Importance-Driven Volume Rendering
VIS '04 Proceedings of the conference on Visualization '04
Exploded Views for Volume Data
IEEE Transactions on Visualization and Computer Graphics
A Flexible Multi-Volume Shader Framework for Arbitrarily Intersecting Multi-Resolution Datasets
IEEE Transactions on Visualization and Computer Graphics
High-Quality Multimodal Volume Rendering for Preoperative Planning of Neurosurgical Interventions
IEEE Transactions on Visualization and Computer Graphics
Dynamic Shader Generation for GPU-Based Multi-Volume Ray Casting
IEEE Computer Graphics and Applications
IEEE Transactions on Visualization and Computer Graphics
Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures
IEEE Transactions on Visualization and Computer Graphics
The Occlusion Spectrum for Volume Classification and Visualization
IEEE Transactions on Visualization and Computer Graphics
Multimodal Vessel Visualization of Mouse Aorta PET/CT Scans
IEEE Transactions on Visualization and Computer Graphics
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Comparing ensembles of learners: detecting prostate cancer from high resolution MRI
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Visual support for interactive post-interventional assessment of radiofrequency ablation therapy
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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Prostate cancer is one of the most prevalent cancers among males, and the use of magnetic resonance imaging (MRI) has been suggested for its detection. A framework is presented for scoring and visualizing various MR data in an efficient and intuitive manner. A classification method is introduced where a cumulative score volume is created which takes into account each of three acquisition types. This score volume is integrated into a volume rendering framework which allows the user to view the prostate gland, the multi-modal score values, and the surrounding anatomy. A visibility persistence mode is introduced to automatically avoid full occlusion of a selected score and indicate overlaps. The use of GPU-accelerated multi-modal single-pass ray casting provides an interactive experience. User driven importance rendering allows the user to gain insight into the data and can assist in localization of the disease and treatment planning. We evaluate our results against pathology and radiologists' determinations.