AIMC Topic:
Magnetic Resonance Imaging
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A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis.
Medical physics
Dec 3, 2023
BACKGROUND: Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention.
Deep-learning-based image quality enhancement of CT-like MR imaging in patients with suspected traumatic shoulder injury.
European journal of radiology
Dec 2, 2023
PURPOSE: To evaluate the diagnostic performance of CT-like MR images reconstructed with an algorithm combining compressed sense (CS) with deep learning (DL) in patients with suspected osseous shoulder injury compared to conventional CS-reconstructed ...
A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.
Neuroinformatics
Dec 2, 2023
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble metho...
Deep learning based synthesis of MRI, CT and PET: Review and analysis.
Medical image analysis
Dec 1, 2023
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in es...
Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.
IEEE transactions on medical imaging
Nov 30, 2023
Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors ba...
Stable Deep MRI Reconstruction Using Generative Priors.
IEEE transactions on medical imaging
Nov 30, 2023
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we addres...
Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.
Journal of magnetic resonance imaging : JMRI
Nov 29, 2023
BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored.
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.
Magma (New York, N.Y.)
Nov 29, 2023
OBJECTIVE: The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net m...
CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 29, 2023
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendor...
Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom.
BMC medical imaging
Nov 29, 2023
BACKGROUND: In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aimed to de...
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