1,721,044 research outputs found
Editorial for “Value of Dynamic Contrast Enhanced (DCE) MRI in Predicting Response to Foam Sclerotherapy of Venous Malformations”
No full textNo abstract is available for this article
Renal arteries and splanchnic vessels
No full textSteno-occlusive diseases of mesenteric and renal vessels depend on a great number of pathological conditions. The most common are: atherosclerosis, collagen vascular disease, vasculitis, fibromuscular dysplasia, trauma, and neoplastic encasement
Optic Chiasm Morphometric Changes in Multiple Sclerosis: Feasibility of a Simplified Brain Magnetic Resonance Imaging Measure of White Matter Atrophy
No full textSophisticated volume measurements of brain structures on magnetic resonance imaging (MRI) may improve specificity in determining long-term progression of multiple sclerosis (MS), but these techniques are laborious. The optic chiasm (OC) is a white matter (WM) structure clearly visible on a routine MRI and is related to the optic nerves (ONs), which are known to atrophy in MS. We hypothesized that OC morphometric measurements would show OC atrophy in MS compared to normal patients. If so, this could help establish a novel simplified brain MRI measure of WM atrophy in MS patients. We retrospectively evaluated standard brain MRIs of 97 patients with known MS and 98 normal individuals. We electronically measured eight OC morphometrics on axial T2WIs and midsagittal T1WIs: OC width and anteroposterior (AP) diameter, diameters of each ON and optic tract (OT), and angles between the ONs or OTs. Mean OC width, AP diameter, and height in MS patients were 11.83 ± 1.25 mm (95% CI 11.58–12.09), 2.99 ± 0.65 mm (95% CI 2.85–3.12), and 2.09 ± 0.37 mm (95% CI 2–2.19), respectively. In normal individuals, they were 12.1 ± 1.4 mm (95% CI 11.78–12.34), 3.43 ± 0.63 mm (95% CI 3.3–3.58), and 2.15 ± 0.37 mm (95% CI 2.07–2.23), respectively. There were statistically significant differences between MS patients and controls for AP diameter (P = 0.000), but not for width (P = 0.204) or height (P = 0.183). The ONs were significantly smaller in MS (P < 0.0017), but not the OTs. Thus, the OC is significantly atrophied in an unstratified cohort of MS patients. Future studies may establish an MRI OC morphometric index to evaluate demyelinating disease in the brain. Clin. Anat. 32:1072–1081, 2019. © 2019 Wiley Periodicals, Inc
Post-processing
No full textAt present, both CTA and MRA allow to obtain datasets formed by a large amount of bi-dimensional images acquired through a spatial axis (x, y, z); however, in consideration of the very thin intervals between contiguous partitions of each dataset, the acquisition as a whole can be considered a true volume formed by basic three-dimensional components (voxels) rather than a stack of independent slices with bi-dimensional properties
Advanced computational methods for oncological image analysis
Full text link: The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]
Imaging techniques in ALS
No full textAmyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by degeneration of both upper and lower motor neuron located in the spinal cord and brainstem. Diagnosis of ALS is predominantly clinical, nevertheless, electromyography and Magnetic Resonance Imaging (MRI) may provide support. Several advanced MRI techniques have been proven useful for ALS diagnosis and, indeed, the combination of different MRI techniques demonstrated an improvement in sensitivity and specificity as far as 90%. This review focus on the imaging techniques currently used in the diagnosis and management of ALS with brief considerations on future applications
Whole-body angiography
No full textDue to the multisegmental distribution of atherosclerotic disease, the traditional segmental diagnostic approach, limited to the evaluation of symptomatic vascular districts, is now considered inadequate for the classification of patients
A Survey
Full text linkAkinyelu, A. A., Zaccagna, F., Grist, J. T., Castelli, M., & Rundo, L. (2022). Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. Journal of Imaging, 8(8), 1-40. [205]. https://doi.org/10.3390/jimaging8080205 -------------- Funding: We gratefully acknowledge financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center—MagIC/NOVA IMS (UIDB/04152/2020).Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.publishersversionpublishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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