Compressed sensing dynamic cardiac MRI using union of randomly shifted lattice sampling patterns = 격자구조 샘플링 기반 압축센싱을 이용한 동적 자기공명영상 기법

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Dynamic MRI is a technique which obtains time series of images at a high frame rate. In dynamic MRI, it is very important to reduce the data acquisition time because it is often not fast enough to meet the Nyquist sampling rate. Downsampling k-t space measurements accelerates the acquisition speed but may incur aliasing artifacts. To resolve this problem, lattice sampling patterns have been widely used. Lattice sampling pattern in k-t space leads to periodic replications of original image in x-f space. There are some algorithms which use this property and exploit spatio-temporal correlations to solve aliasing problem. Recently, they have drawn considerable attention because they successfully reconstructed high spatio-temporal resolution. Meanwhile, after the advent of the ”compressed sensing”, high fidelity of reconstruction is possible from much less number of measurements than Nyquist sampling requirements. However, lattice sampling pattern is not suitable in compressed sensing perspective. In this paper, new algorithm is proposed which combines compressed sensing, lattice sampling pattern, and parallel imaging. The experimental results show high spatio-temporal resolution without aliasing artifacts in reconstructed cardiac cine image. Furthermore, the proposed algorithm outperforms existing method that uses lattice sampling pattern.
Advisors
Ye, Jong-Chulresearcher예종철researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2011
Identifier
467646/325007  / 020093341
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2011.2, [ vi, 38 p. ]

Keywords

Dynamic cardiac imaging; compressed sensing; union of randomly shifted lattices; parallel imaging; 동적 심장 영상; 압축센싱; 격자구조; 병렬 자기공명 영상법; 희소 베이지안 학습; sparse Bayesian learning

URI
http://hdl.handle.net/10203/179769
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=467646&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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