| Summary: | 碩士 === 國立交通大學 === 生醫工程研究所 === 103 === Diffusion Spectrum Imaging (DSI) is one of the diffusion MRI techniques and has the highest accuracy of resolving complex fiber orientations in human brain. However, due to the large data sampling and resulting long scan time, its clinical feasibility has not been verified yet on clinical MRI applications. To reduce the data sampling and accelerate the scan time, a signal processing approach is highly needed without any additional cost of hardware improvement.
Compressive Sensing (CS) technique can moderate huge data information well based on the theory that extracts all the high coefficients from signal bases. This technique has been widely employed in a variety of research fields, such as data mining, wireless network communication, video and image processing. Although implementation of CS technique on DSI has been proposed in previous studies, a systematic and quantitative analysis framework is still lacking.
Therefore, this thesis aimed to establish a dictionary-based CS-DSI technique and quantitative evaluation framework. We developed a multiple-slice dictionary learning method and focused on investigating the improvement on white matter structures. We also discussed the influences of DSI sequence parameters on its performance, such as maximum b-value and signal-to-noise ratio. The framework of multiple-slice learning is verified to has higher accuracy of reconstructing probability distribution function and orientation distribution function. We expect this thesis could provide more useful information for facilitating the development of CS-DSI technology as well as utilizing this technique on neuroscience researches and clinical applications.
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