Purpose: We report the depth dose measurements in air, in solid water, and in bone materials for the Intrabeam 50 kV x‐rays with a needle applicator. Methods: The absolute dose was measured using a PTW TN34013W soft x‐ray ion chamber. Gammex tissue equivalent materials of solid water, inner bone, and cortical bone slabs (minimum thickness of 2 mm) were used. In addition, the PTW solid water slabs with a minimum thickness of 1 mm were used. The manufactory calibrated depth dose data in water were compared. The x‐ray source together with a needle applicator was secured on an Intrabeam stand. The slabs lay on a 6 degrees of freedom treatment couch with a digitally controlled minimum step size of 0.1 mm. The depth of the source to the ion chamber was accurately and reproducibly adjusted by moving the couch up and down. Results: The depth dose measurements for the Intrabeam 50 kV x‐rays with a needle applicator were conducted up to 20 mm in depth. The values for the PTW solid water were close to those for water. The Gammex solid water demonstrated lower values compared to water, consistent with the observation of its positive CT number. At a depth of 10 mm, the dose rates of the system are 29.6, 3.6, 1.2, and 0.24 Gy/min in air, in water, in inner bone, and in cortical bone, respectively. The 10 mm water equivalent depths in inner and cortical bone are about 6.4 and 4.1 mm. A function of power law combining exponential was used to fit and interpolate data well. Conclusion: Direct depth dose measurements in different materials provide a basis for treatment calculation and planning taking into account the heterogeneous effect. The results can be used for verification of analytical and/or Monte Carlo dose calculation methods as well.

Purpose: To evaluate and compare characteristic performance of a new Landauer nanodot Reader with the previous model. Methods: In order to calibrate and test the reader, a set of nanodots were irradiated using a Varian Truebeam Linac. Solid water slabs and bolus were used in the process of irradiation. Calibration sets of nanodots were irradiated for radiation dose ranges: 0 to 10 and 20 to 1000 cGy, using 6MV photons. Additionally, three sets of nanodots were each irradiated using 6MV, 10MV and 15MV beams. For each beam energy, and selected dose in the range of 3 to 1000 cGy, a pair of nanodots was irradiated and three readings were obtained with both readers. Results: The analysis shows that for 3 photon beam energies and selected ranges of dose, the calculated absorbed dose agrees well with the expected value. The results illustrate that the new Microstar II reader is a highly consistent system and that the repeated readings provide results with a reasonably small standard deviation. For all practical purposes, the response of system is linear for all radiation beam energies. Conclusion: The Microstar II nanodot reader is consistent, accurate, and reliable. The new hardware design and corresponding software contain several advantages over the previous model. The automatic repeat reading mechanism, that helps improve reproducibility and reduce processing time, and the smaller unit size that renders ease of transport, are two of such features. Present study shows that for high dose ranges a polynomial calibration equation provides more consistent results. A 3rd order polynomial calibration curve was used to analyze the readings of dosimeters exposed to high dose range radiation. It was observed that the results show less error compared to those calculated by using linear calibration curves, as provided by Landauer system software for all dose ranges.

Purpose: To statistically determine the optimal tolerance level in the verification of delivery dose compared to the planned dose in an in vivo dosimetry system in radiotherapy. Methods: The LANDAUER MicroSTARii dosimetry system with screened nanoDots (optically stimulated luminescence dosimeters) was used for in vivo dose measurements. Ideally, the measured dose should match with the planned dose and falls within a normal distribution. Any deviation from the normal distribution may be redeemed as a mismatch, therefore a potential sign of the dose misadministration. Randomly mis‐positioned nanoDots can yield a continuum background distribution. A percentage difference of the measured dose to its corresponding planned dose (ΔD) can be used to analyze combined data sets for different patients. A model of a Gaussian plus a flat function was used to fit the ΔD distribution. Results: Total 434 nanoDot measurements for breast cancer patients were collected across a period of three months. The fit yields a Gaussian mean of 2.9% and a standard deviation (SD) of 5.3%. The observed shift of the mean from zero is attributed to the machine output bias and calibration of the dosimetry system. A pass interval of −2SD to +2SD was applied and a mismatch background was estimated to be 4.8%. With such a tolerance level, one can expect that 99.99% of patients should pass the verification and at most 0.011% might have a potential dose misadministration that may not be detected after 3 times of repeated measurements. After implementation, a number of new start breast cancer patients were monitored and the measured pass rate is consistent with the model prediction. Conclusion: It is feasible to implement an optimal tolerance level in order to maintain a low limit of potential dose misadministration while still to keep a relatively high pass rate in radiotherapy delivery verification.

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Purpose: The objective of this study is to verify and analyze the accuracy of a clinical deformable image registration (DIR) software. Methods: To test clinical DIR software qualitatively and quantitatively, we focused on lung radiotherapy and analyzed a single (Lung) patient CT scan. Artificial anatomical changes were applied to account for daily variations during the course of treatment including the planning target volume (PTV) and organs at risk (OAR). The primary CT (pCT) and the structure set (pST) was deformed with commercial tool (ImSimQA‐Oncology Systems Limited) and after artificial deformation (dCT and dST) sent to another commercial tool (VelocityAI‐Varian Medical Systems). In Velocity, the deformed CT and structures (dCT and dST) were inversely deformed back to original primary CT (dbpCT and dbpST). We compared the dbpST and pST structure sets using similarity metrics. Furthermore, a binary deformation field vector (BDF) was created and sent to ImSimQA software for comparison with known “ground truth” deformation vector fields (DVF). Results: An image similarity comparison was made by using “ground truth” DVF and “deformed output” BDF with an output of normalized “cross correlation (CC)” and “mutual information (MI)” in ImSimQA software. Results for the lung case were MI=0.66 and CC=0.99. The artificial structure deformation in both pST and dbpST was analyzed using DICE coefficient, mean distance to conformity (MDC) and deformation field error volume histogram (DFEVH) by comparing them before and after inverse deformation. We have noticed inadequate structure match for CTV, ITV and PTV due to close proximity of heart and overall affected by lung expansion. Conclusion: We have seen similarity between pCT and dbpCT but not so well between pST and dbpST, because of inadequate structure deformation in clinical DIR system. This system based quality assurance test will prepare us for adopting the guidelines of upcoming AAPM task group 132 protocol.