KEYWORDS: Visualization, Anatomy, Tumors, Visual process modeling, Voxels, Data modeling, 3D modeling, Process modeling, Cameras, Tumor growth modeling
While there is increased interest in medical and scientific computational modeling tools for generating in silico medical datasets, tools for visualizing the volumetric data present with hurdles for those without previous experience in graphics rendering. We describe an open-source automatedworkflowto visualize volumetric computational medical imaging datasets with a focus on cancer lesion growth models. Simulated raw data for the growth of a tumor were generated at 50 time points using a previously described growth algorithm that considers the surrounding anatomy to affect tumor morphology. The voxelized models were converted to the VDB volume format for rendering using an automated Python script within the software Houdini. The visualization of volume data allows for detailed inspection and improved understanding of the spatial configuration of the tumor and surrounding anatomy affecting the growth.
Emerging uses for extended-reality (XR) head-mounted displays (HMDs) within medical environments include visualizations of medical data across various imaging modalities including radiography, computed tomography, ultrasound, and magnetic resonance images. Rendering medical data in XR environments requires real-time updates to account for user movement within the environment. Unlike stationary 2D medical displays, XR HMDs also require real-time stereoscopic rendering capabilities with high performance graphics processing units. Furthermore, performance depends on the status of added systems including tracking sensor technology, user's input data, and in the case of augmented reality (AR), spatial mapping and image registration. These temporal considerations have implications for the interpretation of medical data. However, methods for the evaluation of their effects on image quality are not yet well defined. The definition of these effects in the context of medical XR devices is at best inconsistent if not completely lacking. In this work, we compare the effects and causes for three classes of XR spatiotemporal characteristics affecting medical image quality: temporal artifacts, luminance artifacts, and spatial mapping artifacts. We describe the XR system components starting from user movement recognized by inertial measurement unit and camera sensors and ending with user perception of the display through the optics of the HMD. We summarize our findings and highlight device performance areas contributing to the different effects.
We demonstrate that transverse chromatic aberration (TCA) measurements can be used to determine the eyebox of a virtual reality head-mounted display using a digital test pattern, which consists of red, green, and blue bars placed horizontally and vertically at ±5°, ±10°, ±15° in the field of view. The pattern also features a white cross in the center of the field of view to determine the horizontal and vertical resolution. This provides simultaneous measurements of the TCAs and resolution for a given position of the camera in the eyebox. A map of the eyebox was generated by raster-scanning the position of the camera over the eyebox. The results for the Oculus Rift show that resolution has approximately a quadratic dependence on the position in the eyebox whereas the response to TCAs is linear. Therefore, TCA provides a more sensitive eyebox measurement at small displacements allowing for repeatable centering within 0.5 mm.
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