An integrated visual analysis system for fusing MR spectroscopy and multi-modal radiology imaging

• Published in: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) pp. 53 - 62
• Main Authors: Nunes, Miguel, Rowland, Benjamin, Schlachter, Matthias, Ken, Soleakhena, Matkovic, Kresimir, Laprie, Anne, Buhler, Katja
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2014
• Subjects: Biomedical imaging; brain; Brushes; cancer; Data visualization; medical decision support systems; MR spectroscopy; multi-modality data; Planning; radiotherapy planning; Spectroscopy; Tumors; visualization
• DOI: 10.1109/VAST.2014.7042481

For cancers such as glioblastoma multiforme, there is an increasing interest in defining "biological target volumes" (BTV), high tumour-burden regions which may be targeted with dose boosts in radiotherapy. The definition of a BTV requires insight into tumour characteristics going beyond conventionally defined radiological abnormalities and anatomical features. Molecular and biochemical imaging techniques, like positron emission tomography, the use of Magnetic Resonance (MR) Imaging contrast agents or MR Spectroscopy deliver this information and support BTV delineation. MR Spectroscopy Imaging (MRSI) is the only non-invasive technique in this list. Studies with MRSI have shown that voxels with certain metabolic signatures are more susceptible to predict the site of relapse. Nevertheless, the discovery of complex relationships between a high number of different metabolites, anatomical, molecular and functional features is an ongoing topic of research - still lacking appropriate tools supporting a smooth workflow by providing data integration and fusion of MRSI data with other imaging modalities. We present a solution bridging this gap which gives fast and flexible access to all data at once. By integrating a customized visualization of the multi-modal and multi-variate image data with a highly flexible visual analytics (VA) framework, it is for the first time possible to interactively fuse, visualize and explore user defined metabolite relations derived from MRSI in combination with markers delivered by other imaging modalities. Real-world medical cases demonstrate the utility of our solution. By making MRSI data available both in a VA tool and in a multi-modal visualization renderer we can combine insights from each side to arrive at a superior BTV delineation. We also report feedback from domain experts indicating significant positive impact in how this work can improve the understanding of MRSI data and its integration into radiotherapy planning.