Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images

Abstract

Abstract. Functional brain imaging techniques require accurate co-registration to anatomical images to precisely identify the areas being activated. Many of them, including diffuse optical imaging, rely on scalp-placed recording sensors. Fiducial alignment is an effective and rapid method for co-registering scalp sensors onto anatomy, but is quite sensitive to placement errors. Surface Euclidean distance minimization using the Levenberq-Marquart algorithm (LMA) has been shown to be very accurate when based on good initial guesses, such as precise fiducial alignment, but its accuracy drops substantially with fiducial placement errors. Here we compared fiducial and LMA co-registration methods to a new procedure, the iterative closest point-to-plane (ICP2P) method, using simulated and real data. An advantage of ICP2P is that it eliminates the need to identify fiducials and is, therefore, entirely automatic. We show that, typically, ICP2P is as accurate as fiducial-based LMA, but is less sensitive to initial placement errors. However, ICP2P is more sensitive to spatially correlated noise in the description of the head surface. Hence, the best technique for co-registration depends on the type of data available to describe the scalp and the surface defined by the recording sensors. Under optimal conditions, co-registration error using surface-fitting procedures can be reduced to ∼3 mm.

Low-resolution mapping of the effective attenuation coefficient of the human head: a multidistance approach applied to high-density optical recordings

“…Optode locations and structural MRI data were coregistered using fiducials and a surface-fitting Levenberg and Marquardt algorithm. 41,42 See Fig. 10(b) for source and detector locations in a representative subject, rendered onto their T1w image.…”

Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data

“…Each image was then placed into a left-right (L-R), posterior-anterior (P-A), inferior-superior (I-S) spatial orientation and resampled into 1-mm isovoxels by cubic spline interpolation of the original image. 45 Segmentation of the subject's head into skull and scalp, CSF, white matter, and gray matter was performed using SPM functions applied to T1 structural images. 37 A fine mesh (maximum tetrahedral volume ¼ 2 mm…”

“…Optode locations and structural MRI data were coregistered using fiducials and a surface-fitting Levenberq-Marquard algorithm. 45 Only data recorded with the 830-nm wavelength were used for the computation of DOT. Preprocessing involved movement correction, 52 removal of noisy channels (i.e., channels that presented a variation in intensity greater than 40%), and signal resampling (0.5 Hz).…”

Individual differences in regional cortical volumes across the life span are associated with regional optical measures of arterial elasticity

“…These fiducial points, optode locations, and other scalp locations were digitized with a Polhemus FastTrak 3D digitizer (Colchester, VT; accuracy: 0.8 mm) using a recording stylus and three head-mounted receivers, which allowed for small movements of the head in between measurements. Optode locations and structural MRI data were then co-registered using the fiducials and a surface-fitting Levenberq and Marquard algorithm (Chiarelli et al, 2015b; see also Whalen et al, 2008). …”