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: 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.