AIMC Topic: Magnetic Resonance Imaging

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Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.

Investigative radiology
OBJECTIVES: Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this stud...

Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.

European journal of nuclear medicine and molecular imaging
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...

A simple clustering approach to map the human brain's cortical semantic network organization during task.

NeuroImage
Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. Howev...

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Computers in biology and medicine
Brain tumours are a significant health concern, often resulting in severe cognitive and physiological impairments. Accurate detection and classification of brain tumours, including glioma, meningioma, and pituitary tumours, are crucial for effective ...

Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Magma (New York, N.Y.)
OBJECTIVE: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Neuroscience
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learning (ML) advancements have revolutionized its diagnosis and treatment approaches. Although, the rese...

Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.

BMC cancer
BACKGROUND: Bone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologis...

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Scientific reports
Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show tha...

Accelerating veterinary low field MRI acquisitions using the deep learning based denoising solution HawkAI.

Scientific reports
Magnetic resonance imaging (MRI) has changed veterinary diagnosis but its long-sequence time can be problematic, especially because animals need to be sedated during the exam. Unfortunately, shorter scan times implies a fall in overall image quality ...

FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification.

Brain research
Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors....