Integrative Visualization of Whol Body Molecular Imaging Data

The miniaturization of image acquisition hardware in recent decades has enabled a detailed study of structure, anatomy and function in animal models. Specifically, molecular imaging methods such as bioluminescence imaging, fluorescence imaging, µPET and µSPECT imaging enable the study of specific cellular processes. In addition, structural imaging techniques such as µCT, µMRI and ultrasound provide a detailed depiction of anatomy, and can be used to monitor changes caused by disease, development or treatment. Histological sectioning and other microscopic imaging techniques enable zooming in on the cellular scale.

The combination of molecular and structural imaging modalities enables life-science researchers to study disease processes and treatment effects over time, from molecule to organism. These new integrated imaging possibilities have generated a new problem: the amount and complexity of imaging data make it very difficult to interpret and quantify the complex relationships between molecular processes and the functional and structural changes they cause. For a single time point, an imaging study may consist of heterogeneous imaging data at multiple scales: photographs, photon emission images, CT, MR or PET slices, functional MR imaging, MR spectroscopy, histological slices etc. Differences in imaging geometry, animal posture and information scale occur between modalities. In addition, large variability in animal posture may exist between time points in a follow-up study. As a result, there is currently a great demand for new visualization and data fusion methods to efficiently visualize the information in this heterogeneous imaging data.

The goal of this thesis was to address three integrative visualization challenges inherent to in vivo small-animal imaging data. First, the combination of different modalities presents challenges in image registration and visualization. As functional modalities provide limited or no references to anatomical structure, registration methods will require additional information. The visualization of the combined data must be intuitive, while it still remains clear what features are produced by each modality. Second, integration of data over time requires methods that can handle the varying posture of a subject between follow-up scans. Here also, visualization methods are required that combine the data such that changes over time become apparent while retaining their structural context. Third, multi-scale challenges such as super resolution reconstruction require selection and extraction of volumes of interest

@PhdThesis { Kok14,
  author       = "Kok, Peter",
  title        = "Integrative Visualization of Whol Body Molecular Imaging Data",
  school       = "Delft University of Technology",
  month        = "December",
  year         = "2014",
  url          = "http://graphics.tudelft.nl/Publications-new/2014/Kok14"
}