Anatomically constrained reconstruction from noisy data.
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.
Haldar
Justin P
JP
Department of Electrical and Computer Engineering, University of Illinois, Urbana, Illinois 61801, USA. haldar@uiuc.edu
Hernando
Diego
D
Song
Sheng-Kwei
SK
Liang
Zhi-Pei
ZP
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity