AIMC Topic: Contrast Media

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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery Jun 16, 2020
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...
Chemotherapy, Adjuvant Humans Breast Neoplasms Middle Aged Antineoplastic Agents Diagnosis, Computer-Assisted Neoadjuvant Therapy Contrast Media Magnetic Resonance Imaging Area Under Curve Deep Learning ROC Curve Image Processing, Computer-Assisted Treatment Outcome Neural Networks, Computer Retrospective Studies Female Adult
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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a m...
Proof of Concept Study Organometallic Compounds Recurrence Models, Cardiovascular Predictive Value of Tests Atrial Fibrillation Heart Rate Catheter Ablation Patient-Specific Modeling Pulmonary Veins Meglumine Male Middle Aged Risk Assessment Diagnosis, Computer-Assisted Treatment Outcome Contrast Media Magnetic Resonance Imaging Action Potentials Female Reproducibility of Results Risk Factors Aged Humans Machine Learning Retrospective Studies
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Deep learning promotes B-mode ultrasound screening for focal liver lesions.

EBioMedicine Jun 5, 2020
Deep Learning Ultrasonography Liver Neoplasms Contrast Media Humans
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Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

Neuroradiology Jun 4, 2020
PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.
Image Interpretation, Computer-Assisted Datasets as Topic Magnetic Resonance Imaging Humans Neurilemmoma Diagnosis, Differential Neural Networks, Computer Glioblastoma Brain Neoplasms Bayes Theorem Contrast Media
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Deep learning-based reconstruction of ultrasound images from raw channel data.

International journal of computer assisted radiology and surgery Jun 3, 2020
PURPOSE: We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic...
Reference Values Signal-To-Noise Ratio Phantoms, Imaging Ultrasonography Image Processing, Computer-Assisted Models, Theoretical Contrast Media Algorithms Humans Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning
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Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder.

Frontiers in bioscience (Landmark edition) Jun 1, 2020
Delineation of the bladder under a dynamic contrast enhanced (DCE)-MRI protocol requires robust segmentation. However, this method is subject to errors due to variations in the content of fluid within the bladder, as well as presence of air and simil...
Humans Contrast Media Magnetic Resonance Imaging Urinary Bladder Neural Networks, Computer Machine Learning Radiographic Image Enhancement Algorithms Reproducibility of Results
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Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network.

Journal of neurointerventional surgery May 29, 2020
BACKGROUND: Intracranial aneurysms (IAs) are common in the population and may cause death.
Deep Learning Time Factors Humans Cerebral Angiography Aged Neural Networks, Computer Intracranial Aneurysm Contrast Media Retrospective Studies Adult Angiography, Digital Subtraction Female Male Middle Aged
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Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

Magnetic resonance imaging May 16, 2020
OBJECTIVE: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.
Image Enhancement Signal-To-Noise Ratio Movement Deep Learning Contrast Media Humans Magnetic Resonance Imaging Brain Artifacts
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Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

International journal of radiation oncology, biology, physics May 14, 2020
PURPOSE: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.
Brain Neoplasms Deep Learning Contrast Media Magnetic Resonance Imaging Radiotherapy Skull Radiometry Neural Networks, Computer Brain Retrospective Studies Tomography, X-Ray Computed Humans
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Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

Osteoarthritis and cartilage May 11, 2020
OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).
Femur Osteoarthritis, Knee Imaging, Three-Dimensional Humans Cartilage, Articular Contrast Media Machine Learning X-Ray Microtomography Arthroplasty, Replacement, Knee Severity of Illness Index Tibia
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