AIMC Topic: Protons

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High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.

Biomedical physics & engineering express Apr 27, 2020
Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-...
Computer Simulation Reproducibility of Results Neoplasms Deep Learning Humans Neural Networks, Computer Algorithms Scattering, Radiation Phantoms, Imaging Proton Therapy Protons Image Processing, Computer-Assisted Nonlinear Dynamics Signal-To-Noise Ratio Radiotherapy
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A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation 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) Dec 20, 2019
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...
Protons Tomography, X-Ray Computed Reproducibility of Results Deep Learning Image Processing, Computer-Assisted Monte Carlo Method Phantoms, Imaging Proton Therapy Gamma Rays Neural Networks, Computer Algorithms Brain Computer Simulation Humans
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Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Medical physics Oct 10, 2019
PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution ...
Machine Learning Electrons Radiation Dosage Radiotherapy Dosage Monte Carlo Method Proton Therapy Protons
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Fast and accurate reconstruction of human lung gas MRI with deep learning.

Magnetic resonance in medicine Jul 19, 2019
PURPOSE: To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning.
Healthy Volunteers Smoking Respiration Signal-To-Noise Ratio Inflammation Magnetic Resonance Imaging Solitary Pulmonary Nodule Tuberculosis, Pulmonary Bronchiectasis Fourier Analysis Protons Xenon Isotopes Pulmonary Disease, Chronic Obstructive Asthma Male Middle Aged Lung Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Reproducibility of Results Aged Young Adult Adult Humans Algorithms Prospective Studies Retrospective Studies Female
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Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.

Magnetic resonance in medicine Mar 12, 2019
PURPOSE: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy ( H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broaden...
Magnetic Resonance Imaging Amino Acids gamma-Aminobutyric Acid Brain Chemistry Young Adult Phantoms, Imaging Proton Magnetic Resonance Spectroscopy Humans Male Protons Brain Deep Learning Image Processing, Computer-Assisted
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Understanding of proton induced synaptic behaviors in three-terminal synapse device for neuromorphic systems.

Nanotechnology Feb 28, 2019
In this study, we investigate a proton-based three-terminal (3-T) synapse device to realize linear weight-update and I-V linearity characteristics for neuromorphic systems. The conductance states of the 3-T synapse device can be controlled by modulat...
Neural Networks, Computer Models, Neurological Silicon Dioxide Synapses Linear Models Protons
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Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification.

Academic radiology Sep 6, 2018
RATIONALE AND OBJECTIVES: We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational eff...
Magnetic Resonance Imaging Datasets as Topic Pulmonary Ventilation Protons Humans Computer Simulation Lung Deep Learning
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine May 17, 2018
PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to asse...
Magnetic Resonance Imaging Databases, Factual Models, Statistical Female Deep Learning Young Adult Humans Aged Signal-To-Noise Ratio Adolescent Adult Algorithms Protons Aged, 80 and over Image Processing, Computer-Assisted Male Middle Aged Contrast Media
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Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Oct 30, 2017
Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone ...
Protons Tomography, Spiral Computed Tomography, X-Ray Computed Multimodal Imaging Humans Deep Learning Pelvis Neural Networks, Computer Image Processing, Computer-Assisted Radiopharmaceuticals Magnetic Resonance Imaging Positron-Emission Tomography
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Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

Physical chemistry chemical physics : PCCP Dec 1, 2014
Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamic...
Thermodynamics Neural Networks, Computer Protons Water Molecular Dynamics Simulation Hydrogen Bonding
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