We hypothesized that hippocampal-sparing radiotherapy via volumetric modulated arc therapy (VMAT) could preserve the neurocognitive function (NCF) of patients with primary brain tumors treated with radiotherapy. We reviewed data from patients with primary brain tumors who underwent hippocampal-sparing brain radiotherapy via VMAT between February 2014 and December 2015. The optimization criteria for the contralateral hippocampus was a maximum dose (Dmax) of less than 17 Gy. For NCF evaluations, the Seoul Verbal Learning Test for total recall, delayed recall, and recognition (SVLT-TR, DR, and Recognition) was performed at baseline and at seven months after radiotherapy. A total of 26 patients underwent NCF testing seven months after radiotherapy. Their median age was 49.5 years (range 26–77 years), and 14 (53.8%) had grade III/IV tumors. The median Dmax to the contralateral hippocampus was 16.4 Gy (range 3.5-63.4). The median mean dose to the contralateral hippocampus, expressed as equivalent to a 2-Gy dose (EQD2/2), was 7.4 Gy2 (0.7–13.1). The mean relative changes in SVLT-TR, SVLT-DR, and SVLT-Recognition at seven months compared to the baseline were − 7.7% (95% confidence interval [CI], − 19.6% to 4.2%), − 9.2% (95% CI, − 25.4% to 7.0%), and − 3.4% (− 12.7% to 5.8%), respectively. Two patients (7.7%) showed deteriorated NCF in the SVLT-TR and SVLT-DR, and three (11.5%) in the SVLT-Recognition. The mean dose of the left hippocampus and bilateral hippocampi were significantly higher in patients showing deterioration of the SVLT-TR and SVLT-Recognition than in those without deterioration. The contralateral hippocampus could be effectively spared in patients with primary brain tumor via VMAT to preserve the verbal memory function. Further investigation is needed to identify those patients who will most benefit from hippocampal-sparing radiotherapy of the primary brain tumor.

Background: Computed Tomography (CT) is a technology that utilizes X-rays and offers several benefits in the medical field, but it comes with the drawback of radiation exposure. Radiation exposure poses risks, and extensive research is being carried out on CT to mitigate this concern. Artificial intelligence (AI)-based approaches have been introduced recently, including the application of AI-driven super-resolution (SR) techniques. However, these methods are not widely adopted, and the standards for dose reduction remain insufficient. Hence, this study aimed to assess the effectiveness of AI-based SR techniques for dose reduction. Results: The experiment utilized the Lung man phantom, acquiring images by reducing the CT dose index volume (CTDI_vol) by 25% from a baseline of 3.0 mGy. These images were then processed using AI-based SR models, super-resolution convolutional neural network (SRCNN) and very deep super-resolution (VDSR), to obtain the results. After learning, the results from 50% CTDI_vol were generally superior to those from 25% CTDI_vol. However, there was no significant difference between the outcomes from 50% CTDI_vol and 75% CTDI_vol. The best outcomes in this experiment were achieved by SRCNN, with a peak signal-to-noise ratio (PSNR) of 33.52 and structural similarity index measure (SSIM) of 0.878, and by VDSR, with a PSNR of 35.532 and an SSIM of 0.894. Conclusions: This study aimed to evaluate the impact of dose reduction and presented AI-based SR results that vary with reduced doses. Conducted from the standpoint of dose change, the study is expected to serve as foundational data for future research in this area.

Energy window selection is a critical parameter for optimizing planar gamma image quality in nuclear medicine. In this study, we developed dedicated nuclear medicine phantoms using 3D printing technology to evaluate the impact of varying energy window levels on image quality. Three types of phantoms—a Derenzo phantom with six different sphere diameters, a modified Hoffman phantom incorporating lead for attenuation, and a quadrant bar phantom with four bar thicknesses constructed from bronze filament—were fabricated using Fusion 360 and an Ultimaker S5 3D printer with PLA and bronze-based materials. Planar images were acquired using 37 MBq of Tc-99m for 60 s at energy windows centered at 122, 140, and 159 keV. Quantitative assessments included contrast-to-noise ratio (CNR), coefficient of variation (COV), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), comparing all images with the 140 keV image as the reference. The results showed a consistent decline in image quality at 122 keV and 159 keV, with the highest CNR, lowest COV, and optimal PSNR/SSIM values obtained at 140 keV. In visual analysis using the quadrant bar phantom, thinner bars were more clearly discernible at 140 keV than at other energy levels. These findings demonstrate that the application of an appropriate energy window—particularly 140 keV for Tc-99m—substantially improves image quality in planar gamma imaging. The use of customized, material-specific 3D-printed phantoms also enables flexible, reproducible evaluation protocols for energy-dependent imaging optimization and quality assurance in clinical nuclear medicine.

The purpose of this study was to compare the difference in peak velocity (PV) and average flow (AF) values between the shimming of the aortic arch (AA) and the left ventricle (LV) using the 2D phase contrast (PC) technique at 1.5 Tesla and 3.0 Tesla. The sino-tubular junction (S-T junction), the proximal AA, and the LV, an aortic valve, were examined using a 2D PC technique at 1.5 Tesla and 3.0 Tesla. At 1.5 Tesla and 3.0 Tesla, 2D PC technique was used to examine the proximal AA Sino-Tubular (S-T) junction and LV was examined at the aortic valve area. shim was not used (no_Shim) in 1.5 Tesla and 3.0 Tesla to compare differences in heart blood flow due to magnetic field strength. To assess the difference due to shimming, an auto_shim, target_shim was used and an all_shimthat used auto and target shim simultaneously was used. The average value between 1.5 Tesla and 3.0 Tesla as a result of inspection was different when AA_AF was no_shim and all_shim. In LV_AF, the difference in mean values was found in 1.5 Tesla and 3.0 Tesla when it was no_shim and all_shim. Other tests did not show any significant differences. Also, according to the shimming method in 1.5 Tesla, the mean value difference occurred in AA_AF and the mean value difference in LV_AF in 3.0 Tesla. In conclusion, it is necessary to improve uniformity of magnetic field through shimming for accurate blood flow evaluation in 1.5 Tesla and 3.0 Tesla. Therefore, this study is expected to be used as a basis for improving the uniformity of magnetic field.

The purpose of this study was to analyze the actual reflection of the biopsy needle when performing computed tomography (CT) guided biopsy in lung cancer. CT used in the experiment was a 128-channel...