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

• Published in: NeuroImage (Orlando, Fla.) Vol. 44; no. 3; pp. 839 - 848
• Main Authors: Saad, Ziad S., Glen, Daniel R., Chen, Gang, Beauchamp, Michael S., Desai, Rutvik, Cox, Robert W.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-02-2009; Elsevier Limited
• Subjects: Algorithms; Artificial Intelligence; Automation; Brain - anatomy & histology; Brain - physiology; Data analysis; Data Interpretation, Statistical; Data processing; Echo-Planar Imaging - methods; Humans; Image Enhancement - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Medical imaging; Methods; Optimization algorithms; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Software; Statistics as Topic; Subtraction Technique
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2008.09.037

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.