Technical note: A PBS source model based independent dose calculation frame on TOPAS MC.

Purpose: We endeavor to present a comprehensive methodology for establishing a versatile Monte Carlo (MC) dose calculation platform that operates independently of the treatment planning system (TPS, e.g., RayStation).

Virtual-simulation boosted neural network dose calculation engine for intensity-modulated radiation therapy.

The Monte Carlo (MC) dose calculation method is widely recognized as the gold standard for precision in dose calculation. However, MC calculations are computationally intensive and time-consuming. This study aims to develop a neural network-based dose calculation engine using a virtual simulation database, producing dose distributions with accuracy comparable to MC dose calculations.

An AI dose-influence matrix engine for robust pencil beam scanning protons therapy.

Background: Rapid planning is of tremendous value in proton pencil beam scanning (PBS) therapy in overcoming range uncertainty. However, the dose calculation of the dose influence matrix (D) in robust PBS plan optimization is time-consuming and requires substantial acceleration to enhance efficiency.

Purpose: To accelerate the D calculations in PBS therapy, we developed an AI-D engine integrated into our in-house treatment planning system (TPS).

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

Background: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients.

A feasibility study of dose-band prediction in radiation therapy: Predicting a spectrum of plan dose.

Purpose: The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called "dose-band prediction," which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes.

Three-dimensional dose prediction based on deep convolutional neural networks for brain cancer in CyberKnife: accurate beam modelling of homogeneous tissue.

Objectives: Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans.

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN-based image synthesis.

Background: Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR).

Commissioning dose computation model for proton source in pencil beam scanning therapy by convolution neural networks.

. Proton source model commissioning (PSMC) is critical for ensuring accurate dose calculation in pencil beam scanning (PBS) proton therapy using Monte Carlo (MC) simulations. PSMC aims to match the calculated dose to the delivered dose.

A generalization performance study on the boosting radiotherapy dose calculation engine based on super-resolution.

Purpose: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution-based deep learning model for fast and accurate dose prediction in clinical practice.

Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer.

Purpose: Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate.

Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection.

Purpose: Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models.

Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy.

Purpose: This study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).

Methods: A convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the "3D Dense-U-Net", which combines the U-Net and the Dense-Net.