AIMC Topic: Contrast Media
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Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.
Radiology
Mar 30, 2021
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bo...
Editorial for "Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach".
Journal of magnetic resonance imaging : JMRI
Mar 29, 2021
Editorial For "Reduction of Contrast Agent Dose in Cardiovascular MR Angiography Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
Mar 26, 2021
Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.
European radiology experimental
Mar 25, 2021
After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scal...
Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.
European radiology
Mar 18, 2021
OBJECTIVES: To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using...
Deep learning-based methods may minimize GBCA dosage in brain MRI.
European radiology
Mar 18, 2021
OBJECTIVES: To evaluate the clinical performance of a deep learning (DL)-based method for brain MRI exams with reduced gadolinium-based contrast agent (GBCA) dose to provide better understanding of the readiness and limitations of this method.
Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.
Radiology
Mar 9, 2021
Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for ...
Super-Resolution Ultrasound Localization Microscopy Through Deep Learning.
IEEE transactions on medical imaging
Mar 2, 2021
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with signi...
Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic.
BMC medical imaging
Feb 27, 2021
BACKGROUND: Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to va...
Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI.
Journal of magnetic resonance imaging : JMRI
Feb 25, 2021
BACKGROUND: Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications.
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