Cited by 407Medical Image Segmentation TechniquesAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

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A new method for improving functional-to-structural MRI alignment using local Pearson correlation

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Saad, Ziad S., Glen, Daniel R., Chen, Gang, Beauchamp, Michael S., Desai, Rutvik, Cox, Robert W.·2009·NeuroImage

DOI 10.1016/j.neuroimage.2008.09.037PMC PMC2649831

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Abstract

Accurate registration of Functional Magnetic Resonance Imaging (FMRI) T2-weighted volumes to same-subject high-resolution T1-weighted structural volumes is important for Blood Oxygenation Level Dependent (BOLD) FMRI and crucial for applications such as cortical surface-based analyses and pre-surgical planning. Such registration is generally implemented by minimizing a cost functional, which measures the mismatch between two image volumes over the group of proper affine transformations. Widely used cost functionals, such as mutual information (MI) and correlation ratio (CR), appear to yield decent alignments when visually judged by matching outer brain contours. However, close inspection reveals that internal brain structures are often significantly misaligned. Poor registration is most evident in the ventricles and sulcal folds, where CSF is concentrated. This observation motivated our development of an improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2- and T1-weighted images. In the absence of an alignment gold standard, we used three human observers blinded to registration method to provide an independent assessment of the quality of the registration for each cost functional. We found that LPC performed significantly better (p<0.001) than generic cost functionals including MI and CR. Generic cost functionals were very often not minimal near the best alignment, thereby suggesting that optimization is not the cause of their failure. Lastly, we emphasize the importance of precise visual inspection of alignment quality and present an automated method for generating composite images that help capture errors of misalignment.

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Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
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Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
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“The use of crosscorrelation is common as a measure of matching in image processing and in MRI in particular (Collins et al., 1994); however, the LPC approach differs by its use of the weighting function and of localized estimates that are later combined nonlinearly.”
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An overlap invariant entropy measure of 3D medical image alignment
Studholme, C., Hill, D., & Hawkes, D. J. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00091-0 · Cited by 2,167
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Authors: Saad, Z. S., Glen, D. R., Chen, G., Beauchamp, M. S., Desai, R., & Cox, R. W.
Affiliations: National Institutes of Health (US)
The University of Texas Health Science Center at Houston (US)
Medical College of Wisconsin (US)
Corresponding author: Cox, Robert W.
ORCID: Glen, Daniel R.
Chen, Gang
Beauchamp, Michael S.
Desai, Rutvik
Cox, Robert W.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2009-02
Volume: 44
Issue: 3
Pages: 839-848
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2008.09.037
PMC: PMC2649831
OpenAlex: W2018731294
Cited by: 405
References: 37
Citations by year: 2026: 12 · 2025: 11 · 2024: 21 · 2023: 18 · 2022: 21 · 2021: 30 · 2020: 30 · 2019: 16 · 2018: 23 · 2017: 27 · 2016: 21 · 2015: 37 · 2014: 33 · 2013: 36 · 2012: 26
FWCI: 20.56
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Scholar Commons (University of South Carolina) (submittedVersion)
Funders: National Science Foundation
National Institutes of Health
National Institute of Mental Health (Z01MH002888)
National Institute on Deafness and Other Communication Disorders (R01DC010783, R03DC008416)
National Institute of Neurological Disorders and Stroke (R01NS033576, 2R01NS33576-11)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: cost functional, mutual information, correlation ratio, BOLD fMRI, surgical planning, composite images, CsF
MeSH terms: Algorithms, Artificial Intelligence, Brain, Data Interpretation, Statistical, Humans, Image Enhancement, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Statistics as Topic, Subtraction Technique, Reproducibility of Results, Echo-Planar Imaging, Imaging, Three-Dimensional

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A new method for improving functional-to-structural MRI alignment using local Pearson correlation

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