AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Phantoms, Imaging

Showing 461 to 470 of 748 articles

Clear Filters

3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation.

Ultrasound in medicine & biology
H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size esti...

A coordinate positioning puncture method under robot-assisted CT-guidance: phantom and animal experiments.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
PURPOSE: To evaluate the accuracy of the robot-assisted computed tomography (CT)-guided coordinate positioning puncture method by phantom and animal experiments.

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

Medical physics
PURPOSE: Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is a...

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

Medical physics
PURPOSE: To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

Neural networks-based regularization for large-scale medical image reconstruction.

Physics in medicine and biology
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs ha...

Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.

PloS one
The use of image covariates to build a classification model has lots of impact in various fields, such as computer science, medicine, and so on. The aim of this paper is to develop an estimation method for logistic regression model with image covaria...

Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
We study training deep neural network (DNN) frequency-domain beamformers using simulated and phantom anechoic cysts and compare to training with simulated point target responses. Using simulation, physical phantom, and in vivo scans, we find that tra...

Validating robotic couch isocentricity with 3D surface imaging.

Journal of applied clinical medical physics
BACKGROUND: A proton therapy system with 190° gantries uses robotic couch rotations to change the treatment beam laterality. Couch rotations are typically validated clinically with post-rotation radiographic imaging.

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging.

Biomedical physics & engineering express
The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, hig...

Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning.

Computational and mathematical methods in medicine
This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims...