Data-gedreven lokale en globale reconstructie van wittestofbanen in diffusie-gewogen beelden

Christiaens D., ''Data-driven local and global reconstruction of white matter fibres in diffusion-weighted imaging'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, July 2016, Leuven, Belgium.Mapping the structural network topology of the human brain is a fundamental challenge in science, and one that may advance our understanding and treatment of neurological and neuropsychiatric disorders. Diffusion-weighted magnetic resonance imaging (DWI) is currently the only non-invasive technique for probing the structural connectivity in the brain in vivo. Its principle is based on indirect measurement of the diffusion anisotropy of water, which is correlated with tissue microstructure. Estimating the local geometry of axonal fibres then hinges on biophysical models of diffusion in white matter. Such local estimates can subsequently be integrated along the image to reconstruct global structural connections in the brain, a process known as tractography. Many state-of-the-art DWI analysis methods build on strong model assumptions about the signal in white matter, which are hard to validate and may not generalize to other tissues and pathology. In this thesis, we therefore aim to reconstruct the local and global fibre configuration in brain white matter with as few prior assumptions about the microstructure as possible. Instead, we develop data-driven methodology, informed by spatial priors, population priors, and the signal itself. From this perspective, we develop a global tractography framework that integrates a spatial prior on the continuity of white matter fibres into a minimal convolutive multi-tissue model for DWI in the brain, based on a fibre response function that is estimated from and thus adapted to the data at hand. In this method, local fibre orientation estimates inform the global track configuration and vice versa, hence integrating local and global scales into one Markov chain Monte Carlo optimization framework. Results show improved specificity of valid connections and maintain a quantitative correspondence between track density and the apparent fibre density in the data. Secondly, we introduce population priors in the form of atlases of the local fibre orientation or of the global white matter bundle label to which individual fibres belong. As such, tractography in individual subjects is informed by common structure found across a cohort. Results indicate that such priors can reduce false positive tracks, thus improving specificity. Finally, we develop a blind source separation technique for multi-shell DWI, decomposing the data as a convolutive mixture of nonnegative tissue orientation distribution functions and corresponding response functions, without assuming the latter as known thus fully unsupervised. In healthy human brain data, the resulting components are associated with white matter fibres, grey matter, and cerebrospinal fluid. This factorization is on par with state-of-the-art supervised methods, as demonstrated also in Monte-Carlo simulations evaluating accuracy and precision. In animal data and in the presence of edema, our method is able to recover unseen tissue structure, fully data-driven. In summary, we developed local and global fibre reconstruction methods for DWI that improve over the state-of-the-art and extend to applications in preclinical data and pathology.Abstract v Contents xiii List of Figures xix List of Tables xxiii 1 Introduction 1 1.1 The brain at micro- and macroscopic scale . . . . . . . . . . . . 1 1.1.1 Brain histology . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Brain anatomy . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Neuroimaging in research and clinical practice . . . . . . . . . 5 1.3 Aims and objectives . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Main contributions . . . . . . . . . . . . . . . . . . . . . . . 7 1.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Diffusion-weighted imaging: concepts, challenges, and limitations 9 2.1 Brownian motion and diffusion . . . . . . . . . . . . . . . . . . 9 2.2 Diffusion-weighted imaging . . . . . . . . . . . . . . . . . . 11 2.2.1 The Stejskal-Tanner sequence . . . . . . . . . . . . . . . 12 2.2.2 Q-space imaging . . . . . . . . . . . . . . . . . . . . . . 14 2.2.3 Gradient strength and b-value . . . . . . . . . . . . . . . 14 2.3 Signal modelling . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.1 Diffusion tensor imaging . . . . . . . . . . . . . . . . . 