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Phantoms, Imaging

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3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility.

Sensors (Basel, Switzerland)
This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorit...

Deep learning-based velocity antialiasing of 4D-flow MRI.

Magnetic resonance in medicine
PURPOSE: To develop a convolutional neural network (CNN) for the robust and fast correction of velocity aliasing in 4D-flow MRI.

Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis.

Physics in medicine and biology
4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic feat...

Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis.

Medical physics
BACKGROUND: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DB...

Improving Image Quality for Single-Angle Plane Wave Ultrasound Imaging With Convolutional Neural Network Beamformer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrafast ultrasound imaging based on plane wave (PW) compounding has been proposed for use in various clinical and preclinical applications, including shear wave imaging and super resolution blood flow imaging. Because the image quality afforded by ...

Robust Scatterer Number Density Segmentation of Ultrasound Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Quantitative ultrasound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, scatterer number density is an important property that can affect the es...

Cramér-Rao bound-informed training of neural networks for quantitative MRI.

Magnetic resonance in medicine
PURPOSE: To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters.

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...

Assessing radiomics feature stability with simulated CT acquisitions.

Scientific reports
Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis...

Artificial intelligence based deconvolving on megavoltage photon beam profiles for radiotherapy applications.

Physics in medicine and biology
. The aim of this work is an AI based approach to reduce the volume effect of ionization chambers used to measure high energy photon beams in radiotherapy. In particular for profile measurements, the air-filled volume leads to an inaccurate measureme...