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Magnetic Resonance Imaging

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Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.

Neuroscience
This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and extracranial plaques, which is crucial for stroke treatment and prevention. We retrospectively analy...

Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.

Neuroradiology
PURPOSE: Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult to evaluate them with preoperative imaging. We developed a software that automatically generates th...

Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.

Medical image analysis
Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal ...

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

IEEE transactions on neural networks and learning systems
Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each indivi...

Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.

Sensors (Basel, Switzerland)
BACKGROUND: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and wh...

Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data ...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Scientific reports
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Translational psychiatry
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

European journal of radiology
In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance imaging (PMMR). PMCT is particularly useful for assessing skeletal injuries, vascular lesions, and est...