AIMC Topic:
Magnetic Resonance Imaging
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Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.
European radiology
Jul 1, 2023
OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT.
Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.
Radiation oncology (London, England)
Jul 1, 2023
PURPOSE: This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.
A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.
European radiology
Jun 30, 2023
OBJECTIVE: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention w...
SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences.
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have...
Two-Stage Self-Supervised Cycle-Consistency Transformer Network for Reducing Slice Gap in MR Images.
IEEE journal of biomedical and health informatics
Jun 30, 2023
Magnetic resonance (MR) images are usually acquired with large slice gap in clinical practice, i.e., low resolution (LR) along the through-plane direction. It is feasible to reduce the slice gap and reconstruct high-resolution (HR) images with the de...
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.
IEEE journal of biomedical and health informatics
Jun 30, 2023
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of th...
An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation.
IEEE journal of biomedical and health informatics
Jun 30, 2023
Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardiovascular diseases and in 3D modeling of the heart. Deep convolutional neural networks have been demonstrated to achieve state-of-the-art performance i...
Federated Learning of Generative Image Priors for MRI Reconstruction.
IEEE transactions on medical imaging
Jun 30, 2023
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling ...
Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study.
European radiology
Jun 29, 2023
OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans.
Global attention-enabled texture enhancement network for MR image reconstruction.
Magnetic resonance in medicine
Jun 29, 2023
PURPOSE: Although recent convolutional neural network (CNN) methodologies have shown promising results in fast MR imaging, there is still a desire to explore how they can be used to learn the frequency characteristics of multicontrast images and reco...
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