Validation of a method for coregistering scalp recording locations with 3D structural MR images

• Published in: Human brain mapping Vol. 29; no. 11; pp. 1288 - 1301
• Main Authors: Whalen, Christopher, Maclin, Edward L., Fabiani, Monica, Gratton, Gabriele
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.11.2008; Wiley-Liss
• Subjects: Biological and medical sciences; Brain - physiology; Brain Mapping - methods; coregistration; Electrodiagnosis. Electric activity recording; fiducial points; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Medical sciences; Nervous system; Radiodiagnosis. Nmr imagery. Nmr spectrometry; surface fitting; Validation; Nervous system diseases; Radiodiagnosis; Scalp; Tridimensional image; fiducial points; Nuclear magnetic resonance imaging; coregistration; surface fitting
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.20465

A common problem in brain imaging is how to most appropriately coregister anatomical and functional data sets into a common space. For surface‐based recordings such as the event related optical signal (EROS), near‐infrared spectroscopy (NIRS), event‐related potentials (ERPs), and magnetoencephalography (MEG), alignment is typically done using either (1) a landmark‐based method involving placement of surface markers that can be detected in both modalities; or (2) surface‐fitting alignment that samples many points on the surface of the head in the functional space and aligns those points to the surface of the anatomical image. Here we compare these two approaches and advocate a combination of the two in order to optimize coregistration of EROS and NIRS data with structural magnetic resonance images (sMRI). Digitized 3D sensor locations obtained with a Polhemus® digitizer can be effectively coregistered with sMRI using fiducial alignment as an initial guess followed by a Marquardt–Levenberg least‐squares rigid‐body transform (df = 6) to match the surfaces. Additional scaling parameters (df = 3) and point‐by‐point surface constraints can also be employed to further improve fitting. These alignment procedures place the lower‐bound residual error at 1.3 ± 0.1 mm (μ ± s) and the upper‐bound target registration error at 4.4 ± 0.6 mm (μ ± s). The dependence of such errors on scalp segmentation, number of registration points, and initial guess is also investigated. By optimizing alignment techniques, anatomical localization of surface recordings can be improved in individual subjects. Hum Brain Mapp, 2008. © 2007 Wiley‐Liss, Inc.