid-face, precision linearly decreases with the distance from the reference markers. The combination of splint with two orbital bone markers significantly improved precision from 1.3 to 0.8mm (p <0.001) on the viscerocranium and 2.3–1.2mm (p <0.001) on the neurocranium. Conclusions: An occlusal splint alone yields poor precision for navigation beyond the mid-face. The precision can be increased by combining an occlusal splint with just two bone implants inserted percutaneously on the lateral orbital rim of each side. [Copyright &y& Elsevier]

Rationale and Objectives: This article presents an initiative for the translation of advances in neuroimage analysis techniques to clinical research scientists. Our objective is to bridge the gap between scientific advances made by the biomedical imaging community and their widespread use in the clinical research community. Through national collaborative effort supported by the National Institutes of Health Roadmap, the integration of the most sophisticated algorithms into usable working open-source systems enables clinical researchers to have access to a broad spectrum of cutting edge analysis techniques. A critical step to maximize the long-term positive impact of this collaborative effort is to translate these techniques into new skills of clinical researchers. To address this challenge, we developed a methodology based on three criteria: a multidisciplinary approach, a balance between theory and common practice, and an immersive collaborative environment. The article illustrates our initiative through the exemplar case of diffusion tensor imaging tractography, and reports on our experience over the past two years of designing and delivering training workshops to more than 300 clinicians and scientists using the developed methodology. [Copyright &y& Elsevier]

Rationale and Objectives: To perform a retrospective, quantitative assessment of the anatomic relationship between intra-axial, supratentorial, primary brain tumors, and adjacent white matter fiber tracts based on anatomic and diffusion tensor magnetic resonance imaging (MRI). We hypothesized that white matter infiltration may be common among different types of tumor.Material and Methods: Preoperative, anatomic (T1- and T2-weighted), and LINESCAN diffusion tensor MRI were obtained in 12 patients harboring supratentorial gliomas (World Health Organization [WHO] Grades II and III). The two imaging modalities were rigidly registered. The tumors were manually segmented from the T1- and T2-weighted MRI, and their volume calculated. A three-dimensional tractography was performed in each case. A second segmentation and volume measurement was performed on the tumor regions intersecting adjacent white matter fiber tracts. Statistical methods included summary statistics to examine the fraction of tumor volume infiltrating adjacent white matter.Results: There were five patients with low-grade oligodendroglioma (WHO Grade II), one with low-grade mixed oligoastrocytoma (WHO Grade II), one with ganglioglioma, two with low-grade astrocytoma (WHO Grade II), and three with anaplastic astrocytoma (WHO Grade III). We identified white matter tracts infiltrated by tumor in all 12 cases. The median tumor volume (+/- standard deviation) in our patient population was 42.5 +/- 28.9 mL. The median tumor volume (+/- standard deviation) infiltrating white matter fiber tracts was 5.2 +/- 9.9 mL. The median percentage of tumor volume infiltrating white matter fiber tracts was 21.4% +/- 9.7%.Conclusions: The information provided by diffusion tensor imaging combined with anatomic MRI might be useful for neurosurgical planning and intraoperative guidance. Our results confirm previous reports that extensive white matter infiltration by primary brain tumors is a common occurrence. However, prospective, large population studies are required to definitively clarify this issue, and how infiltration relates to histologic tumor type, tumor size, and location. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the capabilities of a neurovascular navigation prototype in phantom experiments. Materials and Methods: The proposed navigation system integrates three-dimensional (3D) visualization of the anatomy and real-time electromagnetic localization of the endovascular tools. A 3D model of an endovascular phantom was reconstructed from thresholded preprocedural computed tomographic (CT) data. The vascular model was aligned with the reference frame of an electromagnetic tracker by using paired-point matching based on eight external fiducials. The robustness and accuracy of the registration were evaluated in 29 experiments. A magnetically tracked catheter was inserted into the carotid artery of the phantom, and the navigation system was used to reach five predefined vascular landmarks. The spatial accuracy of the prototype was evaluated during 50 endovascular targeting attempts. Results: The navigation system achieved accurate co-registration of the location of a catheter inside a 3D reconstruction of a phantom vasculature. The experiments demonstrated the robustness of the registration, with a standard deviation for the translation and rotation components of 0.7 mm and 0.3 degrees , respectively. The maximal average error on the fiducials was 3.2 mm. Endovascular navigation by using the 3D real-time display was successfully performed with a mean overall accuracy of 2.7 mm +/- 0.7 and no projection limitation. Conclusion: The authors developed a navigation system that provides real-time 3D visualization of the position of endovascular components in a neurovascular phantom. The preliminary in vitro experiments showed clinically acceptable accuracy. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to determine the range of fixed trajectory curvilinear distraction devices required to correct a variety of severe mandibular deformities. Materials and Methods: Preoperative computed tomography (CT) scans from 18 patients with mandibular deformities were imported into a CT-based software program (Osteoplan). Three-dimensional virtual models of the individual skulls were made with landmarks to track movements. An ideal treatment plan was created for each patient. Upper and lower boundaries for the dimensions of curvilinear distractors were established based on manufacturing and geometric constraints. Then, anatomically acceptable distractor attachment points were identified on the models using proximal and distal grids. Treatment plans were simulated for a series of distractors with varying radii of curvature, elongations (arc-length of device), and placements along the grids. The outcomes using these distractors were compared with the ideal treatment plans. Discrepancies were quantified in millimeters by comparing landmarks in the simulated versus ideal movements. Results: Approximately 400,000 simulated 3-dimensional movements, based on the distractor parameters and variations in placement were computationally evaluated for the 18 cases. It was determined that, by varying distractor placement, a family of 5 distractors, with 3, 5, 7, and 10 cm radii of curvature and a straight-line device, could be used to treat all 18 cases to within 1.8 mm of error. Conclusions: The results of this study indicate that a family of 5 curvilinear distractors may suffice to treat a broad range of mandibular deformities. [Copyright &y& Elsevier]

