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Quality assessment of MEG-to-MRI coregistrations

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Document pages: 26 pages

Abstract: For high precision in source reconstruction of magnetoencephalography (MEG)or electroencephalography data, high accuracy of the coregistration of sourcesand sensors is mandatory. Usually, the source space is derived from magneticresonance imaging (MRI). In most cases, however, no quality assessment isreported for sensor-to-MRI coregistrations. If any, typically root mean squares(RMS) of point residuals are provided. It has been shown, however, that RMS ofresiduals do not correlate with coregistration errors. We suggest using targetregistration error (TRE) as criterion for the quality of sensor-to-MRIcoregistrations. TRE measures the effect of uncertainty in coregistrations atall points of interest. In total, 5544 data sets with sensor-to-head and 128head-to-MRI coregistrations, from a single MEG laboratory, were analyzed. Anadaptive Metropolis algorithm was used to estimate the optimal coregistrationand to sample the coregistration parameters (rotation and translation). Wefound an average TRE between 1.3 and 2.3mm at the head surface. Further, weobserved a mean absolute difference in coregistration parameters between theMetropolis and iterative closest point algorithm of (1.9 $ pm$ 1.5)° and(1.1 $ pm$ 0.9)mm. A paired sample t-test indicated a significant improvementin goal function minimization by using the Metropolis algorithm. The sampledparameters allowed computation of TRE on the entire grid of the MRI volume.Hence, we recommend the Metropolis algorithm for head-to-MRI coregistrations.

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