PhD defence by Henrik Grønholt Jensen
Density-based Similarity in the Registration of Diffusion-Weighted Images
Diffusion-Weighted Magnetic Resonance Imaging (DWI) is a non-invasive scanning protocol aimed at inferring the structure of biological tissue by tracking the movement of water molecules. As molecules diffuse along and around obstacles, in-vivo images of the diffusion can be used to reconstruct the minuscule anatomy that would otherwise be invisible. The applications of DWI range from tumor detection to tracing the neuronal pathways connecting the brain.
DWI is also a complex modality and difficult to both validate and compare. The data is directional and exhibits a non-linear behaviour for high-resolution images. It requires longer scanning times and high magnetic gradients, resulting in an increased amount of noise from motion and external factors. DWI also has no gold standard datasets for comparable quantitative validation. This is a problem as DWI is becoming an issue of Big Data due to increasing amounts of openly available datasets. As such, our first contribution is a critical review of image registration and validation of group-wise alignment of DWI.
Image registration is the process of spatially aligning images in a way that allow us to define a shared coordinate system between them. For DWI, the reorientation of the directional information is challenging. Our second contribution is a density-based formulation for DWI that gives access to information-theoretic similarity measures. The presented framework is a global registration method that optimizes the mutual information between DWI with explicit reorientation of the gradient vectors. We show that the directional scale is important for aligning DWI.
Nonrigid image registration allows for local warping of images. While few studies compare nonrigid methods used for DWI, it is clear that the gold standards of nonrigid algorithms are those to include the angular information as a direct part of the registration. Our third is a nonrigid extension of the density-based DWI registration framework. We present the full analytical solution and demonstrate it on simulated and synthetically warped DWI, finding empirical evidence that the underlying DWI structure is preserved during registration.
- Chairman: Associate Professor Marleen de Bruijne, Department of Computer Science, University of Copenhagen
- Associate Professor Tim Dyrby, DTU Compute, Technical University of Denmark
- Associate Professor Stefan Klein, Erasmus Medical Center Rotterdam
- Principal supervisor: Professor Mads Nielsen, Department of Computer Science, University of Copenhagen
- Co-Supervisor: Associate Professor Sune Darkner, Department of Computer Science, University of Copenhagen
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