Multi-institutional questionnaire-based survey on online adaptive radiotherapy performed using commercial systems in Japan in 2023.

In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023.

A pilot study on interobserver variability in organ-at-risk contours in magnetic resonance imaging-guided online adaptive radiotherapy for pancreatic cancer.

Purpose: Differences in the contours created during magnetic resonance imaging-guided online adaptive radiotherapy (MRgOART) affect dose distribution. This study evaluated the interobserver error in delineating the organs at risk (OARs) in patients with pancreatic cancer treated with MRgOART. Moreover, we explored the effectiveness of drugs that could suppress peristalsis in restraining intra-fractional motion by evaluating OAR visualization in multiple patients.

A new workflow of the on-line 1.5-T MR-guided adaptive radiation therapy.

Purpose: The aim of this study was to develop a new workflow for 1.5-T magnetic resonance (MR)-guided on-line adaptive radiation therapy (MRgART) and assess its feasibility in achieving dose constraints.

Materials And Methods: We retrospectively evaluated the clinical data of patients who underwent on-line adaptive radiation therapy using a 1.

Delivery accuracy of VMAT on two beam-matched linacs provided by accelerated go live service.

Introduction: Dosimetric accuracy is critical when a patient treated with volumetric modulated arc therapy (VMAT) is transferred to another beam-matched linac. To evaluate the performance of Accelerated Go Live (AGL) service, the measured beam characteristics and patient specific quality assurance (QA) results between two AGL-matched linacs were compared.

Materials And Methods: Two VersaHD linacs were installed using the AGL service.

[A Survey of Dosimetry in the Magnetic Field of MR-Linacs].

In recent years, MR-Linac, a radiotherapy linear accelerator (linac) equipped with magnetic resonance (MR) imaging, has been deployed in clinical facilities across Japan. Because of the magnetic field of MR-Linac, which can affect the dose distributions and dose response of ionization chambers, conventional reference dosimetry for absorbed dose to water using an ionization chamber becomes impractical. Consequently, the magnetic field effect should be considered in the reference dosimetry for MR-Linac.

Practical guidelines of online MR-guided adaptive radiotherapy.

The first magnetic resonance (MR)-guided radiotherapy system in Japan was installed in May 2017. Implementation of online MR-guided adaptive radiotherapy (MRgART) began in February 2018. Online MRgART offers greater treatment accuracy owing to the high soft-tissue contrast in MR-images (MRI), compared to that in X-ray imaging.

[Introduction of Young Researchers' Association of Medical Physics and Questionnaire Survey on Medical Physics Education and Medical Physics Training System in Japan for Young Researchers in 2021].

Details of Young Researchers' Association of Medical Physics (YRAMP) was introduced. In addition, several questionnaire surveys on medical physics education (MPE) or medical physicist training system (MPTS) in Japan have been conducted, none have targeted the current status and issues of MPE and MPTS. The purpose of this study was to investigate those from the perspective of researchers and students under 35-year-old (y.

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Purpose: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR-guided radiotherapy (MRgRT).

Material And Methods: In the current study, we used a dataset from the American Association of Physicists in Medicine MRI Auto-Contouring (RT-MAC) Grand Challenge 2019.

Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images.

Objective: The current study aims to propose the auto-segmentation model on CT images of head and neck cancer using a stepwise deep neural network (stepwise-net).

Material And Methods: Six normal tissue structures in the head and neck region of 3D CT images: Brainstem, optic nerve, parotid glands (left and right), and submandibular glands (left and right) were segmented with deep learning. In addition to a conventional convolutional neural network (CNN) on U-net, a stepwise neural network (stepwise-network) was developed.

Study of feasible and safe condition for total body irradiation using cardiac implantable electronic devices.

Cardiac implantable electronic devices (CIEDs) were believed to have a tolerance dose and that direct irradiation has to be avoided. Thus, no clinical guidelines have mentioned the feasibility of total body irradiation (TBI) with a CIED directly. The purpose of this work was to study a feasible and safe condition for TBI using a CIED.

