AIMC Topic: Magnetic Resonance Imaging
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Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears?
PloS one
Oct 10, 2022
BACKGROUND/PURPOSE: The use of MRI as a diagnostic tool has gained popularity in the field of orthopedics. Although 3-dimensional (3D) MRI offers more intuitive visualization and can better facilitate treatment planning than 2-dimensional (2D) MRI, m...
Editorial for "Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI".
Journal of magnetic resonance imaging : JMRI
Oct 8, 2022
Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images.
Journal of magnetic resonance imaging : JMRI
Oct 8, 2022
BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.
A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.
Sensors (Basel, Switzerland)
Oct 6, 2022
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, an...
Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.
Nature communications
Oct 6, 2022
Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanne...
A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.
Physiological measurement
Oct 6, 2022
Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are l...
Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.
Magnetic resonance in medicine
Oct 5, 2022
PURPOSE: To investigate the acceleration of 4D-flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase.
Interpretable deep learning-based hippocampal sclerosis classification.
Epilepsia open
Oct 5, 2022
OBJECTIVE: To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction.
Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.
BMJ open
Oct 5, 2022
INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for ...
Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an e...
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