Purpose: To assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients.
Methods: We analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV and prophylactic CTV, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs). The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined.
Results: The difference in the residual geometrical error dr (propagated CTVs - physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV) = 0.16 mm, Δdr(CTV) = 0.26 mm, |ΔDSC|mean < 0.9%. For all 28 patients, dr(CTV) = 1.91 mm and dr(CTV) = 1.90 mm. However, CTV above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose.
Conclusions: Setup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.
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http://dx.doi.org/10.1016/j.radonc.2023.109856 | DOI Listing |
Materials (Basel)
December 2024
Ulsan Ship and Ocean College, Ludong University, Yantai 264025, China.
This study introduces a novel analytical framework for investigating the vibration characteristics of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) elliptical cylindrical shells under arbitrary boundary conditions. Unlike previous studies that focused on simplified geometries or specific boundary conditions, this work combines the least-squares weighted residual method (LSWRM) with an adapted variational principle, addressing high-order vibration errors and ensuring continuity across structural segments. The material properties are modeled using an extended rule of mixtures, capturing the effects of carbon nanotube volume fractions and distribution types on structural dynamics.
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December 2024
Library, Panjin Campus of Dalian University of Technology, Panjin 124000, China.
Book localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional object detection algorithms. To address these challenges, this study proposes an enhanced oriented R-CNN algorithm for book spine detection.
View Article and Find Full Text PDFArch Ration Mech Anal
December 2024
Instituto de Ciencias Matemáticas, Consejo Superior de Investigaciones Científicas, 28049 Madrid, Spain.
For any (analytic) axisymmetric toroidal domain we prove that there is a locally generic set of divergence-free vector fields that are not topologically equivalent to any magnetohydrostatic (MHS) state in . Each vector field in this set is Morse-Smale on the boundary, does not admit a nonconstant first integral, and exhibits fast growth of periodic orbits; in particular this set is residual in the Newhouse domain. The key dynamical idea behind this result is that a vector field with a dense set of nondegenerate periodic orbits cannot be topologically equivalent to a generic MHS state.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials.
View Article and Find Full Text PDFNanophotonics
March 2024
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.
As a non-destructive and rapid technique, optical scatterometry has gained widespread use in the measurement of film thickness and optical constants. The recent advances in deep learning have presented new and powerful approaches to the resolution of inverse scattering problems. However, the application of deep-neural-network-assisted optical scatterometry for nanostructures still faces significant challenges, including poor stability, limited functionalities, and high equipment requirements.
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