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Monte Carlo Method

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Refining the rheological characteristics of high drug loading ointment via SDS and machine learning.

PloS one
This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jia...

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm.

Journal of X-ray science and technology
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Artificial intelligence in medicine
Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which ar...

Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

Sensors (Basel, Switzerland)
This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strate...

Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment.

Physics in medicine and biology
Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computa...

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

Physics in medicine and biology
In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).The approach relies on transmission measurements, and the estimated spectru...

Deep learning based linear energy transfer calculation for proton therapy.

Physics in medicine and biology
This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for a...

A deep-learning-based scatter correction with water equivalent path length map for digital radiography.

Radiological physics and technology
We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed...

A Deep-Learning-Based Partial-Volume Correction Method for Quantitative Lu SPECT/CT Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Es...