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Anatomically constrained reconstruction from noisy data
Author(s) -
Haldar Justin P.,
Hernando Diego,
Song ShengKwei,
Liang ZhiPei
Publication year - 2008
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21536
Subject(s) - computer science , noise (video) , focus (optics) , sampling (signal processing) , resolution (logic) , signal to noise ratio (imaging) , artificial intelligence , algorithm , signal (programming language) , scheme (mathematics) , prior information , pattern recognition (psychology) , computer vision , data mining , image (mathematics) , mathematics , telecommunications , physics , mathematical analysis , filter (signal processing) , optics , programming language
Noise is a major concern in many important imaging applications. To improve data signal‐to‐noise ratio (SNR), experiments often focus on collecting low‐frequency k ‐space data. This article proposes a new scheme to enable extended k ‐space sampling in these contexts. It is shown that the degradation in SNR associated with extended sampling can be effectively mitigated by using statistical modeling in concert with anatomical prior information. The method represents a significant departure from most existing anatomically constrained imaging methods, which rely on anatomical information to achieve super‐resolution. The method has the advantage that less accurate anatomical information is required relative to super‐resolution approaches. Theoretical and experimental results are provided to characterize the performance of the proposed scheme. Magn Reson Med 59:810–818, 2008. © 2008 Wiley‐Liss, Inc.