ترغب بنشر مسار تعليمي؟ اضغط هنا

Robust dual-field optimization of scanned ion beams against range and setup uncertainties

51   0   0.0 ( 0 )
 نشر من قبل Taku Inaniwa
 تاريخ النشر 2010
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

A dual-field strategy is often used for tumors with highly complex shapes and/or with large volumes exceeding available field-size in both passive and scanning irradiations with ion beams. Range and setup uncertainties can cause hot and cold doses at the field junction within the target. Such uncertainties will also cause cold doses in the peripheral region of the target. We have developed an algorithm to reduce the sensitivity of the dual-field plan to these uncertainties in scanning irradiations. This algorithm is composed of the following two steps: 1) generating the expanded target volume, and 2) solving the inverse problem where the terms suppressing the dose gradient of individual fields are added into the objective function. The validity of this algorithm is demonstrated through the simulation studies for three extreme cases of two fields with unidirectional, opposing and orthogonal geometries. With the proposed algorithm, we can obtain a more robust plan to minimize the effects of range and setup uncertainties than the conventional plan. Compared to that for the conventional plan, the optimization time for the robust plan increased by a factor of approximately three.

قيم البحث

اقرأ أيضاً

An extensive comparison of the path uncertainty in single particle tracking systems for ion imaging was carried out based on Monte Carlo simulations. The spatial resolution as function of system parameters such as geometry, detector properties and th e energy of proton and helium beams was investigated to serve as a guideline for hardware developments. Primary particle paths were sampled within a water volume and compared to the most likely path estimate obtained from detector measurements, yielding a depth-dependent uncertainty envelope. The maximum uncertainty along this curve was converted to a conservative estimate of the minimal radiographic pixel spacing for a single set of parameter values. Simulations with various parameter settings were analysed to obtain an overview of the reachable pixel spacing as function of system parameters. The results were used to determine intervals of detector material budget and position resolution that yield a pixel spacing small enough for clinical dose calculation. To ensure a pixel spacing below 2 mm, the material budget of a detector should remain below 0.25 % for a position resolution of 200 $mathrm{mu m}$ or below 0.75 % for a resolution of 10 $mathrm{mu m}$. Using protons, a sub-millimetre pixel size could not be achieved for a phantom size of 300 mm or at a large clearance. With helium ions, a sub-millimetre pixel spacing could be achieved even for a large phantom size and clearance, provided the position resolution was less than 100 $mathrm{mu m}$ and material budget was below 0.75 %.
Purpose: Beam range control is the essence of radiotherapy with heavy charged particles. In conventional broad-beam delivery, fine range adjustment is achieved by insertion of range shifting and compensating materials. In dosimetry, solid phantoms ar e often used for convenience. These materials should ideally be equivalent to water. In this study, we evaluated dosimetric water equivalence of four common plastics, HDPE, PMMA, PET, and POM. Methods: Using the Bethe formula for energy loss, the Gottschalk formula for multiple scattering, and the Sihver formula for nuclear interactions, we calculated the effective densities of the plastics for these interactions. We experimentally measured variation of the Bragg peak of carbon-ion beams by insertion of HDPE, PMMA, and POM, which were compared with analytical model calculations. Results: The theoretical calculation resulted in slightly reduced multiple scattering and severely increased nuclear interactions for HDPE, compared to water and the other plastics. The increase in attenuation of carbon ions for 20-cm range shift was experimentally measured to be 8.9% for HDPE, 2.5% for PMMA, and 0.0% for POM while PET was theoretically estimated to be in between PMMA and POM. The agreement between the measurements and the calculations was about 1% or better. Conclusions: For carbon-ion beams, POM was dosimetrically indistinguishable from water and the best of the plastics examined in this study. The poorest was HDPE, which would reduce the Bragg peak by 0.45% per 1-cm range shift, although with marginal superiority for reduced multiple scattering. Between the two clear plastics, PET would be superior to PMMA in dosimetric water equivalence.
In dual-energy computed tomography (DECT), low- and high- kVp data are collected often over a full-angular range (FAR) of $360^circ$. While there exists strong interest in DECT with low- and high-kVp data acquired over limited-angular ranges (LARs), there remains little investigation of image reconstruction in DECT with LAR data. Objective: We investigate image reconstruction with minimized LAR artifacts from low- and high-kVp data over LARs of $le 180^circ$ by using a directional-total-variation (DTV) algorithm. Methods: Image reconstruction from LAR data is formulated as a convex optimization problem in which data-$ell_2$ is minimized with constraints on images DTVs along orthogonal axes. We then achieve image reconstruction by applying the DTV algorithm to solve the optimization problem. We conduct numerical studies from data generated over arcs of LARs, ranging from $14^circ$ to $180^circ$, and perform visual inspection and quantitative analysis of images reconstructed. Results: Monochromatic images of interest obtained with the DTV algorithm from LAR data show substantially reduced artifacts that are observed often in images obtained with existing algorithms. The improved image quality also leads to accurate estimation of physical quantities of interest, such as effective atomic number and iodine-contrast concentration. Conclusion: Our study reveals that from LAR data of low- and high-kVp, monochromatic images can be obtained that are visually, and physical quantities can be estimated that are quantitatively, comparable to those obtained in FAR DECT. Significance: As LAR DECT is of high practical application interest, the results acquired in the work may engender insights into the design of DECT with LAR scanning configurations of practical application significance.
In this paper, we solve the multiple product price optimization problem under interval uncertainties of the price sensitivity parameters in the demand function. The objective of the price optimization problem is to maximize the overall revenue of the firm where the decision variables are the prices of the products supplied by the firm. We propose an approach that yields optimal solutions under different variations of the estimated price sensitivity parameters. We adopt a robust optimization approach by building a data-driven uncertainty set for the parameters, and then construct a deterministic counterpart for the robust optimization model. The numerical results show that two objectives are fulfilled: the method reflects the uncertainty embedded in parameter estimations, and also an interval is obtained for optimal prices. We also conducted a simulation study to which we compared the results of our approach. The comparisons show that although robust optimization is deemed to be conservative, the results of the proposed approach show little loss compared to those from the simulation.
The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-depe ndent setup times, where the processing times are uncertain, and the only knowledge is the intervals they take values from. We propose a robust optimization model with min-max regret criterion to formulate this problem. To solve this problem, we prove that the worst-case scenario with the maximum regret for a given solution belongs to a finite set of extreme scenarios. Based on this theoretical analysis, the procedure to obtain the maximum regret is proposed and an enhanced regret evaluation method (ERE) is designed to accelerate this process. A multi-start decomposition-based heuristic algorithm (MDH) is proposed to solve this problem. High-quality initial solutions and an upper bound are examined to help better solve the problem. Computational experiments are conducted to justify the performance of these methods.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا