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Monte Carlo Method

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Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology
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...

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)
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 ...

A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network.

Physics in medicine and biology
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...

Partial-ring PET image restoration using a deep learning based method.

Physics in medicine and biology
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 ...

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps.

Medical image analysis
Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the leading causes of cancer-related deaths on a global scale. Early detection and prevention of colorectal cancer is primarily enabled through manual screen...

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

Physics in medicine and biology
A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, call...

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Medical physics
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 ...

Ensemble of neural networks for 3D position estimation in monolithic PET detectors.

Physics in medicine and biology
We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality...

Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging.

Physics in medicine and biology
Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which can improve the clinical accuracy of disease diagnosis and can also serve as a research tool for sc...