Purpose: This study aimed to clarify the effects of radiopharmaceuticals and buffer solutions on the accuracy of the Hoffman phantom examination. Methods: The Hoffman phantom was prepared using the water immersion method and was injected with 3 different solutions: ¹²³I-MIBG, ¹²³I-IMP only, and ¹²³I-IMP plus buffer solution. Single photon emission computed tomography (SPECT) / computed tomography (CT) imaging was performed at 3-time points: immediately after encapsulation, after 10 min of stirring, and after 20 min of stirring. The relative SPECT counts and left–right ratios of the images obtained under each condition were evaluated. Results: The results revealed that the buffer solution facilitated the mixing of ¹²³I-IMP, affecting the initial distribution of the radiopharmaceutical. However, after 20 min of stirring, no significant differences were observed in the relative SPECT counts and the left–right ratio of SPECT images among the different solutions, regardless of the radiopharmaceutical type or the presence of the buffer, in most regions. Conclusion: Buffer solution promotes the mixing process; however, it was confirmed that sufficient agitation alone can produce comparable SPECT images, indicating that the use of a buffer may not be necessary if proper agitation is ensured during phantom preparation.

Purpose: The purpose of this study was to propose a method for measuring the maximum leaf velocity (V_max) of the multileaf collimator (MLC) in a dynamic MLC irradiation. Methods: The irradiation was carried out with a plan in which the MLC leaves were constantly and gradually accelerated. Based on this plan, it was assumed that the velocity of each leaf v(t) (t is the elapsed time) would initially increase but plateau once it reached its maximum velocity. In the proposed method, v(t) was calculated from the log file data during irradiation, and fitted by a piecewise linear function consisting of 2 linear segments (constant acceleration and constant velocity segments); V_max was determined as the velocity in the constant velocity segments. The V_max values in each accelerator were obtained periodically for 7 months (20 measurements in total). Results: In all measurements, the constant acceleration and constant velocity segments in v(t) were clearly distinguished by the piecewise linear approximation, and the V_max was determined. The mean V_max value of each leaf ranged from 3.63 to 4.32 cm/s with standard deviations (SD) less than 0.04 cm/s. Conclusion: The proposed method made it possible to confirm the long-term stability of the V_max easily.

Purpose: To propose a method for generating simulated images that are used to produce a contrast detail (C-D) diagram in X-ray computed tomography (CT). Methods: We numerically generated object functions assuming ideal spheres of 7 diameters and 9 contrasts between sphere density and background density. Sphere images were calculated from the object functions by the simulation technique of image blurring based on the 3-dimensional spatial resolution measured in the CT system. The calculated sphere images were added in images of the uniform water-equivalent section of a phantom acquired at different tube currents. Obtained images were arranged according to the sphere diameter and contrast and were applied to produce the C-D diagram. For each tube current, the C-D diagram was created by a board-certified diagnostic radiologist. Results: The calculated sphere images showed good agreements with actually acquired sphere images of the phantom. Standard deviations (SDs) in subtraction images of them were equivalent to the SD in the background region of the actual image, suggesting the accuracy of the calculated sphere images. The obtained C-D diagram showed that smaller spheres and spheres with lower contrast tended to be detected as the tube current increased. Conclusion: The proposed method was suggested to be feasible for producing the C-D diagram for CT.

Purpose: To evaluate the setup accuracy of surface-guided postmastectomy radiotherapy (PMRT) under deep inspiration breath hold (DIBH). Methods: Fourteen patients with left-sided breast cancer who underwent PMRT under DIBH were enrolled in this study. The setup error was defined as the difference in the position of clinical target volume between the planning computed tomography (CT) and the cone-beam CT acquired after surface-guided setup. Results: The mean±standard deviation of setup error was 0.2±2.1 mm, −0.1±3.5 mm, and 0.4±2.1 mm in the anterior-posterior, superior-inferior, and right-left directions, respectively, and −0.1±0.8°, 0.5±0.9°, and 0.9±1.0° in the Yaw, Roll, and Pitch angles. Conclusion: The setup accuracy of PMRT under DIBH using a surface-guided system was within 5 mm in 82% of all treatment fractions.

Purpose: This study aims to compare the effects of two types of deep learning (DL) techniques on brain CT values, image noise content, and contrast-to-noise ratio (CNR) between white and gray matter in low-noise head CT images, along with adaptive iterative dose reduction 3D (AIDR 3D). Methods: Twenty-one normal patients with no abnormal findings who underwent head CT for identification of acute illness were included in the study. DL techniques used were Advanced intelligent Clear-IQ Engine (AiCE, Canon Medical systems, Tochigi, Japan) and PixelShine (FUJIFILM Medical, Tokyo, Japan). We performed CT value measurements of 26 cerebrum regions, image noise measurements, and CNR calculations. We also conducted a visual assessment of image noise and white matter–gray matter contrast on a 5-point scale. Results: Image noise significantly decreased with DL techniques. CT values changed significantly with AiCE. CNR for white matter–gray matter was the highest with PixelShine (P<0.01). The visual assessment of white matter–gray matter contrast was the highest for PixelShine and the lowest for AiCE (P<0.01). Conclusion: While DL techniques reduce image noise, there are differences in CT values and visual impression, especially white matter–gray matter contrast, so care should be taken when using it.