TY - JOUR
T1 - Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images
AU - Chiarelli, Antonio M.
AU - Maclin, Edward L.
AU - Low, Kathy A.
AU - Fabiani, Monica
AU - Gratton, Gabriele
N1 - Publisher Copyright:
© 2015 Society of Photo-Optical Instrumentation Engineers.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - co-registration procedures
KW - diffuse optical tomography
KW - fiducial points
KW - scalp sensors
KW - surface fitting
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U2 - 10.1117/1.JBO.20.1.016009
DO - 10.1117/1.JBO.20.1.016009
M3 - Article
C2 - 25574993
AN - SCOPUS:84922569484
SN - 1083-3668
VL - 20
JO - Journal of biomedical optics
JF - Journal of biomedical optics
IS - 1
M1 - 016009
ER -