Rationale and Objectives Both single-shot diffusion-weighted echo-planar imaging (EPI) and line scan diffusion imaging (LSDI) can be used to obtain magnetic resonance diffusion tensor data and to calculate directionally invariant diffusion anisotropy indices, ie, indirect measures of the organization and coherence of white matter fibers in the brain. To date, there has been no comparison of EPI and LSDI. Because EPI is the most commonly used technique for acquiring diffusion tensor data, it is important to understand the limitations and advantages of LSDI relative to EPI.Materials and methods Five healthy volunteers underwent EPI and LSDI diffusion on a 1.5 Tesla magnet (General Electric Medical Systems, Milwaukee, WI). Four-mm thick coronal sections, covering the entire brain, were obtained. In addition, one subject was tested with both sequences over four sessions. For each image voxel, eigenvectors and eigenvalues of the diffusion tensor were calculated, and fractional anisotropy (FA) was derived. Several regions of interest were delineated, and for each, mean FA and estimated mean standard deviation were calculated and compared.Results Results showed no significant differences between EPI and LSDI for mean FA for the five subjects. When intersession reproducibility for one subject was evaluated, there was a significant difference between EPI and LSDI in FA for the corpus callosum and the right uncinate fasciculus. Moreover, errors associated with each FA measure were larger for EPI than for LSDI.Conclusion Results indicate that both EPI- and LSDI-derived FA measures are sufficiently robust. However, when higher accuracy is needed, LSDI provides smaller error and smaller inter-subject and inter-session variability than EPI. [Copyright &y& Elsevier]

Rationale and Objectives. To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy.Materials and Methods. The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA).Results. Example 1: The mean DSCs of 0.883 (range, 0.876–0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819–0.852) with 0.5T intraoperative MRI (P <.001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519–0.893), astrocytomas (0.487–0.972), and other mixed gliomas (0.490–0.899).Conclusion. The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks. [Copyright &y& Elsevier]

