AIMC Topic: Magnetic Resonance Imaging

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Unsupervised single-image super-resolution for infant brain MRI.

NeuroImage
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for ex...

Significance of Papillary and Trabecular Muscular Volume in Right Ventricular Volumetry with Cardiac MR Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Pulmonary valve regurgitation after repaired Tetralogy of Fallot (TOF) or double-outlet right ventricle (DORV) causes hypertrophy and papillary muscle enlargement. Cardiac magnetic resonance imaging (CMR) can evaluate the right ventricular (...

BioTransX: A novel bi-former based hybrid model with bi-level routing attention for brain tumor classification with explainable insights.

Computers in biology and medicine
Brain tumors, known for their life-threatening implications, underscore the urgency of precise and interpretable early detection. Expertise remains essential for accurate identification through MRI scans due to the intricacies involved. However, the ...

Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.

Computers in biology and medicine
BACKGROUND: Automated whole-brain delineation (WBD) techniques often struggle to generalize across pre-clinical studies due to variations in animal models, magnetic resonance imaging (MRI) scanners, and tissue contrasts. We developed a 3D U-Net neura...

Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.

European radiology experimental
BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...

Deep learning for differential diagnosis of parotid tumors based on 2.5D magnetic resonance imaging.

Annals of medicine
PURPOSE: Accurate preoperative diagnosis of parotid gland tumors (PGTs) is crucial for surgical planning since malignant tumors require more extensive excision. Though fine-needle aspiration biopsy is the diagnostic gold standard, its sensitivity in ...

Gene expression profiles associated with gray matter and dynamic connectivity disruptions in major depressive disorder.

Journal of affective disorders
PURPOSE: To identify biomarkers linking molecular mechanisms to macroscale brain changes in major depressive disorder (MDD) by integrating multimodal neuroimaging, transcriptomics, and machine learning.

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data.

Journal of affective disorders
BACKGROUND: Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data...

Image-based AI tools in peripheral nerves assessment: Current status and integration strategies - A narrative review.

European journal of radiology
Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity...

Shared and distinct neural signatures of feature and spatial attention.

NeuroImage
The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent act...