Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Más filtros










Base de datos
Intervalo de año de publicación
1.
Radiol Phys Technol ; 17(1): 280-287, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38261133

RESUMEN

The reference dose for clinical proton beam therapy is based on ionization chamber dosimetry. However, data on uncertainties in proton dosimetry are lacking, and multifaceted studies are required. Monte Carlo simulations are useful tools for calculating ionization chamber dosimetry in radiation fields and are sensitive to the transport algorithm parameters when particles are transported in a heterogeneous region. We aimed to evaluate the proton transport algorithm of the Particle and Heavy Ion Transport Code System (PHITS) using the Fano test. The response of the ionization chamber f Q and beam quality correction factors k Q were calculated using the same parameters as those in the Fano test and compared with those of other Monte Carlo codes for verification. The geometry of the Fano test consisted of a cylindrical gas-filled cavity sandwiched between two cylindrical walls. f Q was calculated as the ratio of the absorbed dose in water to the dose in the cavity in the chamber. We compared the f Q calculated using PHITS with that of a previous study, which was calculated using other Monte Carlo codes (Geant4, FULKA, and PENH) under similar conditions. The flight mesh, a parameter for charged particle transport, passed the Fano test within 0.15%. This was shown to be sufficiently accurate compared with that observed in previous studies. The f Q calculated using PHITS were 1.116 ± 0.002 and 1.124 ± 0.003 for NACP-02 and PTW-30013, respectively, and the k Q were 0.981 ± 0.008 and 1.027 ± 0.008, respectively, at 150 MeV. Our results indicate that PHITS can calculate the f Q and k Q with high precision.


Asunto(s)
Terapia de Protones , Protones , Método de Montecarlo , Radiometría/métodos , Simulación por Computador
2.
Sci Rep ; 13(1): 15413, 2023 09 18.
Artículo en Inglés | MEDLINE | ID: mdl-37723226

RESUMEN

Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phantom for radiotherapy. We compared the CT values, image noise, and CT value-to-electron density conversion table of DLR and hybrid iterative reconstruction (H-IR) for various doses. Further, we evaluated three DLR reconstruction strength patterns (Mild, Standard, and Strong). The variations of CT values of DLR and H-IR were large at low doses, and the difference in average CT values was insignificant with less than 10 HU at doses of 100 mAs and above. DLR showed less change in CT values and smaller image noise relative to H-IR. The noise-reduction effect was particularly large in the low-dose region. The difference in image noise between DLR Mild and Standard/Strong was large, suggesting the usefulness of reconstruction intensities higher than Mild. DLR showed stable CT values and low image noise for various materials, even at low doses; particularly for Standard or Strong, the reduction in image noise was significant. These findings indicate the usefulness of DLR in treatment planning using large-bore CT systems.


Asunto(s)
Aprendizaje Profundo , Oncología por Radiación , Fantasmas de Imagen , Tomografía Computarizada por Rayos X , Procesamiento de Imagen Asistido por Computador
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...