Neuropsychiatric symptoms as a sign of small vessel disease progression in cognitive impairment

BACKGROUND: Neuropsychiatric symptoms associate cross-sectionally with cerebral small vessel disease but it is not clear whether these symptoms could act as early clinical markers of small vessel disease progression. We investigated whether longitudinal change in Neuropsychiatric Inventory (NPI) scores associated with white matter hyperintensity (WMH) progression in a memory clinic population. MATERIAL AND METHODS: We included participants from the prospective Sunnybrook Dementia Study with Alzheimer's disease and vascular subtypes of mild cognitive impairment and dementia with two MRI and ≥ 1 NPI. We conducted linear mixed-effects analyses, adjusting for age, atrophy, vascular risk factors, cognition, function, and interscan interval. RESULTS: At baseline (n=124), greater atrophy, age, vascular risk factors and total NPI score were associated with higher baseline WMH volume, while longitudinally, all but vascular risk factors were associated. Change in total NPI score was associated with change in WMH volume, χ2 = 7.18, p = 0.007, whereby a one-point change in NPI score from baseline to follow-up was associated with a 0.0017 change in normalized WMH volume [expressed as cube root of (WMH volume cm³ as % intracranial volume)], after adjusting for age, atrophy, vascular risk factors and interscan interval. CONCLUSIONS: In memory clinic patients, WMH progression over 1–2 years associated with worsening neuropsychiatric symptoms, while WMH volume remained unchanged in those with stable NPI scores in this population with low background WMH burden

Standardisierter T1w/T2w-Quotient als Marker für mikrostrukturelle Gewebeschäden bei neurologischen Erkrankungen

Introduction Microstructural tissue damage in neurological disorders is typically measured using advanced magnetic resonance imaging (MRI) techniques, such as diffusion-based or quantitative imaging, that require additional scan time and expertise in post-processing, limiting their feasibility in the clinical routine. The ratio of T1-weighted to T2-weighted images (T1w/T2w ratio) was proposed as an alternative to measure tissue microstructure, as it uses scans typically acquired in clinical routine and has simple post-processing. This dissertation investigates the feasibility of a standardized T1w/T2w ratio method and its sensitivity to microstructural tissue damage in multiple sclerosis (MS) and multiple system atrophy (MSA). Methods In Study I, the standardized and conventional T1w/T2w ratios in the gray and white matter were compared between 47 MS patients and healthy controls (matched for age and sex) and clinical correlates (e.g. lesion load, disease severity) were investigated. Study II investigated longitudinal changes in standardized T1w/T2w ratio in the white matter from the first clinical presentation of 102 MS patients and evaluated its association with cortical thickness and disease activity, defined using the No Evidence of Disease Activity (NEDA-3) criteria. Study III investigated whether standardized T1w/T2w ratio values in the middle cerebellar peduncle differed between 28 MSA patients and healthy controls matched for age and sex. Results The standardized T1w/T2w ratio was shown to reduce variability of white matter values and enhance sensitivity to normal-appearing white matter (NAWM) damage in MS patients (Study I). We showed that NAWM standardized T1w/T2w ratio values did not significantly differ from controls in early MS at first clinical presentation but that these values were significantly associated with increasing lesion volume and decreasing cortical thickness over time, mediated by disease activity (Study II). In MSA we showed that the middle cerebellar peduncle standardized T1w/T2w ratio had a high sensitivity and specificity to classify MSA patients compared to controls (Study III). Conclusions This dissertation demonstrates that the standardized T1w/T2w ratio is a valid and more sensitive marker of microstructural tissue damage compared to the conventional T1w/T2w ratio. Furthermore, the standardized T1w/T2w ratio can be used to investigate microstructural damage in neurological disorders such as MS and MSA, corroborating and expanding on findings from more established measures of microstructural damage, such as diffusion tensor imaging. The standardized T1w/T2w ratio represents an important and promising measure of microstructural damage in settings where additional scan time is limited or for retrospective studies where quantitative or diffusion-based MRI data are not available.Mikrostrukturelle Gewebeschäden bei neurologischen Erkrankungen werden typischerweise mit fortschrittlichen Magnetresonanztomographie-Techniken (MRT) wie diffusionsbasierte oder quantitative Verfahren gemessen, die zusätzliche Scan-Zeit und Expertise in der Nachbearbeitung erfordern, was ihre Durchführbarkeit in der klinischen Routine einschränkt. Das Verhältnis von T1-gewichteten zu T2-gewichteten Bildern (T1w/T2w-Verhältnis) wurde als Alternative zur Messung der Gewebemikrostruktur vorgeschlagen, da es Scans verwendet, die im klinischen Alltag aufgenommen werden und eine einfache Nachbearbeitung ermöglichen. Diese Dissertation untersucht die Durchführbarkeit einer standardisierten T1w/T2w-Ratio-Methode und ihre Sensitivität für mikrostrukturelle Schäden bei Multipler Sklerose (MS) und Multipler Systematrophie (MSA). Methoden In Studie I wurden die standardisierten und konventionellen T1w/T2w-Verhältniswerte in der grauen und weißen Substanz zwischen 47 MS Patienten und gematchten gesunden Kontrollen (gematcht für Alter und Geschlecht) verglichen sowie klinische Korrelate (wie z.B. Läsionslast, Krankheitsschwere) untersucht. Studie II untersuchte longitudinale Veränderungen des standardisierten T1w/T2w-Verhältnisses in der weißen Substanz ab der ersten klinischen Präsentation von 102 MS Patienten und bewertete ihre Assoziation mit der kortikalen Dicke und der Krankheitsaktivität, definiert anhand der “No Evidence of Disease Activity” (keine Hinweise auf Krankheitsaktivität; NEDA-3) Kriterien. In Studie III wurde untersucht, ob sich die standardisierten T1w/T2w-Verhältniswerte im mittleren Kleinhirnstiel zwischen 28 MSA Patienten und für Alter und Geschlecht gematchten gesunden Kontrollen unterscheiden. Ergebnisse Es wurde gezeigt, dass das standardisierte T1w/T2w-Verhältnis die Variabilität der Werte der weißen Substanz reduziert und die Sensitivität für normal erscheinende Schäden der weißen Substanz bei MS Patienten erhöht (Studie I). Wir konnten zeigen, dass sich die Werte des standardisierten T1w/T2w-Verhältnisses der normal erscheinenden weißen Substanz bei der ersten klinischen Präsentation der MS nicht signifikant von denen der Kontrollgruppe unterscheiden, dass diese Werte jedoch signifikant mit dem zunehmenden Läsionsvolumen und der abnehmenden kortikalen Dicke im Laufe der Zeit verbunden sind, was durch die Krankheitsaktivität vermittelt wird (Studie II). Bei MSA zeigten wir, dass das standardisierte T1w/T2w-Verhältnis des mittleren Kleinhirnstiels eine hohe Sensitivität und Spezifität zur Klassifizierung von MSA Patienten im Vergleich zu Kontrollen aufweist (Studie III). Schlussfolgerungen Diese Dissertation zeigt, dass das standardisierte T1w/T2w-Verhältnis ein valider und sensitiverer Marker für mikrostrukturelle Schäden im Vergleich zum konventionellen T1w/T2w-Verhältnis ist. Darüber hinaus kann das standardisierte T1w/T2w-Verhältnis zur Untersuchung mikrostruktureller Schäden bei neurologischen Erkrankungen (MS und MSA) verwendet werden und bestätigt und erweitert die Ergebnisse etablierter Maße für mikrostrukturelle Schäden wie diffusionsbasierte MRT Verfahren. Das standardisierte T1w/T2w-Verhältnis stellt ein wichtiges und vielversprechendes Maß für mikrostrukturelle Gewebeschäden in Situationen dar, in denen zusätzliche Scan-Zeit begrenzt ist oder für retrospektive Studien, in denen quantitative oder diffusionsbasierte MRT-Daten nicht verfügbar sind

