Purpose To provide percentage depth dose (PDD) data along the central axis for dosimetry calculations in small‐animal radiation biology experiments performed in cabinet irradiators. The PDDs are provided as a function of source‐to‐surface distance (SSD), field size, and animal size. Methods The X‐ray tube designs for four biological cabinet irradiators, the RS2000, RT250, MultiRad350, and XRAD320, were simulated using the BEAMnrc Monte Carlo code to generate 160, 200, 250, and 320 kVp photon beams, respectively. The 320 kVp beam was simulated with two filtrations: a soft F1 aluminium filter and a hard F2 thoraeus filter made of aluminium, tin, and copper. Beams were collimated into circular fields with diameters of 0.5–10 cm at SSDs of 10–60 cm. Monte Carlo dose calculations in 1–5‐cm diameter homogeneous (soft tissue) small‐animal phantoms as well as in heterogeneous phantoms with 3‐mm diameter cylindrical lung and bone inserts (rib and cortical bone) were performed using DOSXYZnrc. The calculated depth doses in three test‐cases were estimated by applying SSD, field size, and animal size correction factors to a reference case (40‐cm SSD, 1‐cm field, and 5‐cm animal size), and these results were compared with the specifically simulated (i.e., expected) doses to assess the accuracy of this method. Dosimetry for two test‐case scenarios of 160 and 250 kVp beams (representative of end‐user beam qualities) was also performed, whereby the simulated PDDs at two different depths were compared with the results based on the interpolation from reference data. Results The depth doses for three test‐cases calculated at 200, 320 kVp F1, and 320 kVp F2 with half value layers (HVLs) ranging from ∼0.6 to 3.6 mm Cu, agreed well with the expected doses, yielding dose differences of 1.2%, 0.1%, and 1.0%, respectively. The two end‐user test‐cases for 160 and 250 kVp beams with respective HVLs of ∼0.8 and 1.8 mm Cu yielded dose differences of 1.4% and 3.2% between the simulated and the interpolated PDDs. The dose increase at the bone‐tissue proximal interface ranged from 1.2 to 2.5 times the dose in soft tissue for rib and 1.3 to 3.7 times for cortical bone. The dose drop‐off at 1‐cm depth beyond the bone ranged from 1.3% to 6.0% for rib and 3.2% to 11.7% for cortical bone. No drastic dose perturbations occurred in the presence of lung, with lung‐tissue interface dose of >99% of soft tissue dose and <3% dose increase at 1‐cm depth beyond lung. Conclusions The developed dose estimation method can be used to translate the measured dose at a point to dose at any depth in small‐animal phantoms, making it feasible for preclinical calculation of dose distributions in animals irradiated with cabinet‐style irradiators. The dosimetric impact of bone must be accurately quantified as dramatic dose perturbations at and beyond the bone interfaces can occur due to the relative importance of the photoelectric effect at kilovoltage energies. These results will help improve dosimetric accuracy in preclinical experiments.

Purpose To develop a method of correcting for the inaccuracies of small adjoined field segments in their contribution to larger fields in order to get a better match between their combined signals and the measured integral quality monitor (IQM) open field signals. This would enable the pre‐calculation of known irregular segment output signals per monitor unit (MU), which would be later useful for patient‐based dose calculations for treatment verification during pre‐treatment treatment validation using the IQM output signal per MU. Methods Small fields exhibit source obscurity and loss of scatter, resulting in smaller signals being measured by the IQM and the subsequent underestimation of IQM output signals of larger segments obtained by combining small segment signals. Larger field segments were broken down into a set of smaller, regular, abutted segments, whose individual signals were added together to get the predicted output signal of the larger field. The signal/MU for each smaller constituent segment was extracted at its exact location from measured IQM response maps, generated by irradiating the IQM with small elementary segments ranging from 1 × 1 cm2 –5 × 5 cm2, shifting each segment 1 cm at a time and measuring its corresponding output signal/MU throughout the entire IQM sensitive area. The predicted signal was weighed against the IQM‐measured signal of the open field to calculate a signal correction factor (CF) of each elementary segment size. The CFs were applied to known signals of each set of elementary fields before summation in order to pre‐calculate signals of larger irregular fields more accurately. The dependence of CFs on elementary segment size, location of the open field, and beam energy was investigated. Results CFs exhibited an exponential decrease with increase in elementary segment size. CFs were also invariant with beam energy, changing by ≤1% from 6–15 MV. Uncorrected signals for regular fields had relative errors of above 5% whilst signal correction reduced these errors down to ~0.4% (i.e., 99.6% accuracy). For irregular fields, signal correction reduced calculation errors from ~10% to well below 1.5%. Larger signal prediction errors were found when smaller segments were used to reconstruct the field. Open field size and location had a great impact on measured signals but virtually no significance on CFs. Conclusions Results indicate that summation of small segment signals cannot sufficiently reproduce the same output given by an open field if individual elementary segment signals are not weighted with their respective CFs. This effect is particularly predominant for elementary segments 3 × 3 cm2 and for irregular fields. The method outlined enabled the calculation of signal CFs in order to match predicted signals with measured signals to 98.5% accuracy, thus enabling the pre‐calculation of irregular segment output signals/MU for future patient dose calculations.

