title: Effects of image contrast on functional MRI image registration.
abstract: Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts.
authors.affiliation: Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. javier.gonzalez-castillo@nih.gov
authors.forename: Javier
authors.initials: J
authors.lastname: Gonzalez-Castillo
authors.forename: Kristen N
authors.initials: KN
authors.lastname: Duthie
authors.forename: Ziad S
authors.initials: ZS
authors.lastname: Saad
authors.forename: Carlton
authors.initials: C
authors.lastname: Chu
authors.forename: Peter A
authors.initials: PA
authors.lastname: Bandettini
authors.forename: Wen-Ming
authors.initials: WM
authors.lastname: Luh
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000166:cytology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique