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A new method for improving functional-to-structural MRI alignment using local Pearson correlation
Open accessAbstract
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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“compared to generic cost functionals, provides superior spatial alignment between T2*- and T1-weighted images in brain structures near the ventricles (Saad et al., 2009).”
From Phasic and sustained fear in humans elicits distinct patterns of brain activity (2011) · cites this paper as Saad et al., 2009
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Metadata
- 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.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 44
- Issue
- 3
- Pages
- 839-848
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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