Tomotherapy dose distribution verification using MAGIC-f polymer gel dosimetry

Purpose: This paper presents the application of MAGIC-f gel in a three-dimensional dose distribution measurement and its ability to accurately measure the dose distribution from a tomotherapy unit. Methods: A prostate intensity-modulated radiation therapy (IMRT) irradiation was simulated in the gel phantom and the treatment was delivered by a TomoTherapy equipment. Dose distribution was evaluated by the R2 distribution measured in magnetic resonance imaging. Results: A high similarity was found by overlapping of isodoses of the dose distribution measured with the gel and expected by the treatment planning system (TPS). Another analysis was done by comparing the relative absorbed dose profiles in the measured and in the expected dose distributions extracted along indicated lines of the volume and the results were also in agreement. The gamma index analysis was also applied to the data and a high pass rate was achieved (88.4% for analysis using 3%/3 mm and of 96.5% using 4%/4 mm). The real three-dimensional analysis compared the dose-volume histograms measured for the planning volumes and expected by the treatment planning, being the results also in good agreement by the overlapping of the curves. Conclusions: These results show that MAGIC-f gel is a promise for tridimensional dose distribution measurements. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4704496]

Essential steps to providing reliable results using the Analytical Quality Assurance Cycle

Considering how demand for quality assurance (QA) has grown in analytical laboratories, we show the trends in analytical science, illustrated through international standard ISO/IEC 17025, validation, measurements of uncertainty, and quality-control (QC) measures. A detailed review of the history of analytical chemistry indicates that these concepts are consistently used in laboratories to demonstrate their traceabilities and competences to provide reliable results. We propose a new approach for laboratory QA, which also develops a diagram to support routine laboratories (which generally apply a quality system, such as ISO/IEC 17025) or research laboratories (that have some difficult applying this international standard). This approach, called the Analytical Quality Assurance Cycle (AQAC), presents the major QA concepts and the relationships between these concepts in order to provide traceability and reliable results. The AQAC is a practical tool to support the trend towards QA in analytical laboratories. (C) 2012 Elsevier Ltd. All rights reserved.

Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis

A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.

Effects of 3-Dimensional Conformal or Intensity-modulated Radiotherapy on Dental Pulp Sensitivity during and after the Treatment of Oral or Oropharyngeal Malignancies

Introduction: Radiation therapy (RT) of malignant tumors in the head and neck area may have damaging effects on surrounding tissues. The aim of this investigation was to evaluate the effects of RI delivered by 3-dimensional conformal radiotherapy (3D-RT) or intensity-modulated radiotherapy (IMRT) on dental pulp sensitivity. Methods: Twenty patients with oral or oropharyngeal cancer receiving RT with 3D-RT or IMRT underwent cold thermal pulp sensitivity testing (PST) of 2 teeth each at 4 time points: before RT (TP1), the beginning of RT with doses between 30 and 35 Gy (TP2), the end of RT with doses between 60 and 70 Gy (TP3), and 4 to 5 months after the start of RT (TP4). Results: All 40 teeth showed positive responses to PST at TP1 (100%) and 9 at TP2 (22.5%; 3/16 [18.8%] for 3D-RT and 6/24 [25.0%] for IMRT). No tooth responded to PST at TP3 and TP4 (0%). A statistically significant difference existed in the number of positive pulp responses between different time points (TP1 through TP4) for all patients receiving RT (P <= .05), IMRT (P <= .05), and 3D-RT (P <= .05). No statistically significant differences in positive sensitivity responses were found between 3D-RT and IMRT at any time point (TP1, TP3, TP4, P = 1.0; TP2, P = .74). A statistically significant correlation existed between the location of the tumor and PST at TP2 for IMRT (P <= .05) but not for 3D-RT (P = .14). Conclusions: RT decreased the number of teeth responding to PST after doses greater than 30 to 35 Gy. The type of RT (3D-RT or IMRT) had no influence on the pulp responses to PST after the conclusion of RT. (J Endod 2012;38:148-152)