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Phantoms, Imaging

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Artificial intelligence for quality assurance in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...

Independent verification of brachytherapy treatment plan by using deep learning inference modeling.

Physics in medicine and biology
This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodo...

A deep learning approach to gold nanoparticle quantification in computed tomography.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
INTRODUCTION: Deep learning (DL) is used to classify, detect, and quantify gold nanoparticles (AuNPs) in a human-sized phantom with a clinical MDCT scanner.

Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation.

NeuroImage
Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist...

Development of retake support system for lateral knee radiographs by using deep convolutional neural network.

Radiography (London, England : 1995)
INTRODUCTION: Lateral radiography of the knee joint is frequently performed; however, the retake rate is high owing to positioning errors. Therefore, in this study, to reduce the required number and time of image retakes, we developed a system that c...

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction.

European journal of radiology
OBJECTIVES: To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.

Deep learning-based coronary artery motion estimation and compensation for short-scan cardiac CT.

Medical physics
PURPOSE: During a typical cardiac short scan, the heart can move several millimeters. As a result, the corresponding CT reconstructions may be corrupted by motion artifacts. Especially the assessment of small structures, such as the coronary arteries...

Integrating robot-assisted ultrasound tracking and 3D needle shape prediction for real-time tracking of the needle tip in needle steering procedures.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Needle insertions have been used in several minimally invasive procedures for diagnostic and therapeutic purposes. Real-time position of the needle tip is an important information in needle steering systems.

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the...