Accurate and Robust Brain Image Alignment using Boundary-based Registration

By Douglas N. Greve, Bruce Fischl. NeuroImage, 2009

3,512 citations recorded by the source corpus.

The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial brain images to whole brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.

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

View Paper: https://www.semanticscholar.org/paper/c65d341e1313e153cbce97dc9de38bbf6b94bcfc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Automated brain extraction of multisequence MRI using artificial neural networks (2019, methodology): “First, all sequences were spatially aligned to the respective T1-w sequence by rigid registration with 6 degrees of freedom (Greve & Fischl, 2009; Jenkinson & Smith, 2001), resulting in a transformation matrix for each of them.”
FreeSurfer (2012, methodology): “FreeSurfer is constantly being improved and extended, with our most recent release including tools for accurate cross-modal intra-subject registration (Greve and Fischl, 2009), combined volume and surface cross-subject registration (Postelnicu et al.”
Laminar analysis of 7 T BOLD using an imposed spatial activation pattern in human V1 (2010, positive): “We employed a recently proposed boundary-based registration method (Greve and Fischl, 2009) that identifies the interface between gray matter and white matter in the EPI data and then calculates a rigid transformation that registers this interface in the EPI data to the corresponding surface…”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “, 2006), 3) spatial normalization from an individual functional space to MNI152 standard brain space (FLIRT+FNIRT in FSL), and 4) boundary-based registration between individual structural and functional images (Greve and Fischl, 2009).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “edu; Fischl 2012) and structure-function registration was performed using FsFast (http://surfer.nmr.mgh.harvard.edu/fswiki/FsFast; Greve and Fischl 2009).”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “1 http://surfer.nmr.mgh.harvard.edu/fswiki/recon-all 2 http://hdl.handle.net/10380/3053 3 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/UserGuide 4 http://brainsuite.org/processing/surfaceextraction/bse 5 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT 6 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT 7 http”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, methodology): “Both pipelines underwent the following steps: (1) removal of the first four frames, (2) slice time correction with FSL package, (3) motion correction using rigid body translation and rotation using FSL, together with outlier detection (see below), (4) alignment with structural image using boundary-b”
Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data (2011, background): “We then registered the two sessions to the subject’s native anatomical space (Greve and Fischl, 2009).”
The impact of T1 vs EPI spatial normalization templates for fMRI data analyses (2017, methodology): “BBR includes field inhomogeneity correction and aligns the EPI to the T1 by maximizing the intensity gradient across tissue boundaries [Greve and Fischl, 2009].”
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, methodology): “Functional data were registered to structural images using FREESURFER’s FsFast package ([65]; http://surfer.”
Non-invasive access to the vagus nerve central projections via electrical stimulation of the external ear: fMRI evidence in humans (2014, positive): “Registration of the functional images to the high-resolution anatomical images was performed using Boundary-Based Registration (BBR) [21].”
Volumetric Navigators (vNavs) for Prospective Motion Correction and Selective Reacquisition in Neuroanatomical MRI (2011, methodology): “The T2 SPACE or T2 SPACE FLAIR volumes for a subject were then brought into alignment with the computed surface model via bbregister (34), bringing the segmentation into alignment with the T2 SPACE (FLAIR) volumes and allowing us to select equivalent voxels within a given subject’s volumes and simil”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, methodology): “, 1999) followed by coregistering functional images to the reconstructed surface using BBRegister (Greve and Fischl, 2009).”
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, positive): “Registration was performed by first co-registering the IC spatial maps to the participant's structural image using an affine boundary-based registration (FSL-FLIRT; Jenkinson et al. (2002), Jenkinson and Smith (2001), Greve and Fischl (2009)).”
Effective learning is accompanied by high-dimensional and efficient representations of neural activity (2017, methodology): “between structural and mean functional image was performed with Freesurfer bbregister [26].”
Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2⁎-imaging at ultra-high magnetic field strength (2013, positive): “Magnitude information of both single echo GRE data in normal position and multi-echo GRE data were registered to the T1-weighted dataset using boundary-based registration (Greve and Fischl, 2009).”