A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Learning from Complex Neuroimaging Datasets

Advancements in Magnetic Resonance Imaging (MRI) allowed for the early diagnosis of neurodevelopmental disorders and neurodegenerative diseases. Neuroanatomical abnormalities in the cerebral cortex are often investigated by examining group-level differences of brain morphometric measures extracted from highly-sampled cortical surfaces. However, group-level differences do not allow for individual-level outcome prediction critical for the application to clinical practice. Despite the success of MRI-based deep learning frameworks, critical issues have been identified: (1) extracting accurate and reliable local features from the cortical surface, (2) determining a parsimonious subset of cortical features for correct disease diagnosis, (3) learning directly from a non-Euclidean high-dimensional feature space, (4) improving the robustness of multi-task multi-modal models, and (5) identifying anomalies in imbalanced and heterogeneous settings. This dissertation describes novel methodological contributions to tackle the challenges above. First, I introduce a Laplacian-based method for quantifying local Extra-Axial Cerebrospinal Fluid (EA-CSF) from structural MRI. Next, I describe a deep learning approach for combining local EA-CSF with other morphometric cortical measures for early disease detection. Then, I propose a data-driven approach for extending convolutional learning to non-Euclidean manifolds such as cortical surfaces. I also present a unified framework for robust multi-task learning from imaging and non-imaging information. Finally, I propose a semi-supervised generative approach for the detection of samples from untrained classes in imbalanced and heterogeneous developmental datasets. The proposed methodological contributions are evaluated by applying them to the early detection of Autism Spectrum Disorder (ASD) in the first year of the infant’s life. Also, the aging human brain is examined in the context of studying different stages of Alzheimer’s Disease (AD).Doctor of Philosoph

CUSTOMERS LOYALTY: DOES VALUE CO-CREATION BECOME INDISPENSABLE FOR UNIVERSITIES?

This paper investigates the direct and indirect relationships between customers` participation in value co-creation activities (CPVCA) and their loyalty. Quantitative research approach is adopted, while the population consists of all the Lebanese private universities` students. A questionnaire was used to collect data from 403 students, nominated according to convenience sampling technique. The study proposed scale validity and the relationships between variables were examined depending on PLS-SEM. The findings reveal a direct significant relationship between CPVCA and customers` loyalty; in addition, to indirect relationship, through the partial mediating role for customers` satisfaction and relationship strength. Research implications and limitations are presented

A novel method for high-dimensional anatomical mapping of extra-axial cerebrospinal fluid: Application to the infant brain

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development

Oral versus rectal route of misoprostol administration: a randomized controlled trial

Objective: to compare between the efficacy of oral versus rectal misoprostol in the treatment of incomplete abortion. Design: prospective controlled trial. Patients were allocated to intervention using alternate sequence Setting: Al-Hussein Hospital, Al-Azhar University Materials and methods: one hundred women with retained products of conception were divided into two groups: G1: fifty women received misoprostol 200 μg misoprostol/4 hs rectally and G2; fifty women received it orally. Follow up and side effects were recorded Results: There was no significant difference between both groups regarding their background characteristics, response to misoprostol, need to do D&C or side effects. the dose of 200ug misoprostol every 4 hours (orally and rectally) for a maximum of 3 doses was not only effective in complete evacuation but also had low incidence of side-effects especially vomiting diarrhea and bleeding Conclusion: misoprostol whether by oral or rectal route seems to be an effective as a non invasive method for evacuation of the uterus in women with retained products of conception

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitatenon-invasive clinical diagnostics of various diseases and abnormalities since their inception in the1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employedto investigate facets of ASD in order to better understand this complex syndrome. This paperreviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensorimaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimescontradictory due to different age ranges, hardware protocols, population types, numbers of participants,and image analysis parameters. The primary anatomical structures, such as amygdalae,cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlightedthrough successive life stages, from infancy to adulthood. This survey demonstrates the absenceof consistent pathology in the brains of autistic children and lack of research investigations in patientsunder two years of age in the literature. The known publications also emphasize advancesin data acquisition and analysis, as well as significance of multimodal approaches that combineresting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTIshow good promise towards the early and non-invasive ASD diagnostics

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1–4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49–85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005–<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality