AIMC Topic: Monte Carlo Method

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A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

Medical physics Apr 3, 2020
PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous...
Tomography, X-Ray Computed Deep Learning Monte Carlo Method Phantoms, Imaging Humans Prospective Studies
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A deep learning approach to radiation dose estimation.

Physics in medicine and biology Feb 4, 2020
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the ra...
Monte Carlo Method Radioisotopes Humans Tomography, X-Ray Computed Tissue Distribution Neoplasms Neural Networks, Computer Glutamate Carboxypeptidase II Reproducibility of Results Lutetium Deep Learning Algorithms Radiopharmaceuticals Organs at Risk Radiation Dosage
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Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning.

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) Jan 30, 2020
PURPOSE: This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Particle Accelerators Monte Carlo Method X-Rays Equipment Design Models, Biological Computer Simulation Humans Tomography, X-Ray Computed Machine Learning
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Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

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) Jan 25, 2020
In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of distant metastasis. However, multi-class classification problems also have great clinical relevance, e.g...
Technology Assessment, Biomedical Monte Carlo Method Radiation Injuries Radiation Exposure Models, Theoretical Humans Area Under Curve Radiation Oncology Machine Learning Biomarkers Risk Factors
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation Jan 16, 2020
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Mutation Molecular Dynamics Simulation HIV-1 Ligands Humans Machine Learning Protein Binding Hydrogen Bonding Drug Resistance, Viral Static Electricity HIV Protease Monte Carlo Method HIV Protease Inhibitors
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Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.

Computer methods and programs in biomedicine Jan 11, 2020
BACKGROUND AND OBJECTIVE: Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflo...
Magnetic Resonance Imaging Knee Joint Supervised Machine Learning Monte Carlo Method Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans
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Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology Dec 30, 2019
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...
Metabolic Engineering Monte Carlo Method Reinforcement, Psychology Enzymes Models, Biological Markov Chains Synthetic Biology Artificial Intelligence Algorithms Software Metabolic Networks and Pathways
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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 ...
Humans Brain Proton Therapy Image Processing, Computer-Assisted Computer Simulation Tomography, X-Ray Computed Gamma Rays Monte Carlo Method Reproducibility of Results Neural Networks, Computer Protons Phantoms, Imaging Deep Learning Algorithms
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A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network.

Physics in medicine and biology Dec 19, 2019
Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT re...
Neural Networks, Computer Animals Monte Carlo Method Neoplasms, Experimental Mice Luminescence Tomography, Optical
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Partial-ring PET image restoration using a deep learning based method.

Physics in medicine and biology Nov 21, 2019
PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance ...
Monte Carlo Method Humans Image Processing, Computer-Assisted Phantoms, Imaging Brain Positron-Emission Tomography Deep Learning Artifacts
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