16 2.3.2 Diffusion kurtosis imaging . . . . . . . . . . . . . . . . 18 2.3.3 High angular resolution diffusion imaging . . . . . . . . . 19 2.4 Microstructure and tissue modelling . . . . . . . . . . . . . . 22 2.4.1 Compartment models . . . . . . . . . . . . . . . . . . . . 23 2.4.2 Spherical deconvolution . . . . . . . . . . . . . . . . . . 24 2.4.3 Multi-tissue spherical deconvolution . . . . . . . . . . . 28 2.5 Tractography . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.1 Streamline tractography . . . . . . . . . . . . . . . . . . 29 2.5.2 Probabilistic tractography . . . . . . . . . . . . . . . . 29 2.5.3 Global tractography . . . . . . . . . . . . . . . . . . . . 30 2.5.4 Track filtering and anatomical priors . . . . . . . . . . . 30 2.5.5 Bundle segmentation and clustering . . . . . . . . . . . . 31 2.6 Validation of microstructure and tractography . . . . . . . . . 32 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Multi-shell multi-tissue global tractography 35 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Global tractography in the spherical harmonics basis . . . 37 3.2.2 Response function estimation . . . . . . . . . . . . . . . 40 3.3 Data and Experiments . . . . . . . . . . . . . . . . . . . . . 41 3.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.3.2 Parameter settings . . . . . . . . . . . . . . . . . . . . 42 3.3.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.4.1 Simulated data . . . . . . . . . . . . . . . . . . . . . . 44 3.4.2 In vivo data . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.5.1 Multi-shell, multi-tissue global tractography . . . . . . . 49 3.5.2 Response function estimation . . . . . . . . . . . . . . . 53 3.5.3 Tractometer results . . . . . . . . . . . . . . . . . . . . 54 3.5.4 In vivo results . . . . . . . . . . . . . . . . . . . . . . 55 3.5.5 Futureper spectives . . . . . . . . . . . . . . . . . . . . 56 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 Population priors in global tractography 59 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2 Imposing a prior on the local track orientation . . . . . . . . 61 4.2.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . 62 4.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Imposing global label priors . . . . . . . . . . . . . . . . . 64 4.3.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.4 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . 73 5 Population-based atlas construction 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.2 Groupwise diffeomorphic track set registration . . . . . . . . 78 5.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2.2 Experiments and results . . . . . . . . . . . . . . . . . . 81 5.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3 Fibre bundle segmentation . . . . . . . . . . . . . . . . . . . 87 5.3.1 Materials and methods . . . . . . . . . . . . . . . . . . . 88 5.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . 93 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 94 6 Nonnegative factorization of diffusion-weighted images 97 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.2.1 Multi-tissue spherical convolution. . . . . . . . . . . . . 99 6.2.2 Convexity- and nonnegativity-constrained spherical factorization . . . . . . . . . . . . . . . . . . . . . . . 100 6.2.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . 101 6.2.4 Implementation . . . . . . . . . . . . . . . . . . . . . . 104 6.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6.3.1 Phantom simulation . . . . . . . . . . . . . . . . . . . . 104 6.3.2 Accuracy and precision . . . . . . . . . . . . . . . . . . 105 6.4 Data and results . . . . . . . . . . . . . . . . . . . . . . 107 6.4.1 Data and preprocessing . . . . . . . . . . . . . . . . . . 107 6.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.5.1 Unsupervised DWI factorization . . . . . . . . . . . . . . 112 6.5.2 Model selection . . . . . . . . . . . . . . . . . . . . . . 116 6.5.3 Future perspectives . . . . . . . . . . . . . . . . . . . . 117 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7 Conclusions and future work 125 7.1 Summary discussion . . . . . . . . . . . . . . . . . . . . . . 125 7.1.1 Global tractography . . . . . . . . . . . . . . . . . . . . 126 7.1.2 Population priors and atlases . . . . . . . . . . . . . . . 126 7.1.3 Nonnegative DWI factorization . . . . . . . . . . . . . . . 127 7.2 Future perspectives . . . . . . . . . . . . . . . . . . . . . . 128 Bibliography 131 Curriculum 163 List of publications 165nrpages: 169status: publishe