High spatial resolution compressed sensing (HSPARSE) functional MRI.
To propose a novel compressed sensing (CS) high spatial resolution functional MRI (fMRI) method and demonstrate the advantages and limitations of using CS for high spatial resolution fMRI.



A randomly undersampled variable density spiral trajectory enabling an acceleration factor of 5.3 was designed with a balanced steady state free precession sequence to achieve high spatial resolution data acquisition. A modified k-t SPARSE method was then implemented and applied with a strategy to optimize regularization parameters for consistent, high quality CS reconstruction.



The proposed method improves spatial resolution by six-fold with 12 to 47% contrast-to-noise ratio (CNR), 33 to 117% F-value improvement and maintains the same temporal resolution. It also achieves high sensitivity of 69 to 99% compared the original ground-truth, small false positive rate of less than 0.05 and low hemodynamic response function distortion across a wide range of CNRs. The proposed method is robust to physiological noise and enables detection of layer-specific activities in vivo, which cannot be resolved using the highest spatial resolution Nyquist acquisition.



The proposed method enables high spatial resolution fMRI that can resolve layer-specific brain activity and demonstrates the significant improvement that CS can bring to high spatial resolution fMRI. Magn Reson Med 76:440-455, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Department of Electrical Engineering, Stanford University, Stanford, California, USA.|Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, California, USA.
Zhongnan
Z
Fang
Department of Electrical Engineering, Stanford University, Stanford, California, USA.|Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, California, USA.
Nguyen
N
Van Le
Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA.
ManKin
M
Choy
Department of Electrical Engineering, Stanford University, Stanford, California, USA.|Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, California, USA.|Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA.|Department of Bioengineering, Stanford University, Stanford, California, USA.|Department of Neurosurgery, Stanford University, Stanford, California, USA.|Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA.|Neuroscience, and Biomedical Engineering Interdepartmental Program, University of California, Los Angeles, California, USA.
Jin Hyung
JH
Lee
D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D002529:Cerebellar Nuclei / Q000502:physiology*; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
compressed sensing; fMRI; high spatial resolution; optogenetic fMRI