Veronica Lily Ravano

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Objective: Pathology in multiple sclerosis is not homogenously distributed. Recently, it has been shown that structures adjacent to CSF are more severely affected. A gradient of brain tissue involvement was shown with more severe pathology in periventricul ...

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

BACKGROUND: Diffusion-weighted magnetic resonance imaging is essential for diagnosing ischemic stroke and identifying targets for emergency revascularization. Apparent diffusion coefficient (ADC) maps derived from diffusion-weighted magnetic resonance imag ...

Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data heterogeneity. In particular, magnetic resonance images depend on a large number of acquisition protocol parameters ...

Background: Early diagnosis and treatment of patients with multiple sclerosis (MS) are associated with better outcomes; however, diagnostic delays remain a major problem.Objective: Describe the prevalence, determinants and consequences of delayed diagnoses ...

Objectives Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize micro ...

Determining the penumbra, i.e., the at-risk but salvageable tissue, is crucial in the context of acute ischemic stroke imaging. Deep learning methods performing segmentation from perfusion parameter maps have shown promise in this regard. However, these me ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Federated learning allows for training deep learning models from various sources (e.g., hospitals) without sharing patient information, but only the model weights. Two central problems arise when sending the updated weights to the central node in a federat ...

Veronica Lily Ravano

Chattez avec Graph Search

Periventricular gradient of T1 tissue alterations in multiple sclerosis

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Diffusion Imaging Protocol Heterogeneity Biases Ischemic Core Volume, Location, and Clinical Associations in Acute Stroke

Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency

Diagnostic delay of multiple sclerosis: prevalence, determinants and consequences

Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis

Deep Learning for Ischemic Penumbra Segmentation from MR Perfusion Maps: Robustness to the Deconvolution Algorithm

Domain shift, domain adaptation, and generalization: A focus on MRI

Weighting Schemes for Federated Learning in Heterogeneous and Imbalanced Segmentation Datasets

Towards better characterization of spinal cord pathology in early stages of multiple sclerosis using T1 mapping

First evidence on the utility of T-1 mapping of the optic nerve to investigate differences between MS and NMOSD

Diagnostic delay of multiple sclerosis: frequency, reasons, and consequences

Pathology of normal-appearing white matter assessed by T1 and T2 single-voxel z-scores correlates with cognition in multiple sclerosis

Perilesional tissue exhibits different T1 gradients depending on multiple sclerosis subtypes

Spinal cord microstructural abnormalities on quantitative T1 mapping predict short-term spinal cord atrophy in early multiple sclerosis