Diffusion MRI with generalised gradient waveforms : methods, models, and neuroimaging applications

Linköpings universitet, Avdelningen för medicinsk teknik Linköpings universitet, Tekniska fakulteten Linköping 2023

The incessant, random motion of water molecules within biological tissues reveals unique information about the tissues’ structural and functional characteristics. Diffusion magnetic resonance imaging is sensitive to this random motion, and since the mid-1990s it has been extensively employed for studying the human brain. Most notably, measurements of water diffusion allow for the early detection of ischaemic stroke and for the unveiling of the brain’s wiring via reconstruction of the neuronal connections. Ultimately, the goal is to employ this imaging technique to perform non-invasive, in vivo virtual histology to directly characterise both healthy and diseased tissue. Recent developments in the field have introduced new ways to measure the diffusion process in clinically feasible settings. These new measurements, performed by employing generalised magnetic field gradient waveforms, grant access to specific features of the cellular composition and structural organisation of the tissue. Methods based on them have already proven beneficial for the assessment of different brain diseases, sparking interest in translating such techniques into clinical practice. This thesis focuses on improving the methods currently employed for the analysis of such diffusion MRI data, with the aim of facilitating their clinical adoption. The first two publications introduce constrained frameworks for the estimation of parameters from diffusion MRI data acquired with generalised gradient waveforms. The constraints are dictated by mathematical and physical properties of a multi-compartment model used to represent the brain tissue, and can be efficiently enforced by employing a relatively new optimisation scheme called semidefinite programming. The developed routines are demonstrated to improve robustness to noise and imperfect data collection. Moreover, constraining the fit is shown to relax the requirements on the number of points needed for the estimation, thus allowing for faster data acquisition.

Diffusion; Diffusion MRI; Microstructure; Brain; White matter; Microscopic anisotropy; Constrained optimisation; Medical Laboratory and Measurements Technologies; Medicinsk laboratorie- och mätteknik; Doctoral thesis, comprehensive summary; info:eu-repo/semantics/doctoralThesis; text

10.3384.9789180754439

Open access content. Open access content
info:eu-repo/semantics/openAccess

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English

UPE oai:DiVA.org:liu-199898
0000-0001-8759-7142
urn:isbn:9789180754422
urn:isbn:9789180754439
doi:10.3384/9789180754439
1428239108

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