Person: Kikinis, Ron
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Kikinis
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Ron
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Kikinis, Ron
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Publication Instrument-mounted displays for reducing cognitive load during surgical navigation(Springer International Publishing, 2017) Herrlich, Marc; Tavakol, Parnian; Black, David; Wenig, Dirk; Rieder, Christian; Malaka, Rainer; Kikinis, RonPurpose Surgical navigation systems rely on a monitor placed in the operating room to relay information. Optimal monitor placement can be challenging in crowded rooms, and it is often not possible to place the monitor directly beside the situs. The operator must split attention between the navigation system and the situs. We present an approach for needle-based interventions to provide navigational feedback directly on the instrument and close to the situs by mounting a small display onto the needle. Methods By mounting a small and lightweight smartwatch display directly onto the instrument, we are able to provide navigational guidance close to the situs and directly in the operator’s field of view, thereby reducing the need to switch the focus of view between the situs and the navigation system. We devise a specific variant of the established crosshair metaphor suitable for the very limited screen space. We conduct an empirical user study comparing our approach to using a monitor and a combination of both. Results Results from the empirical user study show significant benefits for cognitive load, user preference, and general usability for the instrument-mounted display, while achieving the same level of performance in terms of time and accuracy compared to using a monitor. Conclusion We successfully demonstrate the feasibility of our approach and potential benefits. With ongoing technological advancements, instrument-mounted displays might complement standard monitor setups for surgical navigation in order to lower cognitive demands and for improved usability of such systems.Publication Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆(Elsevier, 2012) Irimia, Andrei; Wang, Bo; Aylward, Stephen R.; Prastawa, Marcel W.; Pace, Danielle F.; Gerig, Guido; Hovda, David A.; Kikinis, Ron; Vespa, Paul M.; Van Horn, John D.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.Publication Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning(Oxford University Press (OUP), 2011) Golby, Alexandra; Kindlmann, Gordon; Norton, Isaiah Hakim; Yarmarkovich, Alexander; Pieper, Steven; Kikinis, RonBACKGROUND: Diffusion tensor imaging (DTI) infers the trajectory and location of large white matter tracts by measuring the anisotropic diffusion of water. DTI data may then be analyzed and presented as tractography for visualization of the tracts in 3 dimensions. Despite the important information contained in tractography images, usefulness for neurosurgical planning has been limited by the inability to define which are critical structures within the mass of demonstrated fibers and to clarify their relationship to the tumor. OBJECTIVE: To develop a method to allow the interactive querying of tractography data sets for surgical planning and to provide a working software package for the research community. METHODS: The tool was implemented within an open source software project. Echo-planar DTI at 3 T was performed on 5 patients, followed by tensor calculation. Software was developed that allowed the placement of a dynamic seed point for local selection of fibers and for fiber display around a segmented structure, both with tunable parameters. A neurosurgeon was trained in the use of software in < 1 hour and used it to review cases. RESULTS: Tracts near tumor and critical structures were interactively visualized in 3 dimensions to determine spatial relationships to lesion. Tracts were selected using 3 methods: anatomical and functional magnetic resonance imaging-defined regions of interest, distance from the segmented tumor volume, and dynamic seed-point spheres. CONCLUSION: Interactive tractography successfully enabled inspection of white matter structures that were in proximity to lesions, critical structures, and functional cortical areas, allowing the surgeon to explore the relationships between them.Publication A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients(Oxford University Press (OUP), 2011) Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William; Kikinis, Ron; Black, PeterBACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.Publication