"Multi-modal small-animal imaging: image processing challenges and applications"

Artem Khmelinskii

Abstract

In pre-clinical research, whole-body small animal imaging is widely used for the 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 radiography, CT, MRI and ultrasound provide detailed depictions of anatomy. Optical imaging modalities, such as BLI and near-infrared fluorescence imaging offer a high sensitivity in visualizing molecular processes in vivo. The combination of 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. The organization, analysis and interpretation of this heterogeneous whole-body imaging data has become a demanding task.

In this thesis, the data processing approach depicted in Figure 1.1 was further explored. This 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 articulated atlas has the advantage that all the different imaging modalities can be (semi) automatically 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 highthroughput whole-body data.

In this thesis, we have focused on three complementary aspects of the approach described in Figure 1.1, and worked towards an automated analysis pipeline for quantitative small animal image analysis. The specific goals of this thesis were:

1. to further generalize the articulated atlas-based registration method to the multi-modality component of the global approach presented in Figure 1.1, focusing on SPECT and MRI whole-body mouse data
2. to expand the Articulated Planar Reformation algorithm by linking it to recently introduced resolution-enhancing MR reconstruction techniques which enable "zooming in" on small anatomical details not detectable with conventional MRI
3. to prove the added value of atlas-based analysis of multi-modal follow-up data in a life-science study of the ageing processes in the brain, with a specific focus on multi-contrast MR rat brain data

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