Purpose: This study aims to devise and identify the errors of the beam width measurements in a wide beam CT system using three tungsten rings (TR) in comparison with a flat-panel detector (FPD), and develop a new method to correct the errors. Methods: A pencil-type ionization chamber was placed at the isocenter. The Kerma-length product ( KLP) was measured at 80 and 120 kV, 400 mA, and a rotation time of 0.5s with nominal beam widths ranging from 20 to 160 mm in 20 mm increments. Subsequently, each TR was attached to the chamber to measure the KLP_mask at the isocenter, and the beam width was calculated as KLP×ring length/( KLP−KLP_mask). To compare the measurement accuracy, the beam widths were measured using the FPD with a double-exposure technique. The X-ray exposures were performed at 80 kV, rotation time of 0.5 s, and 10 and 20 mA were used for the measurements. Finally, the heel effect correction, replacing the KLP at the anode side, was also compared. Results: The measured beam widths using 5-, 10-, and 15-mm TRs at 80/120 kV, and the FPD were 182.5 /182.1, 167.5/165.7, 168.2/163.0, and 172.9 mm in the nominal beam width of 160 mm, respectively. The heel effect correction with 10- and 15-mm TRs at 80 kV improved the measurement accuracy, and the corrected beam widths were 172.4 and 173.2 mm, respectively. Conclusion: In conclusion, 10- and 15-mm TRs in conjunction with the heel effect correction are appropriate for the beam width measurements in a wide beam CT system.

Purpose: Although the research areas applying artificial intelligence in the field of magnetic resonance imaging (MRI) have been expanding rapidly in recent years, the means to comprehensively understand these research areas have been limited. The purpose of this study was to visualize the research areas related to artificial intelligence in the field of MRI, and to understand the trend of research. Methods: Using PubMed database, we extracted article titles applying artificial intelligence in the MRI field from January 1, 1989 to December 31, 2023, created an extracted word list, graphs showing the relative frequency of occurrences of words, and drew a co-occurrence network diagram to investigate the frequency of appearance of words and changes in frequency and characteristic words over time. Results: The number of extracted titles was 2870. The most frequently appearing word was “deep learning” (1170 times from 2019 to 2023). Furthermore, deep learning was the word with the strongest co-occurrence (Jaccard coefficient 0.48 from 2019 to 2023). Regarding words related to organs, there was an increasing trend in the appearance frequency of the brain, prostate, and breast. Conclusion: In recent years, the research area related to artificial intelligence in the field of MRI has become a thriving area involving deep learning. In addition, there were many studies in the diagnostic area throughout the period.

Purpose: The effects of reconstruction algorithm and noise reduction intensity on low-contrast detectability in abdominal CT examinations were investigated. Methods: FBP, hybrid IR, and deep learning-based reconstruction methods (DLR for body, DLR for body sharp) were compared using an upper abdominal pseudo-human phantom. Imaging was performed under four radiation dose conditions, with three noise reduction intensities, and NPS and CNR_LO were used as evaluation indices. Results: DLR for body sharp showed excellent low-contrast detection performance with strong noise reduction and achieved a higher CNR_LO than the others. Hybrid IR and DLR for body showed equivalent performance regardless of noise reduction intensity, confirming the limitations of low-frequency noise suppression. Conclusion: It is important to select a reconstruction algorithm and noise reduction intensity according to the purpose of the examination, and DLR for body sharp is useful for improving image quality and reducing exposure at low doses.

Purpose: This study aimed to evaluate the changes in physical characteristics when the image reconstruction method, radiation dose, and pitch factor (PF) were varied in low-dose lung cancer CT screening, and to determine the optimal radiation dose and PF for appropriate dose reduction and the usefulness of deep learning reconstruction (DLR). Methods: Physical characteristics were evaluated using an Aquilion PrimeSP/i Edition (Canon Medical Systems, Tochigi) X-ray CT unit in conjunction with water phantoms and a chest phantom. Image reconstruction methods included filtered back projection (FBP), iterative reconstruction (IR), and DLR. Exposure conditions were varied across four dose levels and three PF levels. Physical characteristics were quantitatively evaluated using the noise power spectrum, task transfer function (TTF), low-contrast object-specific contrast-to-noise ratio (CNR_LO), and system performance function (SPF). Results: Both the IR application method and DLR improved noise characteristics compared to FBP, even at low doses, and reduced noise in the high spatial frequency domain when the PF level was lowered. DLR improved TTF at low doses and SPF at a standard deviation (SD) of 50. There was no significant difference in CNR_LO by PF level. Conclusion: DLR may be useful in low-dose lung cancer CT screening, and appropriate SD settings and PF selection may contribute to image optimization.

Purpose: The purpose of this study is to evaluate the detection accuracy of a high-definition optical surface imaging (OSI) system for non-coplanar radiotherapy (by rotating a phantom instead of a couch rotation). Methods: The constancy, reproducibility, and accuracy of the positioning of the OSI system, Catalyst HD (CHD), for non-coplanar treatment were examined by rotating the head phantom around the isocenter. For all the tests, the phantom was rotated by ±30°, ±45°, ±60°, ±90° after correction of the phantom position within 0.0 mm±0.2 mm, and 0.0°±0.1° using Cone Beam CT (CBCT); the CBCT images were acquired again after rotation. We compared the phantom position derived from CHD, translational displacements of the isocenter (Dev.), and rotational displacements (Rot.) to the position derived from CBCT. The constancy of monitoring was evaluated by observing the variation in the isocenter position for 30 min. For evaluating reproducibility, the positions derived from CHD were compared with those from the planning data. The accuracy of positioning was evaluated by comparing CHD and CBCT findings after the couch rotation of ±0.5°. Results: The constancy test revealed a maximum Rot. of 0.02±0.01° and Dev. of 0.20±0.08 mm, and the reproducibility test showed a maximum Rot. of 0.26±0.15° and Dev. of 0.93±0.26 mm. In the accuracy tests, when the phantom was further rotated by +0.5°, the maximum values were Rot. of 0.73±0.05° and Dev. of 0.35±0.15 mm; at −0.5°, the values were Rot. of −0.37±0.34° and Dev. of 0.43±0.24 mm. Conclusion: A high-resolution OSI system is useful for position detection during treatment, even in non-coplanar irradiation.

Understanding the trends in occupational radiation exposure is important for raising awareness of radiation protection. However, there have been no reports on the trends in exposure since the fiscal year 2018. In this resource, occupational exposure in the medical/radiological and nuclear fields from 2018 to 2022 was investigated, and the annual trends were evaluated. The method used was to organize the average/group doses using comprehensive statistical data on individual doses. In addition, the “dose lower limit index value” was set to 0.1, 1, and 20 mSv/y, and each group was analyzed. As a result, the majority of doses were below the detection limit, and there was no annual change in this proportion. There was no significant annual change in the average dose. Many radiologic technologists and doctors were over 0.1 and 1 mSv/y, but this has been decreasing since 2018. More than half of the people were doctors at over 20 mSv/y. However, medical care may not be reflected in dose data. No nuclear power workers received more than 20 mSv/y, and there was almost no annual change in any group. By setting the “dose lower limit index value” and conducting analysis, the individual dose and its annual change can be evaluated for workers who have been actually exposed to radiation.