Quantitative MRI in leukodystrophies

Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies

Качественная и количественная оценка проводящих путей с помощью диффузионно- тензорной магнитно-резонансной томографии у детей с церебральным инсультом

Objective: to analysis of quantitative and qualitative changes in MR tractography in the diagnosis of structural brain damage in children with stroke.Materials and methods. We examined 55 children with stroke in different periods between the ages of birth to 6 years (mean age 5.99 ± 1.67 months; Me = 2.0 months.). All patients underwent DTI evaluation by measuring the fractional anisotropy (FA) and apparent diffusion coefficient (ACD).Results. During the assessment of the FA in the affected and healthy side of the brain we have revealed a statistically significant (p < 0.001) decrease of indicators of FA and increase of MCD along the corticospinal tracts and at all levels of its passage. In the area of cystic degeneration the value of FA was significantly lower (0.05 ± 0.02) than in the area of gliosis (0.15 ± 0.03). In the area of cystic degeneration ACD was within 2.91 ± 0.44 • 10–3 mm2/s, and in the area of gliosis – 1.49 ± 0.27 • 10–3 mm2/s. Comparison of ACD values depending on the motor deficits showed statistically significant differences between patients with monoparesis, hemiparesis and tetraparesis (p < 0.029).Conclusion. Diffusion-tensor magnetic resonance imaging possible for not only to evaluate the available quantitative and qualitative changes brain pathways in different periods of stroke in children, but also it can predict the growth of motor deficits (in this case fractional anisotropy more sensitive indicator which significantly correlated with functional outcome (p < 0.05) in children with stroke).Цель исследования: анализ количественных и ка чественных изменений при МР-трактографии в диагностике структурных повреждений головного мозга у детей с инсультом.Материал и методы. Обследовано 55 детей с острым нарушением мозгового кровообращения в различных периодах в возрасте от рождения до 6 лет (средний возраст 5,99 ± 1,67 мес; Ме = 2,0 мес). Всем пациентам проводили МР+трактографическое исследование с измерением фракциональной анизотропии (ФА) и измеряемого коэффициента диффузии (ИКД).Результаты. При проведении оценки ФА в пораженной и здоровой стороне головного мозга выявлено статистически значимое (p < 0,001) снижение показателей ФА и повышение ИКД по ходу кортикоспинальных трактов на всех уровнях его прохождения. В зоне кистозной дегенерации значения ФА были значительно низкими (0,05 ± 0,02), чем в зоне глиоза (0,15 ± 0,03). ИКД в зоне кистозной дегенерации находился в пределах 2,91 ± 0,44 • 10–3 мм2/с, а в зоне глиоза – 1,49 ± ± 0,27 • 10–3 мм2/с. При сравнении значений ИКД в зависимости от моторного дефицита были выявлены статистически значимые различия между пациентами с монопарезом, гемипарезом и тетрапарезом (p < 0,029).Заключение. Диффузионно-тензорная магнитно-резонансная томография позволяет не только оценивать имеющиеся количественные и качественные изменения проводящих путей головного мозга в различных периодах инсульта у детей, но и прогнозировать нарастание моторного дефицита (при этом наиболее чувствительным является показатель ФА, достоверно коррелирующий с функциональными исходами (p < 0,05) у детей с инсультами)

Current State-of-the-Art of AI Methods Applied to MRI

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe

EXplainable Artificial Intelligence: enabling AI in neurosciences and beyond

The adoption of AI models in medicine and neurosciences has the potential to play a significant role not only in bringing scientific advancements but also in clinical decision-making. However, concerns mounts due to the eventual biases AI could have which could result in far-reaching consequences particularly in a critical field like biomedicine. It is challenging to achieve usable intelligence because not only it is fundamental to learn from prior data, extract knowledge and guarantee generalization capabilities, but also to disentangle the underlying explanatory factors in order to deeply understand the variables leading to the final decisions. There hence has been a call for approaches to open the AI `black box' to increase trust and reliability on the decision-making capabilities of AI algorithms. Such approaches are commonly referred to as XAI and are starting to be applied in medical fields even if not yet fully exploited. With this thesis we aim at contributing to enabling the use of AI in medicine and neurosciences by taking two fundamental steps: (i) practically pervade AI models with XAI (ii) Strongly validate XAI models. The first step was achieved on one hand by focusing on XAI taxonomy and proposing some guidelines specific for the AI and XAI applications in the neuroscience domain. On the other hand, we faced concrete issues proposing XAI solutions to decode the brain modulations in neurodegeneration relying on the morphological, microstructural and functional changes occurring at different disease stages as well as their connections with the genotype substrate. The second step was as well achieved by firstly defining four attributes related to XAI validation, namely stability, consistency, understandability and plausibility. Each attribute refers to a different aspect of XAI ranging from the assessment of explanations stability across different XAI methods, or highly collinear inputs, to the alignment of the obtained explanations with the state-of-the-art literature. We then proposed different validation techniques aiming at practically fulfilling such requirements. With this thesis, we contributed to the advancement of the research into XAI aiming at increasing awareness and critical use of AI methods opening the way to real-life applications enabling the development of personalized medicine and treatment by taking a data-driven and objective approach to healthcare

Three-dimensional Quantitative Magnetic Resonance Imaging of Carotid Atherosclerotic Plaque

Stroke is one of the leading causes of death and disability worldwide with 20% of ischemic strokes attributed to carotid atherosclerosis. In recent years, morphological characteristics of atherosclerotic plaque such as a thin fibrous cap, large lipid-rich necrotic core, intraplaque haemorrhage and ulceration have shown correlations with subsequent clinical events. High resolution, multi-contrast magnetic resonance imaging (MRI) can qualitatively identify these features and monitor disease progression. Compared to traditional contrast weighted imaging, quantitative MRI could provide an objective assessment of disease. Therefore, the general hypothesis investigated in this thesis is: Quantitative MRI methods can be used to acquire objective biomarkers of carotid vessel wall and atherosclerotic plaque, with high accuracy and good repeatability. The research presented in this thesis describes the use of multiple quantitative MRI methods to evaluate the carotid vessel wall. These include dynamic contrast-enhanced (DCE) MRI analysis for the assessment of plaque inflammation/neovascularization and the development of black-blood quantitative T2/T2* mapping sequences for plaque component characterisation. The acceleration of the sequences was also investigated using a combination of compressed sensing (CS) and parallel imaging (PI). Chapter 3 investigated the hypothesis that plaque functional characteristics and surface morphology can be evaluated using a high temporal and spatial resolution 4D contrast-enhanced MRI/MR angiography (MRA) sequence. Chapter 4 tested the hypothesis that magnetisation prepared 3D fast-spin-echo (FSE) is the best sequence for in vivo T2 mapping. Four different black-blood T2 mapping sequences were developed and compared in phantom and volunteers. Chapter 5 tested the hypothesis that the optimised iMSDE 3D FSE T2 mapping sequence can be combined with CS and PI to further reduce the acquisition time without significantly affecting image quality and the measured T2 relaxation times. Chapter 6 investigated the hypothesis that compressed sensing can be used to reduce the overall examination time of a comprehensive multi-contrast MRI protocol, comprising black-blood T1 weighted, T2 weighted and proton density weighted sequences. Finally, Chapter 7 investigated the hypothesis that accurate 3D vessel wall R2* mapping can be achieved through black-blood preparation. In summary, this thesis investigated the use of multiple quantitative MRI methods in evaluating the carotid vessel wall and atherosclerotic plaque. The results demonstrate that quantitative MRI is an accurate and reproducible method for the carotid plaque characterization

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models