A knowledge-guided active model method of skull segmentation on T1-weighted MR images

Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm based on normalized mutual information. The transformed mesh models actively located skull boundaries by minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using KAM

Sex-based differences in functional brain activity during working memory in survivors of pediatric acute lymphoblastic leukemia

BACKGROUND: Long-term survivors of pediatric acute lymphoblastic leukemia are at elevated risk for neurocognitive deficits and corresponding brain dysfunction. This study examined sex-based differences in functional neuroimaging outcomes in acute lymphoblastic leukemia survivors treated with chemotherapy alone. METHODS: Functional magnetic resonance imaging (fMRI) and neurocognitive testing were obtained in 123 survivors (46% male; median [min-max] age = 14.2 years [8.3-26.5 years]; time since diagnosis = 7.7 years [5.1-12.5 years]) treated on the St. Jude Total XV treatment protocol. Participants performed the n-back working memory task in a 3 T scanner. Functional neuroimaging data were processed (realigned, slice time corrected, normalized, smoothed) and analyzed using statistical parametric mapping with contrasts for 1-back and 2-back conditions, which reflect varying degrees of working memory and task load. Group-level fMRI contrasts were stratified by sex and adjusted for age and methotrexate exposure. Statistical tests were 2-sided (P \u3c .05 statistical significance threshold). RESULTS: Relative to males, female survivors exhibited less activation (ie, reduced blood oxygen dependent-level signals) in the right parietal operculum, supramarginal gyrus and inferior occipital gyrus, and bilateral superior frontal medial gyrus during increased working memory load (family-wise error-corrected P = .004 to .008, adjusting for age and methotrexate dose). Female survivors were slower to correctly respond to the 2-back condition than males (P \u3c .05), though there were no differences in overall accuracy. Performance accuracy was negatively correlated with fMRI activity in female survivors (Pearson\u27s r = -0.39 to -0.29, P = .001 to .02), but not in males. CONCLUSIONS: These results suggest the working memory network is more impaired in female survivors than male survivors, which may contribute to ongoing functional deficits

A 3D model-based simulation of demyelination to understand its effects on diffusion tensor imaging

Demyelination is the progressive damage to the myelin sheath, a protective covering that surrounds a nerve\u27s axon. Due to its high sensitivity to microscopic tissue changes, diffusion tensor imaging (DTI) is a powerful means of detecting signs of demyelination and axonal injury. In this paper, we present a 3D virtual model capable of simulating the complex Brownian motion of water molecules in a bundle of myelinated axons and glial cells for the purpose of synthesizing DTI data, characterizing and verifying the impact of demyelination on DTI. Our model consists of a highly detailed and realistic 3D representation of biological fiber bundles, with a myelin sheath covering the axons and glial cells in between them. The system simulates the Brownian motion of molecules to extract diffusion data. We perform our experiment for progressive stages of demyelination and demonstrate its effect on DTI measurements

Reduced brain microstructural asymmetry in patients with childhood leukemia treated with chemotherapy compared with healthy controls.

Microstructural asymmetry of the brain can provide more direct causal explanations of functional lateralization than can macrostructural asymmetry. We performed a cross-sectional diffusion imaging study of 314 patients treated for childhood acute lymphoblastic leukemia (ALL) at a single institution and 92 healthy controls. An asymmetry index based on diffusion metrics was computed to quantify brain microstructural asymmetry. The effects of age and the asymmetry metrics of the two cohorts were examined with t-tests and linear models. We discovered two new types of microstructural asymmetry. Myelin-related asymmetry in controls was prominent in the back brain (89% right), whereas axon-related asymmetry occurred in the front brain (67% left) and back brain (88% right). These asymmetries indicate that white matter is more mature and more myelinated in the left back brain, potentially explaining the leftward lateralization of language and visual functions. The asymmetries increase throughout childhood and adolescence (P = 0.04) but were significantly less in patients treated for ALL (P<0.01), especially in younger patients. Our results indicate that atypical brain development may appear long before patients treated with chemotherapy become symptomatic

Cerebella segmentation on MR images of pediatric patients with medulloblastoma

In this study, an automated method has been developed to identify the cerebellum from T1-weighted MR brain images of patients with medulloblastoma. A new objective function that is similar to Gibbs free energy in classic physics was defined; and the brain structure delineation was viewed as a process of minimizing Gibbs free energy. We used a rigid- body registration and an active contour (snake) method to minimize the Gibbs free energy in this study. The method was applied to 20 patient data sets to generate cerebellum images and volumetric results. The generated cerebellum images were compared with two manually drawn results. Strong correlations were found between the automatically and manually generated volumetric results, the correlation coefficients with each of manual results were 0.971 and 0.974, respectively. The average Jaccard similarities with each of two manual results were 0.89 and 0.88, respectively. The average Kappa indexes with each of two manual results were 0.94 and 0.93, respectively. These results showed this method was both robust and accurate for cerebellum segmentation. The method may be applied to various research and clinical investigation in which cerebellum segmentation and quantitative MR measurement of cerebellum are needed

Retrospective Evaluation of PET-MRI Registration Algorithms

The purpose of this study is to evaluate the accuracy of registration positron emission tomography (PET) head images to the MRI-based brain atlas. The [18F]fluoro-2-deoxyglucose PET images were normalized to the MRI-based brain atlas using nine registration algorithms including objective functions of ratio image uniformity (RIU), normalized mutual information (NMI), and normalized cross correlation (CC) and transformation models of rigid-body, linear, affine, and nonlinear transformations. The accuracy of normalization was evaluated by visual inspection and quantified by the gray matter (GM) concordance between normalized PET images and the brain atlas. The linear and affine registration based on the RIU provided the best GM concordance (average similarity index of 0.71 for both). We also observed that the GM concordances of linear and affine registration were higher than those of the rigid and nonlinear registration among the methods evaluated

Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks

We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen selforganizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard Tl-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (r;) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p \u3c0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intraand interobserver variability. © 1997 IEEE

Early Imaging-Based Predictive Modeling of Cognitive Performance following Therapy for Childhood ALL

In the United States, Acute Lymphoblastic Leukemia (ALL), the most common child and adolescent malignancy, accounts for roughly 25% of childhood cancers diagnosed annually with a 5-year survival rate as high as 94%. This improved survival rate comes with an increased risk for delayed neurocognitive effects in attention, working memory, and processing speed. Predictive modeling and characterization of neurocognitive effects are critical to inform the family and also to identify patients for interventions targeting. Current state-of-the-art methods mainly use hypothesis-driven statistical testing methods to characterize and model such cognitive events. While these techniques have proven to be useful in understanding cognitive abilities, they are inadequate in explaining causal relationships, as well as individuality and variations. In this study, we developed multivariate data-driven models to measure the late neurocognitive effects of ALL patients using behavioral phenotypes, Diffusion Tensor Magnetic Resonance Imaging (DTI) based tractography data, morphometry statistics, tractography measures, behavioral, and demographic variables. Alongside conventional machine learning and graph mining, we adopted \u27Stability Selection\u27 to select the most relevant features and choose models that are consistent over a range of parameters. The proposed approach demonstrated substantially improved accuracy (13%-26%) over existing models and also yielded relevant features that were verified by domain experts