Sentence examples for mutual information cost from inspiring English sources

The MR T1-weighted image was registered to the time-weighted integral of the dynamic PET images following motion correction using the rigid registration function in SPM8 with a mutual information cost function.

Dynamic PET data were corrected for motion via frame-to-frame image registration and aligned with the structural T1 MRI image using SPM5 (Wellcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm) with a mutual information cost function.

The registration used a global affine (12 df) transformation, a mutual information cost function and sinc resampling.

Using the mean realigned image, all images were coregistered to a segmented high-resolution structural scan (voxel size, 1 × 1 × 1 mm) using a normalized mutual information cost function.

Fractional Anisotropy (FA) maps were acquired from the DTI images from the software DTI Studio [ 27] and were then coregistered automatically to the MP-RAGE images using a mutual information cost function and a 12 parameter affine transformation.

Images were co-registered to the T1-weighted image using the Functional Magnetic Resonance Imaging of the Brain (FMRIB Software Libraryy's (FSL) flirt tool and a normalised mutual information cost function [ 41].

Then, the best candidate values are selected jointly for all the voxels by simultaneously maximizing a global mutual information-based cost function and a local spatial consistency cost function.

Slice timing correction was applied and each individual's fMRI time-series was coregistered to a T1-weighted structural image using the mean image as the reference and normalized mutual information as the cost function.

When the mutual information term in the cost function is substantially greater than the constraint term, both the healthy tissue and the tumor area will be stretched during registration, and this could result in tumor expansion.

Since for the computation of the term f x (x) is taken into account i.e., the dependencies are considered, the mutual information is a stronger cost function than Rényi divergence and using it for resolving the permutations, better results are to be expected.

Each reverse-engineering method has specific features and can be suitable for different needs: for example, Bayesian networks can provide statistically rigorous results but at the price of high computational costs, whereas mutual information approaches are computationally fast but are limited mostly by the lack of directionality of the inferred links.