The next generation of medical imaging scanners are introducing new diagnostic capabilities that improve patient care. These medical images are multi-dimensional (3D), multi-modality (fusion of PET and MRI for example), total-body, and also time varying (that is, 3D volumes taken over multiple time points and functional MRI). It is becoming ever more important to have advanced visualization of the data: this involves computer graphics algorithms to present and interact with the imaging data that is not only rendered beautifully but also practical and efficient for the end users (clinicians). To achieve this, it is important to ‘pre-process’ the images, via image processing which includes artificial intelligence and machine learning, to derive meaningful semantic data that can aid in setting up the parameters for volume rendering. In this tutorial, we will present the state-of-the-art in research that couples volume rendering with image processing algorithms to render useful as well as aesthetic 3D visualization of the human body.

Tutorial Organizers and Presenters:
Prof Nadia Magnenat-Thalmann, University of Geneva, Switzerland and NTU, Singapore
A/Prof Bin Sheng, Shanghai Jiao Tong University
A/Prof Jinman Kim, The University of Sydney, Australia