Rationale and Objectives. A novel method to correct flow data from magnetic resonance phase contrast (MR-PC) angiography, based on combining computational fluid dynamics and segmentation in a level set framework, was developed and tested in this study.Materials and Methods. The MR-PC velocity data was used in a partial differential equation-based level set method for vessel segmentation. The results were supplied as the quantitative description of the vessel wall to the flow field solver using computational fluid dynamics, based on the level set method, to obtain a physically meaningful flow. The most significant characteristic of our novel approach is that it requires light computational loads, especially insofar as it avoids generation of complex computational grid system. The integration of segmentation and computational fluid dynamics in a level set framework is shown to be both robust and economic, and yet yields a physically correct velocity field and optimal vessel geometry.Results. The application to the flow field in a straight tube with circular cross section of constant radius demonstrated the validity of out new approach, especially the treatment of the velocity boundary conditions on the solid wall. Simulation of the velocity field in both common carotid artery and bifurcation of basilar and vertebral arteries, based on clinical MR-PC data, provided with smooth and stable results.Conclusion. Applying this procedure to both synthetic and clinical data, significant improvement of the blood velocity field, such as a smooth velocity distribution aligned along the vessels and removal of burst or error vectors, could be observed. This procedure also offers possibilities for improved vessel segmentation. [Copyright &y& Elsevier]

Rationale and Objectives. The accuracy of diagnostic test and imaging segmentation is important in clinical practice because it has a direct impact on therapeutic planning. Statistical validations of classification accuracy was conducted based on parametric receiver operating characteristic analysis, illustrated on three radiologic examples.Materials and Methods. Two parametric models were developed for diagnostic or imaging data. Example 1: A semi-automated fractional segmentation algorithm was applied to magnetic resonance imaging of nine cases of brain tumors. The tumor and background pixel data were assumed to have bi-beta distributions. Fractional segmentation was validated against an estimated composite pixel-wise gold standard based on multi-reader manual segmentations. Example 2: The predictive value of 100 cases of spiral computed tomography of ureteral stone sizes, distributed as bi-normal after a nonlinear transformation, under two treatment options received. Example 3: One hundred eighty cases had prostate-specific antigen levels measured in a prospective clinical trial. Radical prostatectomy was performed in all to provide a binary gold standard of local and advanced cancer stages. Prostate-specific antigen level was transformed and modeled by bi-normal distributions. In all examples, areas under the receiver operating characteristic curves were computed.Results. The areas under the receiver operating characteristic curves were: Example 1: Fractional segmentation of magnetic resonance imaging of brain tumors: meningiomas (0.924–0.984); astrocytomas (0.786–0.986); and other low-grade gliomas (0.896–0.983). Example 3: Ureteral stone size for treatment planning (0.813). Example 2: Prostate-specific antigen for staging prostate cancer (0.768).Conclusion. All clinical examples yielded fair to excellent accuracy. The validation metric area under the receiver operating characteristic curves may be generalized to evaluating the performances of several continuous classifiers related to imaging. [Copyright &y& Elsevier]

: Rationale and ObjectivesThe authors performed this study to document the deformations that occur between pretreatment magnetic resonance (MR) imaging and intraoperative MR imaging during brachytherapy.: Materials and MethodsMR images obtained at 1.5 and 0.5 T in 10 patients with prostate cancer were analyzed for changes in the shape and substructure of the prostate. Three-dimensional models of the prostate were obtained. The authors measured anteroposterior dimension; total gland, peripheral zone, and central gland volumes; transverse dimension; and superoinferior height.: ResultsGland deformations were seen at visual inspection of the three-dimensional models. The anteroposterior dimension of the total gland, central gland, and peripheral zone increased from 1.5- to 0.5-T imaging (median dimension, 4.9, 1.5, and 1.8 mm, respectively), and the increase was greatest in the peripheral zone (P < .05, all comparisons). There was a decrease in the transverse dimension from 1.5- to 0.5-T imaging (median, 4.5 mm; P < .005). The total gland volume and the superoinferior height did not show a statistically significant change.: ConclusionThere were significant deformations in the shape of the prostate, especially in the peripheral zone, between the two imaging studies. The likely causes of the shape change are differences in rectal filling (endorectal coil used in 1.5-T studies vs obturator in 0.5-T studies) and/or changes in patient position (supine vs lithotomy). These findings suggest that pretreatment images alone may not be reliable for accurate therapy planning. It may be useful to integrate pre- and intraoperative data. [Copyright &y& Elsevier]