Extended Broca’s Area in the Functional Connectome of Language in Adults: Combined Cortical and Subcortical Single-Subject Analysis Using fMRI and DTI Tractography(Springer Nature, 2012) Lemaire, Jean-Jacques; Golby, Alexandra; Wells, William; Pujol, Sonia; Tie, Yanmei; Rigolo, Laura; Yarmarkovich, Alexander; Pieper, Steve; Westin, Carl-Fredrik; Jolesz, Ferenc; Kikinis, RonTraditional models of the human language circuitry encompass three cortical areas, Broca’s, Geschwind’s and Wernicke’s, and their connectivity through white matter fascicles. The neural connectivity deep to these cortical areas remains poorly understood, as does the macroscopic functional organization of the cortico-subcortical language circuitry. In an effort to expand current knowledge, we combined functional MRI (fMRI) and diffusion tensor imaging to explore subject-specific structural and functional macroscopic connectivity, focusing on Broca’s area. Fascicles were studied using diffusion tensor imaging fiber tracking seeded from volumes placed manually within the white matter. White matter fascicles and fMRI-derived clusters (antonym-generation task) of positive and negative blood-oxygen-level-dependent (BOLD) signal were co-registered with 3-D renderings of the brain in 12 healthy subjects. Fascicles connecting BOLD-derived clusters were analyzed within specific cortical areas: Broca’s, with the pars triangularis, the pars opercularis, and the pars orbitaris; Geschwind’s and Wernicke’s; the premotor cortex, the dorsal supplementary motor area, the middle temporal gyrus, the dorsal prefrontal cortex and the frontopolar region. We found a functional connectome divisible into three systems—anterior, superior and inferior—around the insula, more complex than previously thought, particularly with respect to a new extended Broca’s area. The extended Broca’s area involves two new fascicles: the operculo-premotor fascicle comprised of well-organized U-shaped fibers that connect the pars opercularis with the premotor region; and (2) the triangulo-orbitaris system comprised of intermingled U-shaped fibers that connect the pars triangularis with the pars orbitaris. The findings enhance our understanding of language function.Publication Morphological Characteristics of Brain Tumors Causing Seizures(American Medical Association (AMA), 2010) Lee, Jong; Wen, Patrick; Hurwitz, Shelley; Black, Peter; Kesari, Santosh; Drappatz, Jan; Golby, Alexandra; Wells, William; Warfield, Simon; Kikinis, Ron; Bromfield, Edward B.Objective: To quantify size and localization differences between tumors presenting with seizures vs nonseizure neurological symptoms. Design: Retrospective imaging survey. We performed magnetic resonance imaging–based morphometric analysis and nonparametric mapping in patients with brain tumors. Setting: University-affiliated teaching hospital. Patients or Other Participants: One hundred twenty-four patients with newly diagnosed supratentorial glial tumors. Main Outcome Measures: Volumetric and mapping methods were used to evaluate differences in size and location of the tumors in patients who presented with seizures as compared with patients who presented with other symptoms. Results: In high-grade gliomas, tumors presenting with seizures were smaller than tumors presenting with other neurological symptoms, whereas in low-grade gliomas, tumors presenting with seizures were larger. Tumor location maps revealed that in high-grade gliomas, deep-seated tumors in the pericallosal regions were more likely to present with nonseizure neurological symptoms. In low-grade gliomas, tumors of the temporal lobe as well as the insular region were more likely to present with seizures. Conclusions: The influence of size and location of the tumors on their propensity to cause seizures varies with the grade of the tumor. In high-grade gliomas, rapidly growing tumors, particularly those situated in deeper structures, present with non–seizure-related symptoms. In low-grade gliomas, lesions in the temporal lobe or the insula grow large without other symptoms and eventually cause seizures. Quantitative image analysis allows for the mapping of regions in each group that are more or less susceptible to seizures. Seizures are encountered in a majority of patients with primary brain tumors and are a major cause of morbidity in these patients.1,2 Thirty percent to 50% of patients experience a seizure by the time their tumors are diagnosed, and an additional 6% to 45% of patients who do not initially present with seizures eventually develop them.3- 5 Characteristics of brain tumors and