Super-Resolution Reconstruction of Multi-Slice T2-W FLAIR MRI Improves Multiple Sclerosis Lesion Segmentation.

Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and subsequent lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.Clinical relevance- This study demonstrates the positive impact of super-resolution reconstruction from T2-w FLAIR multi-slice MRI acquisitions on segmentation performance of MS lesions.

Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI.

BACKGROUND: Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE: In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS: By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS: Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION: This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.

Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer's disease dementia.

INTRODUCTION: Neuropsychological test scores are limited and standard outcomes may mask the heterogeneity of cognitive impairment. This article presents the calculation and evaluation of six composite scores that quantify domain-specific impairment. METHODS: Parameters for composite scores calculation were learned by performing confirmatory factor analysis in a sample of participants from the Alzheimer's Disease Neuroimaging Initiative database. The obtained scores were evaluated with a separate sample of mild cognitive impairment (MCI) in two automated tasks: unsupervised partition in different subgroups and prediction of progression to dementia for different time windows. RESULTS: MCI subgroups with distinctive cognitive profiles and risk of progression emerged from cluster analysis. Composite scores outperform standard neuropsychological tests when automatically predicting progression within time windows up to 5 years. CONCLUSIONS: Domain-specific composite scores are useful to delineate profiles of impairment, stratify the MCI risk, and predict progression to dementia.

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease.

Purpose: This work presents an automatic characterization of the Alzheimer's disease describing the illness as a multidirectional departure from a baseline defining the control state, being these directions determined by a distance between functional-equivalent anatomical regions. Methods: After a brain parcellation, a region is described by its histogram of gray levels, and the Earth mover's distance establishes how close or far these regions are. The medoid of the control group is set as the reference and any brain is characterized by its set of distances to this medoid. Evaluation: This hypothesis was assessed by separating groups of patients with mild Alzheimer's disease and mild cognitive impairment from control subjects, using a subset of the Open Access Series of Imaging Studies (OASIS) database. An additional experiment evaluated the method generalization and consisted in training with the OASIS data and testing with the Minimal Interval Resonance Imaging in Alzheimer's disease (MIRIAD) database. Results: Classification between controls and patients with AD resulted in an equal error rate of 0.1 (90% of sensitivity and specificity at the same time). The automatic ranking of regions resulting is in strong agreement with those regions described as important in clinical practice. Classification with different databases results in a sensitivity of 85% and a specificity of 91%. Conclusions: This method automatically finds out a multidimensional expression of the AD, which is directly related to the anatomical changes in specific areas such as the hippocampus, the amygdala, the planum temporale, and thalamus.