Topological correction of brain surface meshes using spherical harmonics

Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter.

Cortical surface complexity in a population-based normative sample

MRI studies on abnormal brain development are dependent on the quality, quantity, and type of normative development data available for comparison. Limitations affecting previous studies on normative development include small sample sizes, lack of demographic representation, heterogeneous subject populations, and inadequate longitudinal data. The National Institutes of Health Pediatric MRI Data Repository (NIHPD) for normative development was designed to address the aforementioned issues in reliability measures of control subjects for comparison studies. The subjects were recruited from six Pediatric Study Centers nationwide to create the largest, non-biased, longitudinal database of the developing brain. Using the NIHPD, we applied a 3D shape analysis method involving spherical harmonics to identify the cortical surface complexity of 396 subjects (210 female; 186 male) between the ages of 4.8 y and 22.3 y. MRI data had been obtained at one, two, or three time points approximately two years apart. A total of 144 participants (79 female; 65 male) provided MRI data from all time points. Our results confirm a direct correlation between cortical complexity and age in both males and females. Additionally, within the examined age range, females displayed consistently and significantly greater cortical complexity than males. Findings suggest that the underlying neural circuitry within male and female brains is different, possibly explaining observations of sexual dimorphism in social interaction, communication, and higher cognitive processes.

Cortical surface reconstruction via unified Reeb analysis of geometric and topological outliers in magnetic resonance images

In this paper we present a novel system for the automated reconstruction of cortical surfaces from T1-weighted magnetic resonance images. At the core of our system is a unified Reeb analysis framework for the detection and removal of geometric and topological outliers on tissue boundaries. Using intrinsic Reeb analysis, our system can pinpoint the location of spurious branches and topological outliers, and correct them with localized filtering using information from both image intensity distributions and geometric regularity. In this system, we have also developed enhanced tissue classification with Hessian features for improved robustness to image inhomogeneity, and adaptive interpolation to achieve sub-voxel accuracy in reconstructed surfaces. By integrating these novel developments, we have a system that can automatically reconstruct cortical surfaces with improved quality and dramatically reduced computational cost as compared with the popular FreeSurfer software. In our experiments, we demonstrate on 40 simulated MR images and the MR images of 200 subjects from two databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and International Consortium of Brain Mapping (ICBM), the robustness of our method in large scale studies. In comparisons with FreeSurfer, we show that our system is able to generate surfaces that better represent cortical anatomy and produce thickness features with higher statistical power in population studies.