their mechanism in causing seizures in patients are incompletely understood.4,6 Low-grade, well-differentiated gliomas,1,6- 9 cortically located tumors,3,10- 14 and location in the temporal/frontal and motor/sensory cortices6,8,15- 17 are more frequently associated with seizures. Although there is a high incidence of seizures in these patients, treatment strategies remain poorly defined. Prophylactic anticonvulsant therapy, shown to be ineffective in preventing seizures in patients with brain tumors in multiple large-scale studies,12,18- 20 is not recommended by the American Academy of Neurology.5 Nonetheless, prophylaxis remains a widespread practice21 because of difficulty in determining which patients are at greatest risk for seizures. Determination of morphometric factors influencing seizures would help in identifying patients at greatest risk for early, targeted treatment and prevent potentially toxic, unnecessary treatment in patients at minimal risk. Although studies examining brain tumors in relationship to epilepsy have localized tumors to a particular lobe,10 few studies have performed quantitative volumetric or spatial mapping analysis of tumors in relation to their epileptogenic potential. Regions within a particular lobe are likely to exhibit different epileptogenic potential to tumor invasion and tumors frequently affect multiple contiguous lobes.6 Modern imaging techniques allow for analysis of lesions over a large group of subjects through registration and mapping techniques. In this study, we used these techniques to examine the size and location of primary supratentorial glial brain tumors and characterized their propensity to cause seizures at presentation.Publication Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation(Public Library of Science, 2014) Parmar, Chintan; Rios Velazquez, Emmanuel; Leijenaar, Ralph; Jermoumi, Mohammed; Carvalho, Sara; Mak, Raymond; Mitra, Sushmita; Shankar, B. Uma; Kikinis, Ron; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, HugoDue to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.Publication Volumetric CT-based segmentation of NSCLC using 3D-Slicer(Nature Publishing Group, 2013) Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond; van Baardwijk, Angela; Fennessy, Fiona; Lewis, John H.; De Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, HugoAccurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.Publication Systemic chemotherapy decreases brain glucose metabolism(BlackWell Publishing Ltd, 2014) Horky, Laura L; Gerbaudo, Victor; Zaitsev, Alexander; Plesniak, Wen; Hainer, Jon; Govindarajulu, Usha; Kikinis, Ron; Dietrich, JorgObjective: Cancer patients may experience neurologic adverse effects, such as alterations in neurocognitive function, as a consequence of chemotherapy. The mechanisms underlying such neurotoxic syndromes remain poorly understood. We here describe the temporal and regional effects of systemically administered platinum-based chemotherapy on glucose metabolism in the brain of cancer patients. Methods: Using sequential FDG-PET/CT imaging prior to and after administration of chemotherapy, we retrospectively characterized the effects of intravenously administered chemotherapy on brain glucose metabolism in a total of 24 brain regions in a homogenous cohort of 10 patients with newly diagnosed non-small-cell lung cancer. Results: Significant alterations of glucose metabolism were found in response to chemotherapy in all gray matter structures, including cortical structures, deep nuclei, hippocampi, and cerebellum. Metabolic changes were also notable in frontotemporal white matter (WM) network systems, including the corpus callosum, subcortical, and periventricular WM tracts. Interpretation Our data demonstrate a decrease in glucose metabolism in both gray and white matter structures associated with chemotherapy. Among the affected regions are those relevant to the maintenance of brain plasticity and global neurologic function. This study potentially offers novel insights into the spatial and temporal effects of systemic chemotherapy on brain metabolism in cancer patients.Publication A Hierarchical Algorithm for MR Brain Image Parcellation(Institute of Electrical and Electronics Engineers (IEEE), 2007) Pohl, Kilian M.; Bouix, Sylvain; Nakamura, Motoaki; Rohlfing, Torsten; McCarley, Robert William; Kikinis, Ron; Grimson, W. Eric L.; Shenton, Martha; Wells, WilliamWe introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the sub-trees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p=0.07) was observed instead of statistical significance.