Plastic scintillation dosimeter with a conical mirror for measuring 3D dose distribution.

Purpose: To test the measurement technique of the three-dimensional (3D) dose distribution measured image by capturing the scintillation light generated using a plastic scintillator and a scintillating screen.

Methods: Our imaging system constituted a column shaped plastic scintillator covered by a Gd O S:Tb scintillating screen, a conical mirror and a cooled CCD camera. The scintillator was irradiated with 6 MV photon beams.

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

Purpose: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT.

Three-dimensional dose-distribution measurement of therapeutic carbon-ion beams using a ZnS scintillator sheet.

The accurate measurement of the 3D dose distribution of carbon-ion beams is essential for safe carbon-ion therapy. Although ionization chambers scanned in a water tank or air are conventionally used for this purpose, these measurement methods are time-consuming. We thus developed a rapid 3D dose-measurement tool that employs a silver-activated zinc sulfide (ZnS) scintillator with lower linear energy transfer (LET) dependence than gadolinium-based (Gd) scintillators; this tool enables the measurement of carbon-ion beams with small corrections.

Verification system for intensity-modulated radiation therapy with scintillator.

In the preparation of intensity-modulated radiation therapy (IMRT), patient-specific verification is widely employed to optimize the treatment. To accurately estimate the accumulated dose and obtain the field-by-field or segment-by-segment verification, an original IMRT verification tool using scintillator light and an analysis workflow was developed in this study. The raw light distribution was calibrated with respect to the irradiated field size dependency and light diffusion in the water.

Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer.

Purpose: High-speed cone-beam computed tomography (CBCT) scan for image-guided radiotherapy (IGRT) can reduce both the scan time and the exposure dose. However, it causes noise and artifacts in the reconstructed images due to the lower number of acquired projection data. The purpose of this study is to improve the image quality of high-speed CBCT using a deep convolutional neural network (DCNN).

Prediction of radiation-induced malfunction for cardiac implantable electronic devices (CIEDs).

Purpose: Cardiac implantable electronic devices (CIEDs) were believed to possess a tolerance dose to malfunction during radiotherapy. Although recent studies have qualitatively suggested neutrons as a cause of malfunction, numerical understanding has not been reached. The purpose of this work is to quantitatively clarify the contribution of secondary neutrons from out-of-field irradiation to the malfunction of CIEDs as well as to deduce the frequency of malfunctions until completion of prostate cancer treatment as a typical case.

Predictive gamma passing rate for three-dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model.

Purpose: We aim to develop a method to predict the gamma passing rate (GPR) of a three-dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model.

Methods: Sixty head-and-neck intensity-modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step-and-shoot beams of the ONCOR linear accelerator.

A novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis.

Purpose: The output of a linear accelerator (linac) is one of the most important quality assurance (QA) factors in radiotherapy. However, there is no quantitative rationale for frequency and tolerance. The purpose of this study is to develop a novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis (FMEA).

Automatic calibration of an arbitrarily-set near-infrared camera for patient surface respiratory monitoring.

Purpose: A patient's respiratory monitoring is one of the key techniques in radiotherapy for a moving target. Generally, such monitoring systems are permanently set to a fixed geometry during the installation. This study aims to enable a temporary setup of such a monitoring system by developing a fast method to automatically calibrate the geometrical position by a quick measurement of calibration markers.

Predictive gamma passing rate by dose uncertainty potential accumulation model.

Purpose: Intensity-modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification.

A novel verification method using a plastic scintillator imagining system for assessment of gantry sag in radiotherapy.

Purpose: High accuracy of the beam-irradiated position is required for high-precision radiation therapy such as stereotactic body radiation therapy (SBRT), volumetric modulated arc therapy (VMAT), and intensity modulated radiation therapy (IMRT). Users generally perform the verification of the mechanical and radiation isocenters using the star shot test and the Winston Lutz test that allow evaluation of the displacement at the isocenter. However, these methods are unable to evaluate directly and quantitatively the sagging angle that is caused by the weight of the gantry itself along the gantry rotation axis.