Daniel H. Adler

An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging

In glioblastoma (GBM), promoter methylation of the DNA repair gene O(6)-methylguanine-DNA methyltransferase (MGMT) is associated with benefit from chemotherapy. Correlations between MGMT promoter methylation and visually assessed imaging features on magnetic resonance (MR) have been reported suggesting that noninvasive detection of MGMT methylation status might be possible. Our study assessed whether MGMT methylation status in GBM could be predicted using MR imaging. We conducted a retrospective analysis of MR images in patients with newly diagnosed GBM. Tumor texture was assessed by two methods. First, we analyzed texture by expert consensus describing the tumor borders, presence or absence of cysts, pattern of enhancement, and appearance of tumor signal in T2-weighted images. Then, we applied space-frequency texture analysis based on the S-transform. Tumor location within the brain was determined using automatized image registration and segmentation techniques. Their association with MGMT methylation was analyzed. We confirmed that ring enhancement assessed visually is significantly associated with unmethylated MGMT promoter status (P=0.006). Texture features on T2-weighted images assessed by the space-frequency analysis were significantly different between methylated and unmethylated cases (P<0.05). However, blinded classification of MGMT promoter methylation status reached an accuracy of only 71%. There were no significant differences in the locations of methylated and unmethylated GBM tumors. Our results provide further evidence that individual MR features are associated with MGMT methylation but better algorithms for predicting methylation status are needed. The relevance of this study lies on the application of novel techniques for the analysis of anatomical MR images of patients with GBM allowing the evaluation of subtleties not seen by an observer and facilitating the standardization of the methods, decreasing the potential for interobserver bias.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI.

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

Characterizing the human hippocampus in aging and Alzheimer’s disease using a computational atlas derived from ex vivo MRI and histology

Significance There has been increasing interest in hippocampal subfield morphometry in aging and disease using in vivo MRI. However, research on in vivo morphometry is hampered by the lack of a definitive reference model describing regional effects of aging and disease pathology on the hippocampus. To address this limitation, we built a 3D probabilistic atlas of the hippocampus combining postmortem MRI with histology, allowing us to investigate Alzheimer’s disease (AD)-related effects on hippocampal subfield morphometry, derived from histology. Our results support the hypothesis of differential involvement of hippocampal subfields in AD, providing further impetus for more granular study of the hippocampus in aging and disease during life. Furthermore, this atlas provides an important anatomical reference for hippocampal subfield research. Although the hippocampus is one of the most studied structures in the human brain, limited quantitative data exist on its 3D organization, anatomical variability, and effects of disease on its subregions. Histological studies provide restricted reference information due to their 2D nature. In this paper, high-resolution (∼200 × 200 × 200 μm3) ex vivo MRI scans of 31 human hippocampal specimens are combined using a groupwise diffeomorphic registration approach into a 3D probabilistic atlas that captures average anatomy and anatomic variability of hippocampal subfields. Serial histological imaging in 9 of the 31 specimens was used to label hippocampal subfields in the atlas based on cytoarchitecture. Specimens were obtained from autopsies in patients with a clinical diagnosis of Alzheimers disease (AD; 9 subjects, 13 hemispheres), of other dementia (nine subjects, nine hemispheres), and in subjects without dementia (seven subjects, nine hemispheres), and morphometric analysis was performed in atlas space to measure effects of age and AD on hippocampal subfields. Disproportional involvement of the cornu ammonis (CA) 1 subfield and stratum radiatum lacunosum moleculare was found in AD, with lesser involvement of the dentate gyrus and CA2/3 subfields. An association with age was found for the dentate gyrus and, to a lesser extent, for CA1. Three-dimensional patterns of variability and disease and aging effects discovered via the ex vivo hippocampus atlas provide information highly relevant to the active field of in vivo hippocampal subfield imaging.

Probabilistic Atlas of the Human Hippocampus Combining Ex Vivo MRI and Histology

The human hippocampus is a complex structure consisting of multiple anatomically and functionally distinct subfields. Obtaining subfield-specific measures from in vivo MRI is challenging, and can benefit from a detailed 3D anatomical reference. This paper builds a computational atlas of the hippocampus from high-resolution ex vivo MRI of 26 specimens using groupwise deformable registration. A surface-based approach based on the explicit segmentation and geometric modeling of hippocampal layers is used to initialize deformable registration of ex vivo MRI scans. This initialization improves of groupwise registration quality, as measured in terms of similarity metrics and qualitatively. The resulting atlas, which also includes annotations mapped from histology, is a unique resource for describing variability in hippocampal anatomy.

A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe

Automatic segmentation of cortical and subcortical structures is commonplace in brain MRI literature and is frequently used as the first step towards quantitative analysis of structural and functional neuroimaging. Most approaches to brain structure segmentation are based on propagation of anatomical information from example MRI datasets, called atlases or templates, that are manually labeled by experts. The accuracy of automatic segmentation is usually validated against the “bronze” standard of manual segmentation of test MRI datasets. However, good performance vis-a-vis manual segmentation does not imply accuracy relative to the underlying true anatomical boundaries. In the context of segmentation of hippocampal subfields and functionally related medial temporal lobe cortical subregions, we explore the challenges associated with validating existing automatic segmentation techniques against underlying histologically-derived anatomical “gold” standard; and, further, developing automatic in vivo MRI segmentation techniques informed by histological imaging.

ALZHEIMER’S DISEASE AND THE HIPPOCAMPUS: NOVEL INSIGHTS FROM AN EX VIVO COMPUTATIONAL ATLAS COMBINING MRI AND HISTOLOGY

Institute, Singapore, Singapore; McGill University Research Centre for Studies in Aging, Verdun, QC, Canada; Translational Neuroimaging LaboratoryMcGill University, Verdun, QC, Canada; Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; McConnell Brain Imaging Centre McGill University, Montr eal, QC, Canada; Douglas Hospital Research Centre, Verdun, QC, Canada. Contact e-mail: ng.kok.pin@singhealth.com.sg