Tutorial 5: Image Registration

Autoalign: Methodology and Technology for the Alignment of Functional Magnetic Resonance Imaging Time Series: Image Registration: The Case of Functional MRI

by: Carlo Ciulla

Functional Magnetic Resonance Imaging (FMRI) is currently acquired in Time Series to detect non-invasively, in-vivo functional activity of the human brain, thus determining four dimensional data sets. Although detectable, due to the short time in between the acquisition of the brain volumes, head motion is usually minimal; however its correction is quite relevant to the consistency of the analysis of the FMRI data. This book presents methodology and technology for registering FMRI Time Series. Math formulations are given along with software engineering descriptions of the algorithms employed for the specific task of FMRI alignment. The book also presents the code that implements the methodology, which was written combining ANSI C, Open GL and Matlab. The intended audience of this work is composed by undergraduate and graduate students in academic disciplines within the domain of computer science applications, applied signal processing and computational engineering. This book is of interest to anyone in the interests of how computer science, mathematics and engineering may combine together to forge the developmental effort in the quest for the solution of a technical challenge.

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

Authors: Klein et al,

Abstract | All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.

More accurate Talairach coordinates for neuroimaging using non-linear registration.

Authors: Lacadie et al, 2008

Abstract | While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.

Consequences of large interindividual variability for human brain atlases: converging macroscopical imaging and microscopical neuroanatomy.

Authors: Uylings et al, 2005

Abstract | In human brain imaging studies, it is common practice to use the Talairach stereotaxic reference system for signifying the convergence of brain function and structure. In nearly all neuroimaging reports, the studied cortical areas are specified further with a Brodmann Area (BA) number. This specification is based upon macroscopic extrapolation from Brodmann's projection maps into the Talairach atlas rather than upon a real microscopic cytoarchitectonic study. In this review we argue that such a specification of Brodmann area(s) via the Talairach atlas is not appropriate. Cytoarchitectonic studies reviewed in this paper show large interindividual differences in 3-D location of primary sensory cortical areas (visual cortex) as well as heteromodal associational areas (prefrontal cortical areas), even after correction for differences in brain size and shape. Thus, the simple use of Brodmann cortical areas derived from the Talairach atlas can lead to erroneous results in the specification of pertinent BA. This in turn can further lead to wrong hypotheses on brain system(s) involved in normal functions or in specific brain disorders. In addition, we will briefly discuss the different 'Brodmann' nomenclatures which are in use for the cerebral cortex. (requires subscription or contact author for reprint).

The problem of functional localization in the human brain

Authors: Brett et al, 2002

Abstract |  Functional imaging gives us increasingly detailed information about the location of brain activity. To use this information, we need a clear conception of the meaning of location data. Here, we review methods for reporting location in functional imaging and discuss the problems that arise from the great variability in brain anatomy between individuals. These problems cause uncertainty in localization, which limits the effective resolution of functional imaging, especially for brain areas involved in higher cognitive function. (requires subscription or contact author for reprint).

CBU Imaging Wiki

Title | The MNI brain and the Talairach atlas.
This page discusses the MNI brain, and the difference between the MNI brain and the brain in the Talairach atlas.