Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient tr...
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different source...
Seminars in musculoskeletal radiology
Sep 29, 2020
Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parame...
Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission tomography (PET). This work aimed to investigate the feasibility of noise reduction for trac...
PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal melanoma (UM) and other intraocular masses.
Neural networks : the official journal of the International Neural Network Society
Aug 18, 2020
Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a magnetic field. Given MRI's non-invasive advantage in repeated imaging, the low-contrast MR images in the target area make segmentation of tissue a challenging pr...
Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To d...
Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach t...
OBJECTIVE: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.
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