The goal of the present study was to develop a new dose–volume histogram (DVH)– based homogeneity index for effectively evaluating the dose homogeneity of intensity‐modulated radiotherapy plans. The new index, called the sigma‐index (“S‐index”) is defined as the standard deviation of the normalized differential DVH curve. In a study of 16 patients with brain tumors at our institution, the S‐index was found to vary from 0.80 to 3.15. Our results showed that the S‐index provides a more reliable and accurate measure of dose homogeneity than that given by conventional methods. A guideline for evaluating the dose homogeneity of treatment plans based on the S‐index and its relation to equivalent uniform dose is discussed.PACS numbers: 87.53.Xd, 87.53.Tf

This article presents the reconfigurable flight controller using an adaptive sliding mode control scheme for actuator fault case. Sliding mode controller, which has good performance for the systems with various uncertainties, is used to deal with the actuator faults. Actuator fault can be considered as a disturbance or an unexpected parameter change, which degrades the system performance and may destabilize the system. In this study, the adaptive sliding mode control technique is adopted to compensate the effects of the disturbance generated by actuator faults. Lyapunov stability theory is used to derive the adaptive rule, and the closed-loop system stability analysis is performed. To demonstrate the effectiveness of the proposed controller, numerical simulation is performed for aircraft having redundant control surfaces.

No abstract

We synthesized 2-{3-[(E)-2-(dibutylamino)-1-ethenyl]-5,5-dimethyl-2-cyclohexenylidene}malononitrile as a noval nonlinear optical (NLO) chromophore for photorefractive materials. The photorefractive polymeric composite composed of photoconducting carbazole-substituted polysiloxane, NLO chromophore, and 2,4,7-trinitro-9-fluorenone exhibited excellent photorefractive properties. The gain coefficient of the composite containing 30 wt % of chromophore was as high as 390 cm -1 at the electric field of 100 V/µm, which is one of the largest values obtained so far in the field of polymeric photorefractive materials. And the composite with a thickness of 60 µm showed the maximum diffraction efficiency of 56% at 30 V/µm.

In proton therapy dosimetry, a fiber-optic radiation sensor incorporating a scintillator must undergo complicated correction processes due to the quenching effect of the scintillator. To overcome the drawbacks of the fiber-optic radiation sensor, we proposed an innovative method using the Cerenkov radiation generated in plastic optical fibers. In this study, we fabricated a fiber-optic Cerenkov radiation sensor without an organic scintillator to measure Cerenkov radiation induced by therapeutic proton beams. Bragg peaks and spread-out Bragg peaks of proton beams were measured using the fiber-optic Cerenkov radiation sensor and the results were compared with those of an ionization chamber and a fiber-optic radiation sensor incorporating an organic scintillator. From the results, we could obtain the Bragg peak and the spread-out Bragg peak of proton beams without quenching effects induced by the scintillator, and these results were in good agreement with those of the ionization chamber. We also measured the Cerenkov radiation generated from the fiber-optic Cerenkov radiation sensor as a function of the dose rate of the proton beam.

No abstract

No abstract

BackgroundAccurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability.This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer.MethodsOn-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures.ResultsThe atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively.ConclusionsIn this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.