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NeuroImage v.162, 2017년, pp.384 - 397   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI

Zhao, Moss Y. (Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom ) ; Mezue, Melvin (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom ) ; Segerdahl, Andrew R. (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom ) ; Okell, Thomas W. (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom ) ; Tracey, Irene (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom ) ; Xiao, Yingyi (St Hilda's College, University of Oxford, Oxford, United Kingdom ) ; Chappell, Michael A. (Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom ) ;
  • 초록  

    Abstract Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR = 5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc). Highlights PVEc is effective for ASL even when there are errors in the PV estimates. The spatially regularized method was superior at preserving spatial details. The linear regression method was less sensitive to errors in PV estimates. PVEc improves repeatability of CBF estimation in ASL MRI.


  • 주제어

    Arterial spin labeling .   Cerebral blood flow .   Partial volume effects .   Systematic analysis .   Perfusion MRI .   Repeatability .   Reproducibility.  

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