In pre-clinical research, whole-body small-animal imaging is widely used for in vivo visualization of functional and anatomical information to study cancer, neurological and cardiovascular diseases... Show moreIn pre-clinical research, whole-body small-animal imaging is widely used for in vivo visualization of functional and anatomical information to study cancer, neurological and cardiovascular diseases and help with a faster development of new drugs. Functional information is provided by imaging modalities such as PET, SPECT and specialized MRI. Structural imaging modalities like CT, MRI and ultrasound provide detailed depictions of anatomy. Optical imaging modalities, such as BLI and near-infrared-fluorescence offer a high sensitivity in visualizing molecular processes in vivo. Combining these modalities enables to follow the subject(s) and molecular processes in time, in living animals. With these advances in image acquisition, the problem has shifted from data acquisition to data processing: organization, analysis and interpretation of this heterogeneous whole-body imaging data has become a demanding task. Here, our data processing approach is based on an articulated whole-body atlas as a common reference to normalize the geometric heterogeneity caused by postural and anatomical differences between individuals and geometric differences between imaging modalities. Mapping to this atlas has the advantage that all the different imaging modalities can be registered to a common anatomical reference; postural variations can be corrected, and the different animals can be scaled properly while allowing for proper management of this high-throughput data. Show less
In this thesis, several strategies are presented that aim to facilitate the analysis and visualization of whole-body in vivo data of small animals. Based on the particular challenges for image... Show moreIn this thesis, several strategies are presented that aim to facilitate the analysis and visualization of whole-body in vivo data of small animals. Based on the particular challenges for image processing, when dealing with whole-body follow-up data, we addressed several aspects in this thesis. The developed methods are tailored to handle data of subjects with significantly varying posture and address the large tissue heterogeneity of entire animals. In addition, we aim to compensate for lacking tissue contrast by relying on approximation of organs based on an animal atlas. Beyond that, we provide a solution to automate the combination of multimodality, multidimensional data. Show less
