Abstract

Bioimaging is a vital tool in the fields of biology and medicine. For instance, it enables the study of biological processes contributing to a better understanding of mechanisms triggering the development of diseases such as cancer. As a diagnostic tool, it is essential for the detection of diseases at a very early stage. Given the vast heterogeneity in diseases and therefore a patient's need for personalized diagnosis, the concept of multimodality is receiving increasing attention (Lee et al. 2012). In fact, multimodal imaging is a powerful tool combining the advantages of several imaging modalities while overcoming their intrinsic individual limitations. Among the most commonly applied techniques used for multimodal approaches are, for example, magnetic resonance imaging (MRI; pros: high spatial resolution, high penetration depth; cons: low sensitivity, long imaging time), computed tomography (CT; pros: high spatial resolution, high penetration depth; cons: radiation risk, not quantitative), ultrasound (US; pros: real time, low cost; cons: low resolution, operator-dependent analysis), positron emission tomography (PET; pros: high sensitivity, no penetration depth limit, quantitative, whole body scan; cons: radiation risk, high cost), single photon emission computed tomography (SPECT; pros: high sensitivity, no penetration depth limit; cons: radiation risk, low spatial resolution), and optical imaging (pros: high sensitivity, multicolor, high temporal resolution; cons: low spatial resolution, low penetration depth). This multimodal approach results in more powerful imaging tools providing increasingly reliable and accurate information, for instance increased sensitivity and higher spatial and temporal resolution when compared to single imaging modalities (Lee et al. 2012).