A common symptom of the neuropsychiatric Post-Acute COVID-19 syndrome (neuro-PACS) is the so called 'brain fog'. Patients describe the brain fog as problems with attention, memory and mental fatigue. Brain fog is experienced by 9–55% of people for months after having contracted SARS-CoV-2 virus. Several theories have been proposed to explain PACS's brain fog, including a neuroinflammatory hypothesis, but the hypothesis remains to be proven. Here, we examined inflammatory and immunological blood profile in a cohort of patients with PACS to investigate the association between executive functions and blood inflammatory markers. Executive function was assessed by the Trail Making Test (TMT) Part A and Part B, as well as the Barkley Deficits in Executive Functioning Scale (BDEFS), in 71 patients (36 men), average age of 40 years (range: 15–82, SD: 15.7). Impairment in executive functioning (BDEFS scores and TMT B scores) correlated with increased levels of Interleukin-6 (IL-6), fibrinogen and ferritin. Moreover, elevated levels of Il-6, fibrinogen, ferritin, tumor necrosis factor-alpha and C-reactive protein have been observed in PACS. These findings demonstrate that PACS is characterized by the presence of an immuno-inflammatory process, which is associated with diminished executive functioning. Here, we argue in favour of a shift from the non-descriptive definition of 'mental fog' to a characterization of a subtype of PACS, associated with alteration in executive functioning. Implication for clinical settings and prevention are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This contribution presents finite element computation of the deformation field within the brain during craniotomy-induced brain shift. The results were used to illustrate the capabilities of non-linear (i.e. accounting for both geometric and material non-linearities) finite element analysis in non-rigid registration of pre- and intra-operative magnetic resonance images of the brain. We used patient-specific hexahedron-dominant finite element mesh, together with realistic material properties for the brain tissue and appropriate contact conditions at boundaries. The model was loaded by the enforced motion of nodes (i.e. through prescribed motion of a boundary) at the brain surface in the craniotomy area. We suggest using explicit time-integration scheme for discretised equations of motion, as the computational times are much shorter and accuracy, for practical purposes, the same as in the case of implicit integration schemes. Application of the computed deformation field to register (i.e. align) the pre-operative images with the intra-operative ones indicated that the model very accurately predicts the displacements of the tumour and the lateral ventricles even for limited information about the brain surface deformation. The prediction accuracy improves when information about deformation of not only exposed (during craniotomy) but also unexposed parts of the brain surface is used when prescribing loading. However, it appears that the accuracy achieved using information only about the deformation of the exposed surface, that can be determined without intra-operative imaging, is acceptable. The presented results show that non-linear biomechanical models can complement medical image processing techniques when conducting non-rigid registration. Important advantage of such models over the previously used linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain stress–strain relationship is linear. [Copyright &y& Elsevier]

As medical data acquisition has become digital and tomographical scanners allow three-dimensional reconstruction in ease, it is still standard to analyze the data plane by plane or in a multiplanar view. Both are limited to two-dimensional environments and cause difficulties in analyzing complex anatomical and pathological structures. Decreasing hardware prices and PC-clusters, which can compare with more expensive super-computers, will help implementing virtual environments in medicine for teaching, simulation and interacting with medical image data. Using free open source software, such as 3D-Slicer, for segmentation and preparing the data for the CAVE is quick and suitable for clinical use as well as for education. For educational purposes, the use of very high-resolution data sets will be ideal and could be made available for everyone, based on the open source philosophy. [Copyright &y& Elsevier]

