Virtual / Augmented Reality-based Visualization

This topic deals with the design of Medical Imaging software applications. To goal is to support medical experts in the 3D reconstruction of anatomical structures coming from DICOM images, by offering a threedimensional visualization of all volumetric data with a high degree of accuracy.

High-fidelity visualization of large medical datasets on commodity hardware

hybridA 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

MITOMITO (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

medicalARMedical 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.