Imaging diffusional variance by MRI [public]: The role of tensor-valued diffusion encoding and tissue heterogeneity

Research output: ThesisDoctoral Thesis (compilation)

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Imaging diffusional variance by MRI [public] : The role of tensor-valued diffusion encoding and tissue heterogeneity. / Szczepankiewicz, Filip.

Lund : Lund University, Faculty of Science, Department of Medical Radiation Physics, 2016. 87 p.

Research output: ThesisDoctoral Thesis (compilation)

Harvard

APA

Szczepankiewicz, F. (2016). Imaging diffusional variance by MRI [public]: The role of tensor-valued diffusion encoding and tissue heterogeneity. Lund: Lund University, Faculty of Science, Department of Medical Radiation Physics.

CBE

Szczepankiewicz F. 2016. Imaging diffusional variance by MRI [public]: The role of tensor-valued diffusion encoding and tissue heterogeneity. Lund: Lund University, Faculty of Science, Department of Medical Radiation Physics. 87 p.

MLA

Vancouver

Szczepankiewicz F. Imaging diffusional variance by MRI [public]: The role of tensor-valued diffusion encoding and tissue heterogeneity. Lund: Lund University, Faculty of Science, Department of Medical Radiation Physics, 2016. 87 p.

Author

Szczepankiewicz, Filip. / Imaging diffusional variance by MRI [public] : The role of tensor-valued diffusion encoding and tissue heterogeneity. Lund : Lund University, Faculty of Science, Department of Medical Radiation Physics, 2016. 87 p.

RIS

TY - THES

T1 - Imaging diffusional variance by MRI [public]

T2 - The role of tensor-valued diffusion encoding and tissue heterogeneity

AU - Szczepankiewicz, Filip

N1 - Defence details Date: 2016-12-02 Time: 09:00 Place: Lecture hall 3 at Skåne University Hospital, Lund External reviewer(s) Name: Jones, Derek K. Title: Professor Affiliation: Cardiff University, United Kingdom ---

PY - 2016/11

Y1 - 2016/11

N2 - Diffusion MRI provides a non-invasive probe of tissue microstructure. We recently proposed a novel method for diffusion-weighted imaging, so-called q-space trajectory encoding, that facilitates tensor-valued diffusion encoding. This method grants access to b-tensors with multiple shapes and enables us to probe previously unexplored aspects of the tissue microstructure. Specifically, we can disentangle diffusional heterogeneity that originates from isotropic and anisotropic tissue structures; we call this diffusional variance decomposition (DIVIDE).In Paper I, we investigated the statistical uncertainty of the total diffusional variance in the healthy brain. We found that the statistical power was heterogeneous between brain regions which needs to be taken into account when interpreting results.In Paper II, we showed how spherical tensor encoding can be used to separate the total diffusional variance into its isotropic and anisotropic components. We also performed initial validation of the parameters in phantoms, and demonstrated that the imaging sequence could be implemented on a high-performance clinical MRI system. In Paper III and V, we explored DIVIDE parameters in healthy brain tissue and tumor tissue. In healthy tissue, we found that diffusion anisotropy can be probed on the microscopic scale, and that metrics of anisotropy on the voxel scale are confounded by the orientation coherence of the microscopic structures. In meningioma and glioma tumors, we found a strong association between anisotropic variance and cell eccentricity, and between isotropic variance and variable cell density. In Paper IV, we developed a method to optimize waveforms for tensor-valued diffusion encoding, and in Paper VI we demonstrated that whole-brain DIVIDE is technically feasible at most MRI systems in clinically feasible scan times.

AB - Diffusion MRI provides a non-invasive probe of tissue microstructure. We recently proposed a novel method for diffusion-weighted imaging, so-called q-space trajectory encoding, that facilitates tensor-valued diffusion encoding. This method grants access to b-tensors with multiple shapes and enables us to probe previously unexplored aspects of the tissue microstructure. Specifically, we can disentangle diffusional heterogeneity that originates from isotropic and anisotropic tissue structures; we call this diffusional variance decomposition (DIVIDE).In Paper I, we investigated the statistical uncertainty of the total diffusional variance in the healthy brain. We found that the statistical power was heterogeneous between brain regions which needs to be taken into account when interpreting results.In Paper II, we showed how spherical tensor encoding can be used to separate the total diffusional variance into its isotropic and anisotropic components. We also performed initial validation of the parameters in phantoms, and demonstrated that the imaging sequence could be implemented on a high-performance clinical MRI system. In Paper III and V, we explored DIVIDE parameters in healthy brain tissue and tumor tissue. In healthy tissue, we found that diffusion anisotropy can be probed on the microscopic scale, and that metrics of anisotropy on the voxel scale are confounded by the orientation coherence of the microscopic structures. In meningioma and glioma tumors, we found a strong association between anisotropic variance and cell eccentricity, and between isotropic variance and variable cell density. In Paper IV, we developed a method to optimize waveforms for tensor-valued diffusion encoding, and in Paper VI we demonstrated that whole-brain DIVIDE is technically feasible at most MRI systems in clinically feasible scan times.

M3 - Doctoral Thesis (compilation)

SN - 978-91-7753-034-3

PB - Lund University, Faculty of Science, Department of Medical Radiation Physics

CY - Lund

ER -