Purpose: This study deals with the conversion of the image quality figure (IQF_inv) values with CDMAM Analyser ver. 1 (old analysis software) used to analyse the images of CDMAM 3.4 phantom into the IQF_inv values with a new analysis software, that is, CDMAM Analyser (ver. 2 and ver. 2.3), which is a software improved to be closer to human visual evaluation. Methods: The IQF_inv values were calculated using four mammography units and three analysis software. The IQF_inv values using the old and new software were compared. Results: The IQF_inv values had no difference according to mammography units and beam quality. The conversion formula of IQF_inv from CDMAM Analyser ver. 1 to CDMAM Analyser ver. 2 was derived. Furthermore, the conversion formula of IQF_inv from CDMAM Analyser ver. 1 to CDMAM Analysis ver. 2.3 was also derived. Conclusion: Using each conversion formula, the IQF_inv values using the old software can be converted to those using the new software. There was a slight difference between the IQF_inv values using the new software.

Objective: Subject contrast of pulmonary tissues was investigated for five X-ray beams (70 kV without filter, 90 kV with 0.15 mm Cu filter, 90 kV with 0.2 mm Cu filter, 120 kV without filter, and 120 kV with 0.2 mm Cu filter) in CsI-FPD chest radiography using two types of model phantoms by Monte Carlo simulation. Methods: A total of 72 million photons were entered to the model lung phantom (width, 300 mm; length, 300 mm; thickness, 200 mm; air space, 120 mm) and model mediastinum phantom (width, 300 mm; length, 300 mm; thickness, 200 mm; air space, 40 mm). Individual primary and secondary photon’s process (absorption, scattering, and penetration) in the phantom and CsI-detector was recorded by Monte Carlo simulation. Subject contrast was calculated by entered and absorbed photon’s number in the CsI-detector. Results: Subject contrast pulmonary tissues were high to low energy X-ray beam; however, the ones of soft tissue and soft tissue overlaying bone had few differences for beam quality except 70 kV without filter. Moreover, the subject contrast by absorbed photons was higher compared to the one by entered photons in CsI. Conclusion: It was shown that the subject contrast study by Monte Carlo calculation can be replaced by the way of physical chest phantom, and that the subject contrast by absorbed photons and by injected photons in CsI was different. Furthermore, be verified that the subject contrast of soft tissue and soft tissue overlaying bone differs hardly.

Purpose: We developed X-ray dose monitoring and optimization software for computed tomography (CT) by using a spreadsheet software. Methods: The analysis was carried out on 1,212 patients using 2,128 CT examinations at our institution, in the period of April 2020 to April 2022. These cases were extracted in the limitation of patient’s weight in the range of 50 to 70 kg. The individual radiation report, including CTDI_vol and DLP, on the console displayed by a CT system was used and manually transferred to our software. The X-ray dose distribution was evaluated by using a boxplot chart and a scatter plot, and the data were referred to Japan DRLs 2020 (National Diagnostic Reference Levels in Japan 2020). Results: Almost all patient’s doses in our institution were distributed in the acceptable ranges in comparison with the Japan DRLs 2020. Neither extremely high nor low dose data were recorded. However, seven cases recorded outlier dose values. Three cases were caused by errors during manually inputting the data. Four from seven cases were explored the reason to record outlier dose values. Conclusion: Our software on a spreadsheet software worked well to explore the CT dose data, such as CTDI_vol and DLP. Our software was able to find cases that were recorded as outlier dose values, but their values were not extremely high/low values. The cautionary notice of either high or low dose, as a function of our software, will be able to keep our patient’s CT dose in the universal-standard “justification and optimization” level.

Purpose: We have proposed a new formulation that can decompose the profile constancy defined in the AAPM TG 142 into energy and symmetry constancies by measuring beam profiles using an IC profiler (ICP). Methods: Measured profiles were laterally inverted to calculate averaged profiles in the lateral direction, thereby cancelling asymmetric components. Validation tests were performed by comparing the proposed calculation and measured results under various experimental conditions. Calculated profile constancies were further compared to decomposed energy and symmetry constancies. Results: The energy constancy calculated from the averaged beam profile by lateral inversion and the measured PDD(10) constancy agreed within 0.1% when only symmetries were varied. The calculated energy and symmetry constancies, and the measured results agreed within 0.2% when both energies and symmetries were varied. Conclusion: The linac beam profile constancy has been decomposed into energy and symmetry terms. The proposed formulation has been validated by comparing the calculations and the direct measurements using the ICP. We have shown that QA/QC for profile constancy tests can be efficiently performed using the proposed formulation.

DRLs 2020 has been revised, and K_a,r and P_KA for each procedure have been set for IVR along with the reference fluoroscopic dose rate. The total dose of IVR includes fluoroscopic and digital acquisition (DA) doses, but in actual clinical practice, the ratio varies greatly depending on the procedure (diagnosis/treatment purpose and procedure content), and there are not many detailed data on the ratio. Therefore, we evaluated previous efforts that optimized radiation protection through examining dose for each procedure and the ratio of fluoroscopic and DA doses to total dose, and reviewing protocols. Since the ratio of fluoroscopy and DA dose differs depending on the procedure, it was suggested that the radiation dose exposed to patients can be optimized by sharing the dose information with physicians and constructing a protocol while considering the image quality for each procedure.

Background: If we try to perform the inspection for an electrometer, two types of electronic signals energized to the electrometer can be used. One is the signal that occurs in the ionization chamber irradiated by radiation. The other is the signal that is derived from a current source. The former signal is changed by radiation output, so we need to use two or more sets of the chambers and the electrometers in the inspection. In addition, the high-performance current source is relatively expensive. Therefore, it is difficult for users to inspect the electrometer simply. To deal with these, we have developed a new current source that allows users to perform highly accurate electrometer inspections at their own facilities. Aim: The purpose of this study was to verify that users can perform electrometer inspections with high accuracy in their own facilities by using a new current source. Materials and Methods: A newly current source equips a dry cell battery as a charge generator. Current, polarity, and energized time can be changed by the source setting. We conducted an inspection by energizing the electrometer using the developed current source. Results: The coefficient of variation of the charge amount in the measurement using the new current source was within 0.05%. The electrometer calibration coefficients measured by sensitivity comparison using this current source could be obtained based on electrometers calibrated at a certified facility of the Japan Calibration Service System. Conclusion: We have shown that the new current source can be used as a relative current value by using a calibrated electrometer as a reference, indicating that the user can check the electrometer.