Biomedical subjects

J Hardies

Publications and source records attributed to J Hardies.

3 recordsLinked to original sources

PubMed2002

An optimized individual target brain in the Talairach coordinate system.

The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional brain images by warping them to match features of a single target brain. Current target brains are either an average, suitable for low-resolution brain mapping studies, or a single brain. While a single high-resolution target brain is desirable to match anatomical detail, it can lead to bias in anatomical studies. An optimization method to reduce the individual anatomical bias of the ICBM high-resolution brain template (HRBT), a high-resolution MRI target brain image used in many laboratories, is presented. The HRBT was warped to all images in a group of 27 normal subjects. Displacement fields were averaged to calculate the "minimal deformation target" (MDT) transformation for optimization. The greatest anatomical changes in the HRBT, following optimization, were observed in the superior precentral and postcentral gyri on the right, the right inferior occipital, the right posterior temporal lobes, and the lateral ventricles. Compared with the original HRBT, the optimized HRBT showed better anatomical matching to the group of 27 brains. This was quantified by the improvements in spatial cross-correlation and between the group of brains and the optimized HRBT (P < 0.05). An intended use of this processing is to create a digital volumetric atlas that represents anatomy of a normal adult brain by optimizing the HRBT to the group consisting of 100+ normal subjects.

Adult↗

PubMed2000

Evaluation of octree regional spatial normalization method for regional anatomical matching.

The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional (3D) brain images by warping them to match features of a standard brain atlas. Processing to fit features at the limiting resolution of a 3D MR image volume is computationally intensive, limiting the broad use of full-resolution regional spatial normalization. In Kochunov et al. (1999: Neuro-Image 10:724-737), we proposed a regional spatial normalization algorithm called octree spatial normalization (OSN) that reduces processing time to minutes while targeting the accuracy of previous methods. In the current study, modifications of the OSN algorithm for use in human brain images are described and tested. An automated brain tissue segmentation procedure was adopted to create anatomical templates to drive feature matching in white matter, gray matter, and cerebral-spinal fluid. Three similarity measurement functions (fast-cross correlation (CC), sum-square error, and centroid) were evaluated in a group of six subjects. A combination of fast-CC and centroid was found to provide the best feature matching and speed. Multiple iterations and multiple applications of the OSN algorithm were evaluated to improve fit quality. Two applications of the OSN algorithm with two iterations per application were found to significantly reduce volumetric mismatch (up to six times for lateral ventricle) while keeping processing time under 30 min. The refined version of OSN was tested with anatomical landmarks from several major sulci in a group of nine subjects. Anatomical variability was appreciably reduced for every sulcus investigated, and mean sulcal tracings accurately followed sulcal tracings in the target brain.

Algorithms↗

PubMedDate not supplied

Regional spatial normalization: toward an optimal target.

PURPOSE: The purpose of this work was to develop methods for defining, constructing, and evaluating a "minimal deformation target" (MDT) brain for multisubject studies based on analysis of the entire group. The goal is to provide a procedure that will create a standard, reproducible target brain image based on common features of a group of three-dimensional MR brain images. METHOD: The average deformation and dispersion distance, derived from discrete three-dimensional deformation fields (DFs), are used to identify the best individual target (BIT) brain. This brain is assumed to be the one with the minimal deformation bias within a group of MR brain images. The BIT brain is determined as the one with the minimal target quality score, our cost function based on the deformation displacement and dispersion distance. The BIT brain is then transformed to the MDT brain using an average DF to create an optimized target brain. This analysis requires the calculation of a large number of DFs. To overcome this limitation, we developed an analysis method (the fast method) that reduces the task from order N2 complexity to one of order N, a tremendous advantage for large-N studies. RESULTS: Multiscale correlation analysis in a group of 20 subjects demonstrated the superiority of warping using the MDT target brain, made from the BIT brain, over several individual and MDT-transformed target brains also from the group. CONCLUSION: Analysis of three-dimensional DF provides a means to quickly create a reproducible MDT target brain for any set of subjects. Warping to the MDT target was shown by an independent multiscale correlation method to produce superior results.

Adult↗