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields

Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence

Surface loss for medical image segmentation

Last decades have witnessed an unprecedented expansion of medical data in various largescale and complex systems. While achieving a lot of successes in many complex medical problems, there are still some challenges to deal with. Class imbalance is one of the common problems of medical image segmentation. It occurs mostly when there is a severely unequal class distribution, for instance, when the size of target foreground region is several orders of magnitude less that the background region size. In such problems, typical loss functions used for convolutional neural networks (CNN) segmentation fail to deliver good performances. Widely used losses,e.g., Dice or cross-entropy, are based on regional terms. They assume that all classes are equally distributed. Thus, they tend to favor the majority class and misclassify the target class. To address this issue, the main objective of this work is to build a boundary loss, a distance based measure on the space of contours and not regions. We argue that a boundary loss can mitigate the problems of regional losses via introducing a complementary distance-based information. Our loss is inspired by discrete (graph-based) optimization techniques for computing gradient flows of curve evolution. Following an integral approach for computing boundary variations, we express a non-symmetric L2 distance on the space of shapes as a regional integral, which avoids completely local differential computations. Our boundary loss is the sum of linear functions of the regional softmax probability outputs of the network. Therefore, it can easily be combined with standard regional losses and implemented with any existing deep network architecture for N-dimensional segmentation (N-D). Experiments were carried on three benchmark datasets corresponding to increasingly unbalanced segmentation problems: Multi modal brain tumor segmentation (BRATS17), the ischemic stroke lesion (ISLES) and white matter hyperintensities (WMH). Used in conjunction with the region-based generalized Dice loss (GDL), our boundary loss improves performance significantly compared to GDL alone, reaching up to 8% improvement in Dice score and 10% improvement in Hausdorff score. It also yielded a more stable learning process

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

Attention-Related Brain Activation Is Altered in Older Adults With White Matter Hyperintensities Using Multi-Echo fMRI

Cognitive decline is often undetectable in the early stages of accelerated vascular aging. Attentional processes are particularly affected in older adults with white matter hyperintensities (WMH), although specific neurovascular mechanisms have not been elucidated. We aimed to identify differences in attention-related neurofunctional activation and behavior between adults with and without WMH. Older adults with moderate to severe WMH (n = 18, mean age = 70 years), age-matched adults (n = 28, mean age = 72), and healthy younger adults (n = 19, mean age = 25) performed a modified flanker task during multi-echo blood oxygenation level dependent functional magnetic resonance imaging. Task-related activation was assessed using a weighted-echo approach. Healthy older adults had more widespread response and higher amplitude of activation compared to WMH adults in fronto-temporal and parietal cortices. Activation associated with processing speed was absent in the WMH group, suggesting attention-related activation deficits that may be a consequence of cerebral small vessel disease. WMH adults had greater executive contrast activation in the precuneous and posterior cingulate gyrus compared to HYA, despite no performance benefits, reinforcing the network dysfunction theory in WMH

Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning