Purpose: The aim of this study was to evaluate the usefulness of combining post-processing scatter correction (IG) and an anti-scatter grid (RG) in chest radiography. Method: To determine the combination protocol (Hyb) that was closed to RG 12:1 (RG12), we measured the content rate of scattered radiation for each combination (RG12, IG12, RG3–12+IG3–12). Task-based modulation transfer function (MTF_Task) and SDNR were evaluated using RG12, IG12, and Hyb. Additionally, seven radiologists performed visual evaluation by using chest phantom. Result: The protocol of Hyb was RG8+IG3. In SDNR, Hyb (RG8+IG3) was equal to or higher than RG12, and MTF_Task was equal in all grid systems. Hyb (RG8+IG3) was significantly superior to RG12 in visual evaluation. Conclusion: The combining post-processing scatter correction should be useful for improving inspection throughput and reducing the risk of gridʼs damage.
Purpose: This study aimed to compare the contrast effects of administration via the subclavian vein, the superior vena cava and right ventricular venous tract, and the aorta in three-dimensional computed tomography angiography (3D-CTA) using one-step injection and trapezoidal cross-injection. Method: The subjects were 56 patients who underwent aortic 3D-CTA. In the one-step injection method, a 30-second injection of contrast medium was followed by saline injected at the same rate as the 30-ml contrast medium. In the trapezoidal cross-injection method, after injecting the contrast agent for 15 seconds, a variable mixture of the contrast agent and saline was injected for 15 seconds, followed by 20 ml saline injected at the same rate as the initial contrast agent injection. CT values were measured in the subclavian vein, superior vena cava, right ventricle, and aorta. Result: A significant difference was found in the subclavian vein and right ventricle, with the trapezoidal cross-injection method showing a lower CT value than the one-step injection method (p<0.01). There were no significant differences in the CT values in the superior vena cava and the aorta. Conclusion: The trapezoidal cross-injection method for aortic 3D-CTA produced lower CT values in venous pathways than those via the one-step injection method, but no changes were observed in the aortic CT values. These results suggest that the trapezoidal cross-injection method is useful in aortic 3D-CTA.
Magnetic resonance angiography (MRA) using ultra-short TE (uTE) is known to be used for the evaluation of cerebral aneurysm after treatment such as clipping and coiling. However, conventional uTE sequences are not appropriate as an additional imaging sequence for 3D time-of-flight (TOF)-MRA because it is not possible to shorten scan time and acquire selective-volume imaging. To solve the problem, we focused on the combination of uTE sampling and 3D radial scan sequences. In this study, we examined the optimal imaging parameters of the proposed uTE-MRA. A simulated blood flow phantom with stents (Enterprise) and titanium clips (YASARGIL) was used for optimizing the TR, flip angle (FA), and radial percentage. The signal intensity in the simulated vessel was measured in each imaging condition, and the ratio of the presence or absence of a stent was evaluated as a relative in-stent signal (RIS). In addition, the diameter of the signal loss of the simulated artery was measured for each imaging condition, and signal loss length (SLL) of a clip was calculated from the average value. The RIS improved with increasing the FA and shortening the TR, but it did not change by changing the radial percentage. The SLL became smaller at the coil as the FA increased, but there was no significant difference between the intersection and the blade. There was also no significant difference between TR and radial percentage. The effective imaging conditions for uTE-MRA to improve the vascular description of the evaluation after treatment of cerebral aneurysms with metallic devices were those with large FA and short TR.
Purpose: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data. Methods: We obtained Z-score maps for 100 cases each of simulated Alzheimerʼs disease (AD), simulated dementia with Lewy bodies (DLB), and simulated normal cognition (NC) by performing statistical analysis of the simulation data that provided defects and healthy patient data. The clinical images were determined by reference to radiological reports, and Z-score maps of AD (n=33), DLB (n=20), and NC (n=28) were used. A network was constructed with reference to AlexNet, 4-fold cross-validation was performed using only simulation data, and classification accuracy was evaluated. We also trained the model using the simulation data and classified the clinical images. Results: The accuracy rate of classification between simulations was 96.2% and that of the clinical images was 84.2%. Conclusion: Through deep learning using simulation data, clinical images may be classified with an accuracy of 84.2%.