Purpose Detailed and accurate absorbed dose calculations from radiation interactions with
the human body can be obtained with the Monte Carlo (MC) method. However, the MC …

Background and purpose Accurate calculation of the absorbed dose delivered to the tumor
and normal tissues improves treatment gain factor, which is the major advantage of …

In recent years, Artificial intelligence (AI), specifically deep-learning (DL) based methods,
have been employed extensively to solve various problems in brachytherapy. This paper …

Purpose Various dose calculation algorithms are available for radiation therapy for cancer
patients. However, these algorithms are faced with the tradeoff between efficiency and …

In radiotherapy, a trade‐off exists between computational workload/speed and dose
calculation accuracy. Calculation methods like pencil‐beam convolution can be much faster …

This study aims to develop a deep learning-based strategy for treatment plan check and
verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to …

Inverse planning is an essential tool for optimizing the delivered radiation dose on low-dose-
rate (LDR) prostate brachytherapy. Clinical inverse planning systems use the TG-43 dose …

We have previously described RapidBrachyMCTPS, a brachytherapy treatment planning
toolkit consisting of a graphical user interface (GUI) and a Geant4-based Monte Carlo (MC) …

Recently, artificial intelligence technologies and algorithms have become a major focus for
advancements in treatment planning for radiation therapy. As these are starting to become …

Deep DoseNet: a deep neural network for accurate dosimetric transformation between
different spatial resolutions and/or different dose calculation algorithms for precision radiation …