Virtual / Augmented Reality-based Visualization
This topic deals with the design of
High-fidelity visualization of large medical datasets on commodity hardware
A novel rendering method aimed at providing medical-quality rendering of large medical datasets using an ordinary desktop PC. GPU-based volume rendering cannot provide high-fidelity images when datasets do not fit into the graphics memory, whereas CPU-based volume rendering cannot provide an adequate frame rate when large datasets are used.
The hybrid approach takes advantage of the heterogeneity of the resources available on off-the-shelf computers: the large availability of system memory and the parallel-oriented architecture of modern GPUs. The CPU is used to render in high resolution a region-of-interest of the volume, whereas the GPU renders the context using a downsampled version of the dataset.
Medical Imaging Toolkit
MITO (Medical Imaging TOolkit) is an open-source, cross-platform software architecture for advanced Medical Imaging. MITO makes it possible to fetch radiological information and images stored in a PACS according to the standard format DICOM, then provides the final user with basic functionalities such as 2D-3D visualization (VR, SR, MIP), image segmentation and fusion, ROI. Moreover, MITO provides interaction techniques for manipulating 3D medical data in a virtual environment by 2 DOF input devices.
Low-cost Medical In-situ Visualization
Medical in-situ visualization deals with the display of patient specific imaging data at the location where they actually are. To be effective, it requires high end I/O devices, and computationally expensive and time-consuming algorithms.
In this work, we explored the potential simplifications derived from the use of 3D point cloud sensors in medical augmented reality applications by designing a low-cost system that takes advantage of depth data to apply medical imagery to live video streams of patients.