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Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams. Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations. Methods: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations. Results: The best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU. Conclusions: The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work was to provide a novel description of how the radiotherapy community configures treatment planning system (TPS) radiation beam models for clinically used treatment machines. Here we describe the results of a survey of self‐reported TPS beam modeling parameter values across different C‐arm linear accelerators, beam energies, and multileaf collimator (MLC) configurations. Acquisition and validation methods: Beam modeling data were acquired via electronic survey implemented through the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center's online facility questionnaire. The survey was open to participation from January 2018 through January 2019 for all institutions monitored by IROC. After quality control, 2818 beam models were collected from 642 institutions. This survey, designed for Eclipse, Pinnacle, and RayStation, instructed physicists to report parameter values used to model the radiation source and MLC for each treatment machine and beam energy used clinically for intensity‐modulated radiation therapy. Parameters collected included the effective source/spot size, MLC transmission, dosimetric leaf gap, tongue and groove effect, and other nondosimetric parameters specific to each TPS. To facilitate survey participation, instructions were provided on how to identify requested beam modeling parameters within each TPS environment. Data format and usage notes: Numeric values of the beam modeling parameters are compiled and tabulated according to TPS and calculation algorithm, linear accelerator model class, beam energy, and MLC configuration. Values are also presented as distributions, ranging from the 2.5th to the 97.5th percentile. Potential applications: These data provide an independent guide describing how the radiotherapy community mathematically represents its clinical radiation beams. These distributions may be used by the community for comparison during the commissioning or verification of their TPS beam models. Ultimately, we hope that the current work will allow institutions to spot potentially suspicious parameter values and help ensure more accurate radiotherapy delivery. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion A passive dosimeter framework for the measurement of dose in carbon ion beams has yet to be characterized or implemented for regular use.This work determined the dose calculation correction factors for absorbed dose in thermoluminescent dosimeters (TLDs) in a therapeutic carbon ion beam. TLD could be a useful tool for remote audits, particularly in the context of clinical trials as new protocols are developed for carbon ion radiotherapy.TLD‐100 were irradiated in a carbon ion beam at the Centro Nazionale di Adroterapia Oncologica (CNAO) in Pavia, Italy. The dose correction factors for linearity, fading, and beam quality were characterized. Fading was characterized from 5 to 100 days post‐irradiation. For linearity, the TLDs were irradiated to absorbed doses ranging from 1 to 15 Gy in both the entrance of a high‐energy pristine carbon ion peak and the center of a 2 cm spread‐out Bragg peak. For beam quality, the TLD was irradiated to the same absorbed dose (3 Gy) in several pristine carbon ion Bragg peaks, as well as in several spread‐out Bragg peaks. Each correction factor was calculated and compared to photon correction factors. The correction factors were also compared between high and low dose‐averaged linear energy transfer (LETD) in the carbon ion beams. The absorbed dose was compared between ion chamber and TLD‐100 in the several tissue substitute phantom materials, applying the carbon ion TLD correction factors.There was no statistically significant difference in the TLD fading correction factor between photons, low LETD carbon ion beams, or high LETD carbon ion beams. The TLD linearity correction factor did differ between photons, low LETD carbon ions, and high LETD carbon ions. The beam quality correction factor was large and changed linearly with LETD. The overall uncertainty of the carbon ion absorbed dose calculation was 3.9% at the one‐sigma level, driven largely by a 3.5% uncertainty in the beam quality correction. TLD measurements were within 1.2% of ion chamber measurements in the phantom material for polyethylene, solid water (Gammex and Sun Nuclear), acrylic, blue water, and techtron HPV. TLD measurements in balsa wood were within 3.0% and cork was 6.6% low compared to ion chamber.TLD‐100 can be used for passive dosimetry in a therapeutic carbon ion beam. Importantly, the linearity and beam quality correction factors are both different from photon therapy, and dependent on LETD of the carbon ion beam. This opens the possibility of TLD use for carbon ion output audits, phantom audits, and in vivo dose measurements, but may be subject to more complicated management and slightly larger uncertainties than are achieved in photon beams. [ABSTRACT FROM AUTHOR]