Exploration of fused multi-volume images using user-defined binary masks

Acquisition and fusion of multiple imaging modalities is becoming an increasingly desired clinical practice. This is particularly the case in radiation therapy, where dosage must be determined using electron density calculations from CT images, which lack the contrast to resolve soft-tissue structures. This often necessitates the fusion of corresponding MRI images in order to plot radiation trajectories safely around critical tissues. Simultaneous visualization of multiple volumes using direct volume rendering (DVR) techniques offers a number of advantages over traditional visualization methods. Specifically, fused visualization enhances the relational aspects of volumes, providing improved context [cite the first one]. However, there are many challenges involved in implementing DVR using fused data sets. The primary challenge is determining how images overlap to provide meaningful information. Additionally, there is increased computational complexity beyond standard DVR techniques, threatening real-time applications of fused DVR. These difficulties are evident to users of such systems as they must manage complex user interfaces and poor application performance. In this work, we introduce a user-centric multi-volume DVR technique which addresses issues of performance and ease of use. Through an intuitive interface, users are able to spatially define regions of interest, determining the relative contributions of each modality in the output rendering.