Image registration is a key component in existing magnetic resonance image processing software packages. Because of nonlinear variability in brain substructures, the commonly used registration pipelines applied to the entire brain area may not lead to accurate registration for substructures. This paper presents a two-stage registration approach for a better alignment of brain substructures or regions-of-interest. In the first stage, an affine transformation function is applied to the entire brain area and in the second stage, a nonrigid or deformable transformation function is applied to the substructure of interest. To show the usefulness of this two-stage registration, images from the Brainweb database are examined using normalized mutual information is used to assess the degree of alignment. The comparison results indicate improvements over the commonly used registration approaches.

We present the performance comparison of sub-band FXLMS algorithm for fMRI acoustic noise cancellation when the secondary path is non-minimum phase. Three types of least square adaptive filtering methods (nLMS, APA, RLS) are used in sub-bands. A series of simulations have been done using recorded fMRI acoustic noise and the results are given and compared based on the noise attenuation level, convergence rate and the quality of primary path estimation. It is verified that the spectrum structure of the fMRI acoustic noise has the main role in the performance of the active noise control especially when sub-band filtering is used.

This paper presents the use of Kullback-Leibler Distance (KLD) as part of an optimization framework to incorporate prior knowledge from field maps into non-rigid registration of echo-planar (EPI) to structural magnetic resonance brain images. An analytical expression is derived for the derivatives of KLD with respect to registration transformation parameters, which is shown to be computationally more efficient as compared to the derivatives of mutual information. Quantitative gold standard validation is carried out on simulated digital brain phantom images with synthesized deformations. In addition, in-vivo validation is performed via a cross-comparison of the similarity of high-resolution and low-resolution EPI to T1-and T2-weighted structural images. The results obtained indicate that the developed KLD-based non-rigid registration technique provides an effective way of correcting local distortions in echo-planar imaging.

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

We present an adaptive enhancement method for speech signal corrupted by the 3 Tesla (3T) functional Magnetic Resonance Imaging (fMRI) acoustic noise. The noisy speech signal is filtered into several frequency bands and noise cancellation is applied to each band independently. We use Normalized Least Mean Squares (NLMS) algorithm for updating the co-efficients of the adaptive filters in both wideband and in multi-band implementations. The NLMS algorithm is used to ensure faster convergence and stability. The multi-band method shows a better performance than the conventional wideband method resulting in a better noise reduction. It is shown that the distortion introduced by the band-pass filters limits the overall speech enhancement when the number of bands increases beyond an optimal value.

Functional to anatomical brain image registration is needed for accurate localization of brain activation maps. Due to the presence of nonlinear distortions, it is more effective to consider non-rigid transformations to achieve such a registration. In this paper, a non-rigid registration technique based on the B-spline free-form deformation model and mutual information similarity measure is introduced. An optimization formulation is devised to achieve a fast and robust registration. This formulation differs from the previous formulations by utilizing a limited-memory, second-order optimization algorithm instead of the usual first-order gradient-based algorithms. It also enforces hard parameter constraints instead of constraints based upon physics or Jacobian smoothness. The results obtained indicate that this registration technique provides improvements over rigid and affine techniques when registering functional to anatomical magnetic resonance brain images.