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无气体校准 fMRI 血管尺寸敏感性校正。

Gas-free calibrated fMRI with a correction for vessel-size sensitivity.

机构信息

Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.

Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada.

出版信息

Neuroimage. 2018 Apr 1;169:176-188. doi: 10.1016/j.neuroimage.2017.12.047. Epub 2017 Dec 15.

Abstract

Calibrated functional magnetic resonance imaging (fMRI) is a method to independently measure the metabolic and hemodynamic contributions to the blood oxygenation level dependent (BOLD) signal. This technique typically requires the use of a respiratory challenge, such as hypercapnia or hyperoxia, to estimate the calibration constant, M. There has been a recent push to eliminate the gas challenge from the calibration procedure using asymmetric spin echo (ASE) based techniques. This study uses simulations to better understand spin echo (SE) and ASE signals, analytical modelling to characterize the signal evolution, and in vivo imaging to validate the modelling. Using simulations, it is shown how ASE imaging generally underestimates M and how this depends on several parameters of the acquisition, including echo time and ASE offset, as well as the vessel size. This underestimation is the result of imperfect SE refocusing due to diffusion of water through the extravascular environment surrounding the microvasculature. By empirically characterizing this SE attenuation as an exponential decay that increases with echo time, we have proposed a quadratic ASE biophysical signal model. This model allows for the characterization and compensation of the SE attenuation if SE and ASE signals are acquired at multiple echo times. This was tested in healthy subjects and was found to significantly increase the estimates of M across grey matter. These findings show promise for improved gas-free calibration and can be extended to other relaxation-based imaging studies of brain physiology.

摘要

校准后的功能磁共振成像 (fMRI) 是一种独立测量代谢和血液动力学对血氧水平依赖 (BOLD) 信号贡献的方法。该技术通常需要使用呼吸挑战,如高碳酸血症或高氧血症,来估计校准常数 M。最近,人们一直在努力从校准过程中消除气体挑战,使用基于非对称自旋回波 (ASE) 的技术。本研究使用模拟来更好地理解自旋回波 (SE) 和 ASE 信号,使用分析模型来描述信号演化,并通过体内成像进行模型验证。通过模拟,展示了 ASE 成像通常如何低估 M,以及这取决于采集的几个参数,包括回波时间和 ASE 偏移,以及血管大小。这种低估是由于水通过围绕微血管的血管外环境扩散导致 SE 重聚焦不完美的结果。通过经验性地将这种 SE 衰减特征化为随回波时间增加的指数衰减,我们提出了一种二次 ASE 生物物理信号模型。如果在多个回波时间采集 SE 和 ASE 信号,则该模型允许对 SE 衰减进行特征描述和补偿。在健康受试者中进行了测试,发现它显著增加了灰质中 M 的估计值。这些发现为无气校准提供了改进的前景,并可扩展到其他基于弛豫的脑生理成像研究。

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