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

Antonio M. Chiarelli, Edward L. Maclin, Kathy A. Low, Monica Fabiani, Gabriele Gratton

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number016009
JournalJournal of biomedical optics
Volume20
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint

Magnetic resonance
magnetic resonance
recording
sensors
Sensors
alignment
Imaging techniques
anatomy
imaging techniques
brain
Brain
optimization

Keywords

  • co-registration procedures
  • diffuse optical tomography
  • fiducial points
  • scalp sensors
  • surface fitting

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering

Cite this

Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images. / Chiarelli, Antonio M.; Maclin, Edward L.; Low, Kathy A.; Fabiani, Monica; Gratton, Gabriele.

In: Journal of biomedical optics, Vol. 20, No. 1, 016009, 01.01.2015.

Research output: Contribution to journalArticle

@article{98090e4e7b184cd7a53eede308588bfa,
title = "Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images",
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.",
keywords = "co-registration procedures, diffuse optical tomography, fiducial points, scalp sensors, surface fitting",
author = "Chiarelli, {Antonio M.} and Maclin, {Edward L.} and Low, {Kathy A.} and Monica Fabiani and Gabriele Gratton",
year = "2015",
month = "1",
day = "1",
doi = "10.1117/1.JBO.20.1.016009",
language = "English (US)",
volume = "20",
journal = "Journal of Biomedical Optics",
issn = "1083-3668",
publisher = "SPIE",
number = "1",

}

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

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

UR - http://www.scopus.com/inward/record.url?scp=84922569484&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922569484&partnerID=8YFLogxK

U2 - 10.1117/1.JBO.20.1.016009

DO - 10.1117/1.JBO.20.1.016009

M3 - Article

C2 - 25574993

AN - SCOPUS:84922569484

VL - 20

JO - Journal of Biomedical Optics

JF - Journal of Biomedical Optics

SN - 1083-3668

IS - 1

M1 - 016009

ER -