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use uni-directional or bi-directional measurements. Segmentation in 3D can provide more accurate evaluations of the lymph node size. Fully convolutional neural networks (FCNs) have achieved state-of-the-art results in segmentation for numerous medical imaging applications, including lymph node segmentation. Adoption of deep learning segmentation models in clinical trials often faces numerous challenges. These include lack of pixel-level ground truth annotations for training, generalizability of the models on unseen test domains due to the heterogeneity of test cases and variation of imaging parameters. In this paper, we studied and evaluated the performance of lymph node segmentation models on a dataset that was completely independent of the one used to create the models. We analyzed the generalizability of the models in the face of a heterogeneous dataset and assessed the potential effects of different disease conditions and imaging parameters. Furthermore, we systematically compared fully-supervised and weakly-supervised methods in this context. We evaluated the proposed methods using an independent dataset comprising 806 mediastinal lymph nodes from 540 unique patients. The results show that performance achieved on the independent test set is comparable to that on the training set. Furthermore, neither the underlying disease nor the heterogeneous imaging parameters impacted the performance of the models. Finally, the results indicate that our weakly-supervised method attains 90%− 91% of the performance achieved by the fully supervised training. • Proposed lymph node segmenters generalized well on an unseen heterogeneous dataset. • Patient disease and imaging parameters did not impact the segmentation performance. • Weakly supervision achieved comparable performance to fully supervised training. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • An uncertainty-aware asynchronous scattered motion interpolation method is proposed. • The method uses non-constant-mean anisotropic-covariance Gaussian process regression. • The motion measurements are defined using a novel motion field representation. • The method outperforms the state-of-the-art Gaussian process regression models. • The method allows for uncertainty-aware registration of soft-tissue navigation data. Abstract We address the problem of interpolating randomly non-uniformly spatiotemporally scattered uncertain motion measurements, which arises in the context of soft tissue motion estimation. Soft tissue motion estimation is of great interest in the field of image-guided soft-tissue intervention and surgery navigation, because it enables the registration of pre-interventional/pre-operative navigation information on deformable soft-tissue organs. To formally define the measurements as spatiotemporally scattered motion signal samples, we propose a novel motion field representation. To perform the interpolation of the motion measurements in an uncertainty-aware optimal unbiased fashion, we devise a novel Gaussian process (GP) regression model with a non-constant-mean prior and an anisotropic covariance function and show through an extensive evaluation that it outperforms the state-of-the-art GP models that have been deployed previously for similar tasks. The employment of GP regression enables the quantification of uncertainty in the interpolation result, which would allow the amount of uncertainty present in the registered navigation information governing the decisions of the surgeon or intervention specialist to be conveyed. [ABSTRACT FROM AUTHOR]

Implant placement has been widely used in various kinds of surgery. However, accurate intraoperative drilling performance is essential to avoid injury to adjacent structures. Although some commercially-available surgical navigation systems have been approved for clinical applications, these systems are expensive and the source code is not available to researchers. 3D Slicer is a free, open source software platform for the research community of computer-aided surgery. In this study, a loadable module based on Slicer has been developed and validated to support surgical navigation. This research module allows reliable calibration of the surgical drill, point-based registration and surface matching registration, so that the position and orientation of the surgical drill can be tracked and displayed on the computer screen in real time, aiming at reducing risks. In accuracy verification experiments, the mean target registration error (TRE) for point-based and surface-based registration were 0.31 ± 0.06 mm and 1.01 ± 0.06 mm respectively, which should meet clinical requirements. Both phantom and cadaver experiments demonstrated the feasibility of our surgical navigation software module. [ABSTRACT FROM AUTHOR]