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: Radioproteomics studies investigating the relationship between lesion phenotype and proteins have been progressed. The purpose of this study was to develop a radioproteomics method for discriminating between active and inactive immune checkpoint molecules based on lesion phenotype. Methods: From the public database TCGA-BRCA, mRNA and fat suppression contrast-enhanced T₁-weighted images of 49 patients with breast cancer were selected for the experiment. Using mRNA, we defined cases with active (10 cases) and inactive (39 cases) immune checkpoint molecules. To discriminate these cases using lesion phenotype, 275 radiomics features were measured from the tumor area. After selecting 3 radiomics features by using Lasso, logistic regression was employed to discriminate between active and inactive cases of immune checkpoint molecules. Results: Evaluation of ROC analysis showed that the AUC was 0.81. Conclusion: Patients whose immune cell function is being braked by immune checkpoint molecules are likely to respond to immune checkpoint inhibitors when their activity is inhibited. Therefore, our results may be applied to predict the effects of immune checkpoint inhibitors in breast cancer treatment.

Purpose: In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions. Methods: We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part. Results: The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness. Conclusion: The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.