AIMC Topic: Magnetic Resonance Imaging

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DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

Scientific reports Sep 12, 2022
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the acc...
Deep Learning Magnetic Resonance Imaging Delivery of Health Care Brain Neoplasms Humans Artificial Intelligence Algorithms
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Region Convolutional Neural Network for Brain Tumor Segmentation.

Computational intelligence and neuroscience Sep 10, 2022
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close s...
Humans Brain Neoplasms Neural Networks, Computer Magnetic Resonance Imaging
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Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

European radiology experimental Sep 8, 2022
OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep l...
Machine Learning Deep Learning Humans Lipoma Magnetic Resonance Imaging Neoplasms, Adipose Tissue Soft Tissue Neoplasms
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Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning.

Physics in medicine and biology Sep 8, 2022
To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously.The proposed framework ...
Deep Learning Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Algorithms Brain
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Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation.

La Radiologia medica Sep 7, 2022
PURPOSE: To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time.
Liver Image Enhancement Echo-Planar Imaging Diffusion Magnetic Resonance Imaging Middle Aged Magnetic Resonance Imaging Aged Adult Humans Artificial Intelligence Female Reproducibility of Results Prospective Studies
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A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Sep 6, 2022
BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation and improve the poor prognosis for hepatocellular carcinoma (HCC). This study aimed to develop and va...
Carcinoma, Hepatocellular Magnetic Resonance Imaging Humans Retrospective Studies Deep Learning Liver Neoplasms Tomography, X-Ray Computed
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Multi-modality MRI for Alzheimer's disease detection using deep learning.

Physical and engineering sciences in medicine Sep 5, 2022
Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by ...
Diffusion Tensor Imaging Atrophy Magnetic Resonance Imaging Alzheimer Disease Humans Deep Learning
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A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Sep 5, 2022
INTRODUCTION: This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax.
Humans Tomography, X-Ray Computed Radiotherapy Dosage Thorax Radiotherapy, Image-Guided Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Lung Deep Learning
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BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.

Zeitschrift fur medizinische Physik Sep 2, 2022
INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial...
Deep Learning Image Processing, Computer-Assisted Algorithms Brain Head Magnetic Resonance Imaging Brain Mapping Humans
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Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.

Computer methods and programs in biomedicine Sep 2, 2022
BACKGROUND AND OBJECTIVE: Deep learning-based methods for fast target segmentation of magnetic resonance imaging (MRI) have become increasingly popular in recent years. Generally, the success of deep learning methods in medical image segmentation tas...
Humans Neural Networks, Computer Supervised Machine Learning Image Processing, Computer-Assisted Uncertainty Magnetic Resonance Imaging
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