期刊
PHYSICS IN MEDICINE AND BIOLOGY
卷 52, 期 11, 页码 3201-3226出版社
IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/52/11/018
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资金
- National Research Foundation of Korea [2005-00152] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The dynamic MR imaging of time-varying objects, such as beating hearts or brain hemodynamics, requires a significant reduction of the data acquisition time without sacrificing spatial resolution. The classical approaches for this goal include parallel imaging, temporal filtering and their combinations. Recently, model-based reconstruction methods called k - t BLAST and k - t SENSE have been proposed which largely overcome the drawbacks of the conventional dynamic imaging methods without a priori knowledge of the spectral support. Another recent approach called k - t SPARSE also does not require exact knowledge of the spectral support. However, unlike k - t BLAST/SENSE, k - t SPARSE employs the so-called compressed sensing (CS) theory rather than using training. The main contribution of this paper is a new theory and algorithm that unifies the abovementioned approaches while overcoming their drawbacks. Specifically, we show that the celebrated k - t BLAST/SENSE are the special cases of our algorithm, which is asymptotically optimal from the CS theory perspective. Experimental results show that the new algorithm can successfully reconstruct a high resolution cardiac sequence and functional MRI data even from severely limited k - t samples, without incurring aliasing artifacts often observed in conventional methods.
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