High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas.

The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p < .05) estimations of the whole amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders.

Characterization of highly multiplexed monolithic PET / gamma camera detector modules.

PET detectors use signal multiplexing to reduce the total number of electronics channels needed to cover a given area. Using measured thin-beam calibration data, we tested a principal component based multiplexing scheme for scintillation detectors. The highly-multiplexed detector signal is no longer amenable to standard calibration methodologies. In this study we report results of a prototype multiplexing circuit, and present a new method for calibrating the detector module with multiplexed data. A [Formula: see text] mm3 LYSO scintillation crystal was affixed to a position-sensitive photomultiplier tube with [Formula: see text] position-outputs and one channel that is the sum of the other 64. The 65-channel signal was multiplexed in a resistive circuit, with 65:5 or 65:7 multiplexing. A 0.9 mm beam of 511 keV photons was scanned across the face of the crystal in a 1.52 mm grid pattern in order to characterize the detector response. New methods are developed to reject scattered events and perform depth-estimation to characterize the detector response of the calibration data. Photon interaction position estimation of the testing data was performed using a Gaussian Maximum Likelihood estimator and the resolution and scatter-rejection capabilities of the detector were analyzed. We found that using a 7-channel multiplexing scheme (65:7 compression ratio) with 1.67 mm depth bins had the best performance with a beam-contour of 1.2 mm FWHM (from the 0.9 mm beam) near the center of the crystal and 1.9 mm FWHM near the edge of the crystal. The positioned events followed the expected Beer-Lambert depth distribution. The proposed calibration and positioning method exhibited a scattered photon rejection rate that was a 55% improvement over the summed signal energy-windowing method.

Thalami and corona radiata in juvenile NCL (CLN3): a voxel-based morphometric study.

Juvenile neuronal ceroid lipofuscinosis (CLN3) is characterized by progressive cerebral atrophy. The purpose of this study was to re-evaluate the three-dimensional magnetic resonance (3D-MR) images of patients with CLN3 using voxel-based morphometry (VBM) to achieve a detailed understanding of the affected brain regions. T1-weighted 3D-MR images of 15 patients with CLN3 (age range: 12-25 years, mean age 17.6 years) and 15 age- and sex-matched controls were analyzed using VBM. VBM showed strikingly focal alterations in the brains of CLN3 patients: the gray matter volume was significantly decreased in the dorsomedial part of the thalami of CLN3 patients. In addition, the volume of the white matter was significantly decreased in the corona radiata, containing cortical efferents and afferents in the transition between the internal capsule and the subcortical white matter. These data suggest that the dorsomedial part of the thalamus and the corona radiata may have a central, previously unrecognized role in the pathogenesis of CLN3.

Automated segmentation of multiple sclerosis lesions by model outlier detection.

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.

Automated model-based bias field correction of MR images of the brain.

We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.

Automated model-based tissue classification of MR images of the brain.

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Mild cognitive impairment: differential atrophy in the hippocampal subfields.

BACKGROUND AND PURPOSE: Hippocampus volumetry is a useful surrogate marker for the diagnosis of Alzheimer disease, but it seems insufficiently sensitive for the aMCI stage. We postulated that some hippocampus subfields are specifically atrophic in aMCI and that measuring hippocampus subfield volumes will improve sensitivity of MR imaging to detect aMCI. MATERIALS AND METHODS: We evaluated episodic memory and hippocampus subfield volume in 15 patients with aMCI and 15 matched controls. After segmentation of the whole hippocampus from clinical MR imaging, we applied a new computational method allowing fully automated segmentation of the hippocampus subfields. This method used a Bayesian modeling approach to infer segmentations from the imaging data. RESULTS: In comparison with controls, subiculum and CA2-3 were significantly atrophic in patients with aMCI, whereas total hippocampus volume and other subfields were not. Total hippocampus volume in controls was age-related, whereas episodic memory was the main explanatory variable for both the total hippocampus volume and the subfields that were atrophic in patients with aMCI. Segmenting subfields increases sensitivity to diagnose aMCI from 40% to 73%. CONCLUSIONS: Measuring CA2-3 and subiculum volumes allows a better detection of aMCI.