AIMC Topic: Phantoms, Imaging

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The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.

Medicine May 14, 2021
The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconst...
Humans Algorithms Radiation Dosage Phantoms, Imaging Radiation Exposure Tomography, X-Ray Computed Deep Learning Abdomen
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Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder.

Science robotics Mar 17, 2021
Enzyme-powered nanomotors are an exciting technology for biomedical applications due to their ability to navigate within biological environments using endogenous fuels. However, limited studies into their collective behavior and demonstrations of tra...
Phantoms, Imaging Motion Administration, Intravesical Urinary Bladder Gold Metal Nanoparticles Positron Emission Tomography Computed Tomography Mice Precision Medicine Equipment Design Mice, Inbred C57BL Urease Female Robotics Translational Research, Biomedical Animals
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Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery.

Technology in cancer research & treatment Jan 1, 2021
The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a deep learning algorithm for Gamma Knife (GK) stereotactic radiosurgery (SRS). The Monte Carlo (MC) m...
Phantoms, Imaging Radiotherapy, Intensity-Modulated Radiosurgery Monte Carlo Method Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Retrospective Studies Tomography, X-Ray Computed Neoplasms Neural Networks, Computer Prognosis Humans
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CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Investigative radiology Jan 1, 2021
Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an ove...
Humans Artificial Intelligence Phantoms, Imaging Radiometry Tomography Scanners, X-Ray Computed Tomography, X-Ray Computed Radiation Dosage Monte Carlo Method
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Machine learning for direct oxygen saturation and hemoglobin concentration assessment using diffuse reflectance spectroscopy.

Journal of biomedical optics Nov 1, 2020
SIGNIFICANCE: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neu...
Machine Learning Oxygen Hemoglobins Spectrum Analysis Phantoms, Imaging
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3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Optics express Oct 26, 2020
Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and...
Imaging, Three-Dimensional Mice Catheters, Indwelling Equipment Design Microscopy, Fluorescence Neurons Neural Networks, Computer Cells, Cultured Animals Hippocampus Microspheres Neuroimaging Phantoms, Imaging Image Enhancement Cannula
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[Application of Convolutional Neural Network for Evaluating CT Dose Using Image Noise Classification: A Phantom Study].

Nihon Hoshasen Gijutsu Gakkai zasshi Jan 1, 2020
PURPOSE: It is well known that there is a trade-off relationship between image noise and exposure dose in X-ray computed tomography (CT) examination. Therefore, CT dose level was evaluated by using the CT image noise property. Although noise power sp...
Radiation Dosage Neural Networks, Computer Signal-To-Noise Ratio Tomography, X-Ray Computed Phantoms, Imaging
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[Use of artificial intelligence for image reconstruction].

Der Radiologe Jan 1, 2020
CLINICAL/METHODOLOGICAL PROBLEM: In the reconstruction of three-dimensional image data, artifacts that interfere with the appraisal often occur as a result of trying to minimize the dose or due to missing data. Used iterative reconstruction methods a...
Humans Artifacts Artificial Intelligence Phantoms, Imaging Algorithms
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Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics.

Academic radiology Jan 1, 2020
OBJECTIVES: Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into im...
Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Phantoms, Imaging Algorithms Tomography, X-Ray Computed Tomography Scanners, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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Evaluating medical images using deep convolutional neural networks: A simulated CT phantom image study.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2020
BACKGROUND: Applied research on artificial intelligence, mainly in deep learning, is widely performed. If medical images can be evaluated using artificial intelligence, this could substantially improve examination efficiency.
Image Processing, Computer-Assisted Phantoms, Imaging Artificial Intelligence Humans Machine Learning Neural Networks, Computer Tomography, X-Ray Computed
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