A New Method for Improving Functional-to-Structural MRI Alignment using Local Pearson Correlation

By Ziad S. Saad, Daniel R. Glen, Gang Chen, Michael S. Beauchamp, Rutvik H. Desai, Robert W. Cox. NeuroImage, 2008

417 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/964fb19f036f48ed314d28ab0296e4e2b1ba9501

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Citations in the corpus

Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, methodology): “By applying a recent edge-based alignment technique called local Pearson correlation (Saad et al., 2009) to T1-weighted anatomical images and the gray matter compartment segmented from T2* images, an accurate coregistration of structural and functional images”
AFNI: What a long strange trip it’s been (2011, methodology): “For example, our EPI-structural registration algorithm (Saad et al., 2009) was developed when a few discerning”
Accurate and Robust Brain Image Alignment using Boundary-based Registration (2009, methodology): “Finally, like Saad et al. (2009), we strongly encourage researchers to visually inspect the quality of their registrations.”
The effect of scan length on the reliability of resting-state fMRI connectivity estimates (2013, methodology): “Functional data were corrected for motion (using AFNI's rigid-body volume registration) and physiological noise from cardiac and respiratory pulsations (RETROICOR) before being aligned to their respective anatomical image (Cox, 1996; Glover et al., 2000; Saad et al., 2009).”
Mapping Sources of Correlation in Resting State FMRI, with Artifact Detection and Removal (2010, positive): “This cross-modal registration cost function is optimized for EPI and T1 images, and has been shown to be superior for this purpose to more general multi-modal cost functions (Saad et al., 2009).”
Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI (2011, methodology): “The functional to structural co-registration parameters were estimated by registering the skull-stripped middle TE image from thefirst time point to the skull-stripped anatomical image using an affine (12 parameters) alignment procedure with the local Pearson correlation cost-functional (3dSkullStip”
The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated (2013, methodology): “EPI time series were aligned to their T1-weighted anatomical (Saad et al., 2009).”
Phasic and sustained fear in humans elicits distinct patterns of brain activity (2010, positive): “The advantage of this LPC-based alignment process for imaging ventromedial basal forebrain structures is that it computes the transformations necessary for aligning anatomical and EPI datasets with minimal interpolation, and compared to generic cost functionals, provides superior spatial alignment b”