Presented herein is the initial clinical experience with 3D printing to facilitate patient's pre-surgical understanding of their kidney tumor and surgery.

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Purpose To develop and apply DTI based normalization methodology for the detection and quantification of traumatic brain injury (TBI) and the impact of injury along specific brain pathways in: a) individual TBI subjects, and b) a TBI group. Materials and Methods Normalized DTI tractography was conducted in the native space of 12 TBI and 10 age-matched control subjects using the same number of seeds in each subject, distributed at anatomically equivalent locations. Whole-brain tracts from the control group were mapped onto the head of each TBI subject. Differences in the Fractional Anisotropy (FA) maps between each TBI subject and the control group were computed in a common space using a t-test, transformed back to the individual TBI subject's head-space, and thresholded to form Regions of Interest (ROIs) that were used to sort tracts from the control group and the individual TBI subject. Tract-counts for a given ROI in each TBI subject were compared to group mean for the same ROI to quantify impact of injury along affected pathways. Same procedure was used to compare TBI group to control group in a common space. Results Sites of injury within individual TBI subjects and affected pathways included hippocampal/fornix, inferior fronto-occipital, inferior longitudinal fasciculus, corpus callosum (genu and splenium), cortico-spinal tracts and the uncinate fasciculus. Most of these regions were also detected in the group study. Conclusions The DTI normalization methodology presented here enables automatic delineation of ROIs within the heads of individual subjects (or in a group). These ROIs not only localize and quantify the extent of injury, but also quantify the impact of injury on affected pathways in an individual or a group of TBI subjects.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta-and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Objective: To determine the intra-, inter-and test-retest variability of CT-based texture analysis (CTTA) metrics. Materials and methods: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. Results: As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. Conclusion: Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.