Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

Purpose Small field dosimetry has been an active area of research for over a decade. It is now known that large dosimetric errors can be introduced if proper detectors or correction factors are not used. The International Atomic Energy Agency (IAEA) through the technical report series No. 483 provides guidelines for small field dosimetry procedures as well as correction factors for most detectors available in the market. The plastic scintillator detector (PSD) Exradin W1 has been found to have a correction factor close to unity; however, it is not designed for beam scanning. To overcome this limitation, the new PSD Exradin W2 has been developed to be used as a scanning as well as a relative dosimeter. Characterization of this detector in small field dosimetry is presented in this study. Methods A 6 MV beam from a Varian‐Edge linac was used to collect data for the characterization of a W2 detector. Cerenkov light ratio (CLR) is corrected through a separate new electrometer system that comes with the W2 detector. The parameters investigated include the dose and dose rate linearity, beam profiles, percent depth dose (PDD), field output factors, and temperature response. The results were compared with Gafchromic film (EBT‐3 film) for beam profiles. The field output factor and temperature response were compared to the Exradin W1 detector. Results The dose linearity measured with 600 MU/min dose rate showed minimal variations (<0.5%) even for small MU, and similar results were seen for dose rate linearity. The comparison of field output factors between the W2 and W1 showed small differences for various depth and field sizes. The temperature response showed small variation when the temperature was varied from 6∘C to 50∘C. The slope was −0.0017false/∘C and −0.0016false/∘C for the W2 and the W1 detector, respectively. The differences in profiles are 0.5% in umbra and penumbra region for 1×1cm2 field size when compared to the EBT‐3 film profile. Conclusions The W2 scintillator detector showed similar dosimetric and temperature properties to the W1 scintillator detector. The main advantage of the W2 detector among other plastic scintillators is the beam scanning capabilities that, combined with its correction factor of 1.0, make it an ideal detector for commissioning of SRS and SBRT techniques.

PurposeThe aim of this paper is to define the requirements and describe the design and implementation of a standard benchmark tool for evaluation and validation of PET‐auto‐segmentation (PET‐AS) algorithms. This work follows the recommendations of Task Group 211 (TG211) appointed by the American Association of Physicists in Medicine (AAPM).MethodsThe recommendations published in the AAPM TG211 report were used to derive a set of required features and to guide the design and structure of a benchmarking software tool. These items included the selection of appropriate representative data and reference contours obtained from established approaches and the description of available metrics. The benchmark was designed in a way that it could be extendable by inclusion of bespoke segmentation methods, while maintaining its main purpose of being a standard testing platform for newly developed PET‐AS methods. An example of implementation of the proposed framework, named PETASset, was built. In this work, a selection of PET‐AS methods representing common approaches to PET image segmentation was evaluated within PETASset for the purpose of testing and demonstrating the capabilities of the software as a benchmark platform.ResultsA selection of clinical, physical, and simulated phantom data, including “best estimates” reference contours from macroscopic specimens, simulation template, and CT scans was built into the PETASset application database. Specific metrics such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity (S), were included to allow the user to compare the results of any given PET‐AS algorithm to the reference contours. In addition, a tool to generate structured reports on the evaluation of the performance of PET‐AS algorithms against the reference contours was built. The variation of the metric agreement values with the reference contours across the PET‐AS methods evaluated for demonstration were between 0.51 and 0.83, 0.44 and 0.86, and 0.61 and 1.00 for DSC, PPV, and the S metric, respectively. Examples of agreement limits were provided to show how the software could be used to evaluate a new algorithm against the existing state‐of‐the art.Conclusions PETASset provides a platform that allows standardizing the evaluation and comparison of different PET‐AS methods on a wide range of PET datasets. The developed platform will be available to users willing to evaluate their PET‐AS methods and contribute with more evaluation datasets.

The purpose of this work is to establish an automated approach for a multiple isocenter volumetric arc therapy (VMAT)-based TBI treatment planning approach. Five anonymized full-body CT imaging sets were used. A script was developed to automate and standardize the treatment planning process using the Varian Eclipse v15.6 Scripting API. The script generates two treatment plans: a headfirst VMAT-based plan for upper body coverage using four isocenters and a total of eight full arcs; and a feet-first AP/PA plan with three isocenters that covers the lower extremities of the patient. PTV was the entire body cropped 5 mm from the patient surface and extended 3 mm into the lungs and kidneys. Two plans were generated for each case: one to a total dose of 1200 cGy in 8 fractions and a second one to a total dose of 1320 cGy in 8 fractions. Plans were calculated using the AAA algorithm and 6 MV photon energy. One plan was created and delivered to an anthropomorphic phantom containing 12 OSLDs for in-vivo dose verification. For the plans prescribed to 1200 cGy total dose the following dosimetric results were achieved: median PTV V100% = 94.5%; median PTV D98% = 89.9%; median lungs Dmean = 763 cGy; median left kidney Dmean = 1058 cGy; and median right kidney Dmean = 1051 cGy. For the plans prescribed to 1320 cGy total dose the following dosimetric results were achieved: median PTV V100% = 95.0%; median PTV D98% = 88.7%; median lungs Dmean = 798 cGy; median left kidney Dmean = 1059 cGy; and median right kidney Dmean = 1064 cGy. Maximum dose objective was met for all cases. The dose deviation between the treatment planning dose and the dose measured by the OSLDs was within AE4%. In summary, we have demonstrated that scripting can produce high-quality plans based on predefined dose objectives and can decrease planning time by automatic target and optimization contours generation, plan creation, field and isocenter placement, and optimization objectives setup.

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