Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.

Background: At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality.

Methods: A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method.

Machine learning-based ensemble prediction model for the gamma passing rate of VMAT-SBRT plan.

Purpose: The purpose of this study was to accurately predict or classify the beam GPR with an ensemble model by using machine learning for SBRT(VMAT) plans.

Methods: A total of 128 SBRT VMAT plans with 330 arc beams were retrospectively selected, and 216 radiomics and 34 plan complexity features were calculated for each arc beam. Three models for GPR prediction and classification using support vector machine algorithm were as follows: (1) plan complexity feature-based model (plan model); (2) radiomics feature-based model (radiomics model); and (3) an ensemble model combining the two models (ensemble model).

The performance of a new type accelerator uRT-linac 506c evaluated by a quality assurance automation system.

Purpose: The purpose of this study was to evaluate the performance of our quality assurance (QA) automation system and to evaluate the machine performance of a new type linear accelerator uRT-linac 506c within 6 months using this system.

Methods: This QA automation system consists of a hollow cylindrical phantom with 18 steel balls in the phantom surface and an analysis software to process electronic portal imaging device (EPID) measurement image data and report the results. The performance of the QA automation system was evaluated by the tests of repeatability, archivable precision, detectability of introduced errors, and the impact of set-up errors on QA results.

A predicted three-dimensional dose sequence based treatment planning optimization method for gynecologic IMRT.

In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT.

Background: Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.

Methods: We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T-weight (T) image for MRIgRT synthetic CT (sCT) image generation.

Cross-engine transformation-based fast dose calculation for MRI-Linac online treatment planning.

Purpose: To propose a novel magnetic field dose calculation method based on transformation from pencil beam (PB) to Monte Carlo (MC) distribution for MRI-Linac online treatment planning.

Methods: The novel magnetic field dose calculation algorithm was established by a PB dose engine and a magnetic field with tissue inhomogeneity influence correction network. The correction network was constructed with a Res-UNet framework, including residual modules and an encoding-decoding path, by inputting three-dimensional PB dose and patient electron density map, and outputting transformed dose distribution.

TransDose: a transformer-based UNet model for fast and accurate dose calculation for MR-LINACs.

. To present a transformer-based UNet model (TransDose) for fast and accurate dose calculation for magnetic resonance-linear accelerators (MR-LINACs)..

Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields.

To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields.A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database.

Dosimetric Accuracy of MR-Guided Online Adaptive Planning for Nasopharyngeal Carcinoma Radiotherapy on 1.5 T MR-Linac.

Purpose: The aim of this study is to evaluate the dose accuracy of bulk relative electron density (rED) approach for application in 1.5 T MR-Linac and assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for nasopharyngeal carcinoma (NPC) patients.

Methods: Ten NPC patients formerly treated on conventional linac were included in this study, with their original planning CT and MRI collected.

Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy.

Background: Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time.

Methods: In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy.

Multisequence MR-generated sCT is promising for HNC MR-only RT: A comprehensive evaluation of previously developed sCT generation networks.

Purpose: To verify the feasibility of our in-house developed multisequence magnetic resonance (MR)-generated synthetic computed tomography (sCT) for accurate dose calculation and fractional positioning for head and neck MR-only radiation therapy (RT).

Methods: Forty-five patients with nasopharyngeal carcinoma were retrospectively studied. By applying our previously in-house developed network, a patient's sCT can rapidly be generated with respect to feeding the sole T1 image, T1C image, T1DixonC image, T2 image, and their combination (five pipelines in total).

Deep learning-based 3Ddose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.

To develop a novel deep learning-based 3Ddose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).The proposed method directly back-projected 2D portal dose into 3D patient coarse dose, which bypassed the complicated patient-to-EPID scatter estimation step used in conventional methods. A pre-trained convolutional neural network (CNN) was then employed to map the coarse dose to the final accurate dose.

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography.

Background: Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice.

Methods: We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT.

Extension and validation of a GPU-Monte Carlo dose engine gDPM for 1.5 T MR-LINAC online independent dose verification.

Purpose: To extend and validate the accuracy and efficiency of a graphics processing unit (GPU)-Monte Carlo dose engine for Elekta Unity 1.5 T Magnetic Resonance-Linear Accelerator (MR-LINAC) online independent dose verification.

Methods: Electron/positron propagation physics in a uniform magnetic field was implemented in a previously developed GPU-Monte Carlo dose engine-gDPM.

Comparison of Intensity Modulated Radiotherapy Treatment Plans Between 1.5T MR-Linac and Conventional Linac.

In this study, we assess the dosimetric qualities and usability of planning for 1.5 T MR-Linac based intensity modulated radiotherapy (MRL-IMRT) for various clinical sites in comparison with IMRT plans using a conventional linac. In total of 30 patients with disease sites in the brain, esophagus, lung, rectum and vertebra were re-planned retrospectively for simulated MRL-IMRT using the Elekta Unity dedicated treatment planning system (TPS) Monaco (v5.

In-Air Electron Streaming Effect for Esophageal Cancer Radiotherapy With a 1.5 T Perpendicular Magnetic Field: A Treatment Planning Study.

Purpose: To investigate the in-air out-of-field electron streaming effect (ESE) for esophageal cancer radiotherapy in the presence of 1.5 T perpendicular magnetic field.

Methods: Ten esophageal cancer patients treated with conventional Linac were retrospectively enrolled into a cohort of this study, with the prescription of 4,400 cGy/20 fx.

Feasibility of using a commercial collapsed cone dose engine for 1.5T MR-LINAC online independent dose verification.

Purpose: To validate the feasibility and accuracy of commonly used collapsed cone (CC) dose engine for Elekta Unity 1.5T MR-LINAC online independent dose verification.

Materials And Methods: The Unity beam model was built and commissioned in RayStation treatment planning system with CC dose engine.

Two ferritin genes (MdFerH and MdFerL) are involved in iron homeostasis, antioxidation and immune defense in housefly Musca domestica.

Ferritin is a ubiquitous multi-subunit iron storage protein, made up of heavy chain and light chain subunits. In recent years, invertebrate ferritins have emerged as an important, yet largely underappreciated, component of host defense and antioxidant system. Here, two alternatively spliced transcripts encoding for a unique ferritin heavy chain homolog (MdFerH), and a transcript encoding for a light chain homolog (MdFerL) are cloned and characterized from Musca domestica.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

Objectives: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.

Materials And Methods: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis.

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

Purpose: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.

Methods: Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs.

MdRDH1, a HSP67B2-like rhodanese homologue plays a positive role in maintaining redox balance in Musca domestica.

Rhodanese homology domains (RHODs) are the structural modules of ubiquitous tertiary that occur in three major evolutionary phyla. Despite the versatile and important physiological functions of RHODs containing proteins, little is known about their invertebrate counterparts. A novel HSP67B2-like single-domain rhodanese homologue, MdRDH1 from Musca domestica, whose expression can be induced by bacterial infection or oxidative stress.

Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy.

Background: Automatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists' planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework.