REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses

ありふれた脳の白質病変がMRI画像解析を悪化させていた --従来手法に機械学習を組み入れた改善手法の開発--. 京都大学プレスリリース. 2023-10-02.The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders

Artificial Intelligence for the Detection of Focal Cortical Dysplasia: Challenges in Translating Algorithms into Clinical Practice

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment

BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. © 2016 The Author

Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies

Background: White matter hyperintensities of presumed vascular origin (WMH) are a common finding in elderly people and a growing social malady in the aging western societies. As a manifestation of cerebral small vessel disease, WMH are considered to be a vascular contributor to various sequelae such as cognitive decline, dementia, depression, stroke as well as gait and balance problems. While pathophysiology and therapeutical options remain unclear, large-scale studies have improved the understanding of WMH, particularly by quantitative assessment of WMH. In this review, we aimed to provide an overview of the characteristics, research subjects and segmentation techniques of these studies.Methods: We performed a systematic review according to the PRISMA statement. One thousand one hundred and ninety-six potentially relevant articles were identified via PubMed search. Six further articles classified as relevant were added manually. After applying a catalog of exclusion criteria, remaining articles were read full-text and the following information was extracted into a standardized form: year of publication, sample size, mean age of subjects in the study, the cohort included, and segmentation details like the definition of WMH, the segmentation method, reference to methods papers as well as validation measurements.Results: Our search resulted in the inclusion and full-text review of 137 articles. One hundred and thirty-four of them belonged to 37 prospective cohort studies. Median sample size was 1,030 with no increase over the covered years. Eighty studies investigated in the association of WMH and risk factors. Most of them focussed on arterial hypertension, diabetes mellitus type II and Apo E genotype and inflammatory markers. Sixty-three studies analyzed the association of WMH and secondary conditions like cognitive decline, mood disorder and brain atrophy. Studies applied various methods based on manual (3), semi-automated (57), and automated segmentation techniques (75). Only 18% of the articles referred to an explicit definition of WMH.Discussion: The review yielded a large number of studies engaged in WMH research. A remarkable variety of segmentation techniques was applied, and only a minority referred to a clear definition of WMH. Most addressed topics were risk factors and secondary clinical conditions. In conclusion, WMH research is a vivid field with a need for further standardization regarding definitions and used methods

The Effects of Longitudinal White Matter Hyperintensity Change on Cognitive Decline and Cortical Thinning over Three Years

White matter hyperintensity (WMH) has been recognised as a surrogate marker of small vessel disease and is associated with cognitive impairment. We investigated the dynamic change in WMH in patients with severe WMH at baseline, and the effects of longitudinal change of WMH volume on cognitive decline and cortical thinning. Eighty-seven patients with subcortical vascular mild cognitive impairment were prospectively recruited from a single referral centre. All of the patients were followed up with annual neuropsychological tests and 3T brain magnetic resonance imaging. The WMH volume was quantified using an automated method and the cortical thickness was measured using surface-based methods. Participants were classified into WMH progression and WMH regression groups based on the delta WMH volume between the baseline and the last follow-up. To investigate the effects of longitudinal change in WMH volume on cognitive decline and cortical thinning, a linear mixed effects model was used. Seventy patients showed WMH progression and 17 showed WMH regression over a three-year period. The WMH progression group showed more rapid cortical thinning in widespread regions compared with the WMH regression group. However, the rate of cognitive decline in language, visuospatial function, memory and executive function, and general cognitive function was not different between the two groups. The results of this study indicated that WMH volume changes are dynamic and WMH progression is associated with more rapid cortical thinning

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders