AIMC Topic: Elasticity Imaging Techniques

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A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.

Thyroid : official journal of the American Thyroid Association Sep 9, 2020
The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic perf...
Male Middle Aged China Adolescent Aged Aged, 80 and over Young Adult Adult Humans Retrospective Studies Machine Learning Thyroid Neoplasms Clinical Decision-Making Ultrasonography Female Thyroid Nodule Reproducibility of Results Predictive Value of Tests Biopsy, Fine-Needle Unnecessary Procedures Elasticity Imaging Techniques Tumor Burden Image Interpretation, Computer-Assisted
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Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.

European radiology Apr 8, 2020
OBJECTIVES: To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification...
Magnetic Resonance Imaging Liver Biopsy Elasticity Imaging Techniques Humans Machine Learning Prospective Studies Female Male Middle Aged ROC Curve Liver Cirrhosis
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Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging.

Physics in medicine and biology Mar 20, 2020
We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach re...
Phantoms, Imaging Humans Finite Element Analysis Neural Networks, Computer Elastic Modulus Machine Learning Elasticity Imaging Techniques Imaging, Three-Dimensional
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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Nature communications Mar 6, 2020
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Her...
Humans Prospective Studies Prognosis Female Middle Aged Deep Learning Ultrasonography ROC Curve Image Processing, Computer-Assisted Aged Aged, 80 and over Adult Breast Neoplasms Preoperative Period Elasticity Imaging Techniques Neoplasm Staging Breast Mastectomy Reference Standards Lymph Nodes Lymphatic Metastasis Lymph Node Excision Axilla
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Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility.

Ultrasonic imaging Jan 30, 2020
Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter r...
Reproducibility of Results Ultrasonography, Mammary Breast Feasibility Studies Image Interpretation, Computer-Assisted Phantoms, Imaging Elasticity Imaging Techniques Humans Neural Networks, Computer Computer Simulation Female
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Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

European radiology Jan 21, 2020
OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.
Humans Male Middle Aged ROC Curve Liver Cirrhosis Hepatitis B, Chronic Data Accuracy Elasticity Imaging Techniques Liver Area Under Curve Retrospective Studies Female Aged Adult Neural Networks, Computer Machine Learning Algorithms Biomarkers
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A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

Ultrasound in medicine & biology Oct 11, 2019
The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different...
Male Middle Aged Liver Cirrhosis Elasticity Imaging Techniques Female Aged Aged, 80 and over Young Adult Adult Humans Machine Learning Retrospective Studies
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Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.

European radiology Sep 2, 2019
OBJECTIVES: The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.
Ultrasonography ROC Curve Liver Cirrhosis Radiologists Male Humans Middle Aged Deep Learning Algorithms Clinical Competence Retrospective Studies Female Reproducibility of Results Elasticity Imaging Techniques
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A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound.

IEEE transactions on bio-medical engineering Jul 25, 2019
OBJECTIVE: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for things such as, cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state-of-the-art shear wave imaging techniques...
Phantoms, Imaging Elasticity Imaging Techniques Humans Deep Learning Ultrasonography Sound
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Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Medical image analysis Jul 19, 2019
Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from th...
Image Interpretation, Computer-Assisted Data Compression Elasticity Imaging Techniques Humans Neural Networks, Computer
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