Background: Sophisticated modern radiation therapy treatments require comprehensive validation in 3D.
Purpose: Investigation and characterization of a novel 3D dosimetry system consisting of ClearView radiochromic gel dosimeters (commercially available from Modus Inc) and an in-house telecentric optical CT scanner DLOS (the Duke Large Field of View Optical-CT Scanner).
Methods: Spectrophotometry measurements were made on small volumes of ClearView gel irradiated with 6X photon doses up to 40 Gy to determine linearity and temporal stability of dose response. Clinical evaluation of Clearview/DLOS system was conducted in two phases. Phase one involved simple photon and electron benchmark irradiations, delivered to 15 and 10 cm diameter dosimeters, at various energies and doses. Phase 2 investigated application to the verification of two single isocenter multi-target (SIMT) stereotactic radiosurgery (SRS) deliveries. These were patient treatments for two and five brain lesions, respectively, and delivered to 15 cm diameter dosimeters. SIMT treatments were delivered by Varian TrueBeam 6X with doses of 40 Gy. For dose read-out, dosimeters were optically scanned in the DLOS both pre- and post- irradiation (within 24 h). 3D reconstructions (1 mm resolution) of the change in linear-optical- attenuation (proportional to dose) was obtained using in-house software and 3D Slicer. Measured and predicted (Eclipse TPS) doses were compared through percent depth-dose (PDD), cross plane and in-plane profiles, and relative 3D gamma analysis (performed at a range of 7%/4 mm down to 2%/2 mm). Regions of known artifacts were excluded from analysis (jar base, neck, and wall). The SIMT SRS deliveries were additionally compared to SciMoca, an independent Monte Carlo second check software.
Results: Linearity of dose response was confirmed with R2 ≥ 0.9986 at both 520 and 630 nm wavelengths and at three post-irradiation time points: 21 h, 6 and 10 days. Dose profiles of all benchmark irradiations, in both 15 and 10 cm dosimeters, show good agreement in useable areas of the gel compared to Eclipse dose calculations, with root mean square errors (RMSE) ≤ 0.0054, and R2 ≥ 0.9808. Gamma pass rates for the 15 cm dosimeter benchmark irradiations were ≥ 94% at 2%/2 mm (central axis), ≥ 90% at 3%/3 mm (left lateral), ≥ 90% at 2%/2 mm (electron), and ≥ 94% at 3%/2 mm (stacking field). Similar high passing rates were observed for benchmark irradiations to the smaller 10 cm diameter dosimeters. Very high Gamma pass rates were found for SIMT SRS deliveries, with 99.82% and 97.80% at 3%/2 mm, for the two and five target plans, respectively.
Conclusion: This work presents the first investigation of ClearView dosimeters in combination with a telecentric optical-CT scanner (DLOS). Simple benchmark irradiations demonstrate ClearView/DLOS can accurately recreate and measure relative 3D dose within non-artifact regions (i.e., > 1 cm away from walls). Application to SIMT SRS deliveries demonstrated the viability of the system as a means for comprehensive 3D verification of complex treatment deliveries as well as confirming the treatment planning system dose distribution. The results indicate that DLOS/ClearView system is a highly effective 3D verification tool for SIMT irradiations and can be applied with 3%/2 mm gamma criteria where passing rates of > 95% are to be expected.
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http://dx.doi.org/10.1002/mp.16632 | DOI Listing |
Sci Rep
January 2025
Imec, imo-imomec, Thor Park 8320, 3600, Genk, Belgium.
This study presents a comprehensive evaluation of Copper Indium Gallium Selenide (CIGS) solar technology, benchmarked against crystalline silicon (c-Si) PERC PV technology. Utilizing a newly developed energy yield model, we analyzed the performance of CIGS in various environmental scenarios, emphasizing its behavior in low-light conditions and under different temperature regimes. The model demonstrated high accuracy with improved error metrics of normalized mean bias error (nMBE) ~ 1% and normalized root mean square error (nRMSE) of ~ 8%-20% in simulating rack mounted setup and integrated PV systems.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China. Electronic address:
Radiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Despite that deep learning has achieved remarkable performance on various medical image segmentation tasks, its performance on OARs and GTVs of NPC is still limited, and high-quality benchmark datasets on this task are highly desirable for model development and evaluation.
View Article and Find Full Text PDFInt J Radiat Biol
January 2025
Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada.
Background: Recent advancements in omics and benchmark dose (BMD) modeling have facilitated identifying the dose required for a predetermined change in a response (e.g. gene or protein change) that can be used to establish acceptable dose levels for hazardous exposures.
View Article and Find Full Text PDFEur Radiol
December 2024
Department of Radiology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Objective: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
Materials And Methods: We compare a multi-organ segmentation approach and the fusion of multiple single-organ models, each dedicated to one OAR. All were trained using nnU-Net with the default parameters and the full-resolution configuration.
Radiother Oncol
December 2024
Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing 400038, China. Electronic address:
Background And Purpose: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' workload, challenges persist due to the microscopic spread of tumor cells undetectable in CT imaging, low-intensity contrast between organs, and inter-observer variability. This study aims to develop and validate a multi-task feature fusion network (MTF-Net) that uses distance-based information to enhance CTV segmentation accuracy.
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