Background: The Integral Quality Monitor (IQM ® ) can essentially measure the integral fluence through a segment and provide real-time information about the accuracy of radiation delivery based on comparisons of measured segment signals and pre-calculated reference values. However, the present IQM chamber cannot calculate the dose in the patient. Aim: This study aims to make use of IQM field output signals to calculate the number of monitor units (MUs) delivered through an arbitrary treatment field in order to convert Monte Carlo (MC)-generated dose distributions in a patient model into absolute dose. Methods: XiO and Monaco treatment planning systems (TPSs) were used to define treatment beam portals for cervix and esophagus conformal radiotherapy as well as prostate intensity-modulated radiotherapy for the translation of patient and beam setup information from DICOM to DOSXYZnrc. The planned beams were simulated in a patient model built from actual patient CT images and each simulated integral field/segment was weighted with its MUs before summation to get the total dose in the plan. The segment beam weights (MUs) were calculated as the ratio of the open-field IQM measured signal and the calculated signal per MU extracted from chamber sensitivity maps. These are the actual MUs delivered not just MUs set. The beam weighting method was evaluated by comparing weighted MC doses with original planned doses using profile and isodose comparisons, dose difference maps, γ analysis and dose-volume histogram (DVH) data. Results: γ pass rates of up to 98% were found, except for the esophagus plan where the γ pass rate was below 45%. DVH comparisons showed good agreement for most organs, with the largest differences observed in low-density lung. However, these discrepancies can result from differences in dose calculation algorithms or differences in MUs used for dose weighting planned by the TPS and MUs calculated using IQM field output signals. To test this, a 4-field box DOSXYZnrc MC simulation weighted with planned (XiO) MUs was compared with the same simulation weighted with IQM-based MUs. Dose differences of up to 5% were found on the isocentre slice. For XiO versus MC, up to 7% dose differences were found, indicating additional error due to limitations of XiO's superposition algorithm. Dose differences between MC Monaco and MC EGSnrc were less than 3%. Conclusions: The most valuable comparison was MC versus MC as it eliminated algorithm discrepancies and evaluated dose differences precisely according to beam weighting. For XiO TPS, care must be taken as dose differences may also arise due to limitations in XiO's planning software, not merely due to differences in MUs. Overall, the IQM was successfully used to compute beam dose weights to accurately reconstruct the patient dose using unweighted MC beams. Our technique can be used for pre-treatment QA provided each segment output is known and an accurate linac source model is available.

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Background: The Integral Quality Monitor (IQM®) is an independent online dosimetry device attached to the treatment machine to monitor the accuracy of radiation delivery. Objective: This study investigates the influence of beam segment size and displacement as projected onto the IQM chamber on the signals and determine how individual signals can be added to get a combined segment signal made up of smaller segments. Material and Methods: This is an experimental original research type of study. IQM response maps were generated by irradiating the IQM sensitive area with small elementary segments and measuring their corresponding signals per monitor unit (MU). The output signal/MU was measured for regular and irregular fields and compared with the predicted signal/MU obtained from decomposing the open segment into a set of smaller regular segments and summing their signals from their respective response maps. The dependence of signals on segment size, shape, location and combination was investigated. Results: Predicted signals were calculated within 95-98 % accuracy for regular fields and 90-98% for irregular fields. More uniform fluence contain distribution for larger segments was observed. Response maps were consistent with the geometrical symmetry in the chamber’s wedge shape and the symmetry in the linac fluence. Conclusion: The field decomposition method allows the pre-calculation of known segment output signals per MU within 2% error, although the accuracy drops significantly for smaller, irregular fields. A method of correcting predicted signals in smaller segments needs to be laid down to get a better match with measured signals.