AIMC Topic: Magnetic Resonance Imaging

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High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

Medical physics
BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) curren...

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

Magnetic resonance in medicine
PURPOSE: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Magnetic resonance in medicine
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unl...

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

Magnetic resonance in medicine
PURPOSE: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction.

CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI da...

Magnetic Resonance Imaging Images Based Brain Tumor Extraction, Segmentation and Detection Using Convolutional Neural Network and VGC 16 Model.

American journal of clinical oncology
OBJECTIVES: In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses an...

Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies.

European journal of radiology
PURPOSE: With the slice thickness routinely used in elbow MRI, small or subtle lesions may be overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm MRI) with deep learning reconstruction (DLR) to 3 mm slice thicknes...

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BMC medical imaging
BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced...

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.

Magnetic resonance imaging
BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR...