Purpose: There are problems with dose management in X-ray computed tomography (CT) because the protocol used for any examination is not always in the same scan range. The purpose of this study was to investigate the usefulness of setting the CT protocol based on the scan range. Methods: We evaluated the examination data of patients who underwent plain CT based on a scan range of chest to pelvis and abdomen to pelvis. The previous protocol [Chest-Abdomen Routine] was changed to the current protocols [Chest_Abdomen] and [Chest_Pelvis], and the previous protocol of [Abdomen Routine] was changed to the current protocols [Abdomen] and [Abdomen_Pelvis]. Examination data of height, scan length, volume CT dose index (CTDI_vol), and dose length product (DLP) were obtained from digital imaging and communications in medicine, and radiation dose structured report using Radimetrics. The relationship between patient height and scan range, and CTDI_vol and DLP was indicated in a scatter plot. Standard deviation (SD) of scan length and DLP were compared between current and previous protocols. Outliers were defined as the data exceeding average ±2SD. Results: The SD of scan length decreased by 77.1% on abdomen to pelvis, and the SD of DLP decreased by 65.2% on abdomen to pelvis. The causes of the outliers were CT scan range, scan parameter, arm position, metal implants, and body thickness of patients. Conclusion: Setting CT protocols based on the scan range reduced SD of scan length and DLP. It was helpful for reducing the number of scan range outliers and analyzing the cause of outliers.

In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT value, noise power spectrum (NPS), and task-based modulation transfer function (TTF) were evaluated. DLR reduced image noise while maintaining sharpness, and the noise reduction effect showed a different characteristic depending on the size of reconstruction FOV from the conventional image reconstruction methods. The SD of CT value increased gradually in the range from 320 mm to 240 mm, but there was almost no change from 240 mm to 200 mm. The NPS showed completely different characteristics. The low-frequency component increased, and the high-frequency component decreased at 240 mm. However, the frequency component below 0.5 cycle/mm decreased at 200 mm and the peak frequency moved to the lower side at 320 mm. DLR showed the highest TTF value compared to the conventional reconstruction methods.

Purpose: Using ultra-high-resolution images reconstructed with the Advanced intelligent Clear-IQ Engine (AiCE) lung to measure wall area percentage (WA%), we demonstrated that WA% measured in more distal bronchus has a stronger correlation with respiratory function (FEV₁%). Furthermore, we also demonstrated that WA% measured from images with the higher spatial resolution has a stronger correlation with FEV₁%. Methods: The modulation transfer function (MTF) and noise power spectrum (NPS) of the ultra-high-resolution images reconstructed by the AiCE body and the AiCE lung were compared. In addition, WA% from the first- to seventh-generation bronchus was measured for B1 and B10 in the right lung from clinical images obtained with the two reconstruction methods, and the correlation coefficients with FEV₁% were evaluated. Results: The MTF was more superior for the AiCE lung than for the AiCE body, and the NPS was lower for the AiCE lung than for the AiCE body in the low-frequency region. The correlation between WA% and FEV₁% was slightly stronger in the AiCE lung than in the AiCE body. Conclusion: This study showed that WA% measured from the 7th-generation bronchus using ultra-high-resolution images reconstructed with the AiCE lung strengthens the correlation with FEV₁%. Furthermore, the higher the spatial resolution of the measurement images, the stronger the correlation between WA% and FEV₁%.

Purpose: The purpose of this study was to evaluate the differences in the organ doses and the effective doses using three types of automated organ dose estimation software for low-dose computed tomography (CT) screening for lung cancer and to evaluate the correlations between each dose and size-specific dose estimates (SSDEs). Methods: Seventy-two adults who underwent low-dose CT screening for lung cancer were included, and the organ doses and the effective doses were calculated using each of automated organ dose estimation software. We evaluated differences between software for the organ doses and the effective doses and the correlations between each dose and SSDEs. Results: Differences in organ doses and effective doses were observed among the software. The organ doses showed a strong correlation (r=0.833–0.995) with SSDEs for organs within the scan range. The effective doses showed a strong correlation (r=0.830–0.970) with SSDEs, although there were significant differences among the software. Conclusion: Although the organ doses and the effective doses differed between software, it may be possible to estimate them from SSDEs by using linear regression equations.

This study evaluated accuracy of deformable image registration (DIR) with twelve parameter settings for thoracic images. We used peak-inhale and peak-exhale images for ten patients provided by DIR-lab. We used a prototype version of iCView software (ITEM Corporation) with DIR to perform intensity, structure, and hybrid-based DIR with the twelve parameter settings. DIR accuracy was evaluated by a target registration error (TRE) using 300 bronchial bifurcations and the Dice similarity coefficient (DSC) of the lungs. For twelve parameter settings, TRE ranged from 2.83 mm to 5.27 mm, whereas DSC ranged from 0.96 to 0.98. These results demonstrated that DIR accuracy differed among parameter settings and show that appropriate parameter settings are required for clinical practice.

Purpose: This retrospective study was conducted to evaluate whether the breast density and initial compression pressure affected the compressed breast thickness (CBT) and compression pressure after decompression using compression control function (CCF) during mammography. Methods: Consecutive 779 mammograms obtained from 392 patients between February and October 2019 were included. The initial compression was randomly performed at 110 N–140 N. CCF was set to stop decompression when the pressure decreased to 80 N or restoration of CBT reached 3 mm. If the CCF stopped due to 3 mm or more restoration of CBT, it was defined as goal unachieved. Mammograms were classified into non-dense and dense groups. CBT, ∆P (actual compression pressure after decompression–80 N), and the ratio of goal unachieved were compared between breast density subgroups and among initial compression pressure. Results: CBT was significantly different between non-dense and dense groups both at initial compression (42.3±12.1 mm vs. 27.6±9.7 mm, p<0.001) and after decompression (44.6±12.3 mm vs. 29.7±9.9 mm, p<0.001), but not different based on initial compression pressure. The higher the initial compression pressure, the higher the ∆P. When the initial compression pressure was 130 N and 140 N, ∆P was significantly higher in the non-dense group, −0.1±3.7 N vs. −1.6±2.7 N (p=0.0018) and 2.9±5.8 N vs. 0.4±3.3 N (p<0.001), respectively. Goal unachieved was significantly frequent in the non-dense group (19.6% vs. 13.1%, p=0.016). Conclusion: Breast density and initial compression pressure affected the decompression using CCF. Especially with lower initial compression pressure to the dense breast, decompression using CCF was successfully performed.