Aim: The aim of this work was to evaluate the coincidence between light and X-ray field width in air. Background: Light fields are often used for confirmation of irradiation position to superficial tumors and final confirmation of the patient’s irradiation position. To guarantee collation by the light field, the light and X-ray fields must coincide. Currently, the light field width is determined mainly by visual evaluation using manual methods, such as use of graph paper and rulers. The light field width is difficult to visually recognize a definite position at the edge of the light field. Materials and Methods: We quantified the width of light fields emitted from a linear accelerator using a light probe detector and compared the results with those of X-ray fields. In-air measurements were conducted at the same position in the light field with the light probe detector and X-ray field using an ionization chamber installed in an emptied three-dimensional water phantom. Results: The radiation field in air was approximately 2 mm larger than the light field, and we found some influence of transmission and scattered rays on the penumbra region. Before and after exchanging crosshair sheets, the fields also exhibited differences in uniformity. Conclusions: The proposed method quantifies the light field using a photodetector and can be used to compare the light field with the X-ray field, conforming a useful tool for evaluating the accuracy of treatment devices in an objective and systematic manner.

The purpose of this study was to calculate statistically significant patient data and test bolus (TB) parameters in order to predict the contrast enhancement of main bolus (eMB) in coronary computed tomography (CT) angiography, and to create a predictive model of eMB with the calculated parameters by machine learning. A total of 126 patients underwent coronary CT angiography. Contrast material was administered at a fixed injection rate and volume. The peak enhancement (PE) and the time needed to reach peak (TP) of the TB were calculated for each patient. The dependency of MB contrast attenuation on these parameters was evaluated. Significant correlations were obtained among PE, TP, and the patient body surface area (BSA) with the eMB. The coefficient of determination of the linear regression model to estimate eMB by machine learning using the above three variables was 0.70 for the training data and 0.55 for the test data. For comparison, the coefficient of determination of the model using only BSA was 0.55 for the training data and 0.36 for the test data; the accuracy of the model created during this time was confirmed.

Although the test bolus tracking method is available as a predicting method of scan timing in the coronary computed tomography (CT) angiography, it is known that there is a problem of scan timing due to the use of a part of test bolus method. The diluted test bolus method was adopted for test bolus, and as a result of using in combination with the test bolus tracking method, it showed a higher contrast enhancement compared with the test bolus tracking method; a stable contrast enhancement with less variation in CT number was obtained. The CT number at the peak in the test scan and the CT number of the main scan showed a high correlation. The contrast injection technique using the diluted test bolus method and the test bolus tracking method is a useful method in the coronary CT angiography. We named this contrast injection technique diluted test bolus tracking method.

Purpose: Dynamic C-arm computed tomography perfusion (C-arm CTP) is a newly developed application that can provide cerebral perfusion images in the angio suite, similar to conventional multi-detector CTP in a diagnostic room. We introduce the workflow of C-arm CTP acquisition and our initial experience in a clinical setting. Method: C-arm CTP was acquired with 40 ml of non-diluted contrast medium injected at 4 ml/s in the median cubital vein followed by 30 ml of saline injected at the same rate. The injection began 5 seconds after the acquisition was started. Two mask runs were followed with eight successive fill runs. Arterial input function was automatically calculated to deliver perfusion maps. Incidence of acquisition errors was evaluated in two phases. Result: C-arm CTP images were successfully acquired in all cases, and the images provided useful information under a stable examination protocol. However, we experienced some operational and systematic artifacts that degraded image quality of perfusion maps in Phase 1. The incident rate of errors was significantly improved in Phase 2. Conclusion: C-arm CTP acquisitions were feasible during acute stroke treatment in the angio suite. It is expected that the image quality will be further improved through process improvement and reconstruction setting optimization to minimize unexpected artifacts in individual cases.

The purpose of this study was to improve the contrast between the nerves and blood by reconsidering the imaging parameters of the sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) method, and to compare it with conventional methods, including the constructive interference in steady state (CISS) and T₂-weighted SPACE (T2-SPACE) methods. In the phantom study, the repetition time (TR), echo time (TE), flip angle (FA), and turbo factor (TF) of SPACE were varied using the restore pulse. The parameters for which the nerve-blood contrast (C1) and cerebrospinal fluid-nerve contrast (C2) were equal were selected. Though multiple conditions resulted in C1 and C2 equivalence, we determined/set the TR=500 ms, TE=21 ms,  FA=120°, and  TF=30, considering the acquisition time, specific absorption rate (SAR), and artifacts. This sequence was called “short TR and short TE SPACE with restore pulse (SSSR)”. In the phantom and healthy volunteer studies, the contrast between the nerves and blood in the SSSR method was statistically superior in both the physical and visual assessments compared with conventional methods. In the healthy volunteer study, C1 improved from 0.08 for CISS and 0.18 for T2-SPACE to 0.43 for SSSR. This is because the nerve signals in conventional methods were low due to the heavy T₂-weighted, while those in the SSSR method were high due to the short TE and effect of the restore pulse. In conclusion, the contrast between the nerves and blood was significantly higher in the SSSR method compared with conventional methods.

It is necessary to verify an intensity-modulated radiation therapy (IMRT) plan and to confirm dose error within the tolerance, in order to perform it securely and precisely. IMRT with dynamic multi-leaf collimator (DMLC) requires high DMLC position accuracy. The DMLC position accuracy analysis software DynaLog File Viewer (DFV; Varian Medical Systems, Palo Alto, CA, USA) is used to analyze position errors of DMLC for IMRT plans. We analyzed correlation between DMLC parameters and position error of DMLC obtained from DFV in prostate IMRT. A regression analysis of the position error and the DMLC parameters was performed. As a result, a strong correlation was found between MLC position error and each of the DMLC parameters: leaf speed, gap width, and segment monitor unit (MU). We found the factors for the DMLC position error in this study. DMLC position error could be estimated from leaf speed, gap width, and segment MU when we analyze IMRT cases in the further study.

It is important to optimize the exposure dose when conducting interventional radiology, but optimization is difficult for medical centers to achieve independently. In 2005, we administered a questionnaire on the measurement of dose rates and awareness of exposure reduction when performing percutaneous coronary intervention. Ten years later, we conducted a follow-up survey of the same 31 centers to determine the current situation and identify trends. The results of the survey showed that the mean fluoroscopy dose rate decreased to 55% of the 2005 value, from 28.2 to 15.6 mGy/min, and the mean radiography dose rate decreased to 71% of the 2005 value, from 4.2 to 3.0 mGy/s. Dose rates for both fluoroscopy and radiography decreased by 84% of facilities. The results also indicated greater cooperation by physicians compared to 10 years ago. In particular, there was a considerable increase in the exchange of ideas with physicians regarding exposure, suggesting a stronger level of interest in exposure. The overall score for questionnaire items was 33% higher than that in the previous survey. These results show that in the past 10 years, awareness of exposure reduction has improved, and dose optimization has been a major factor in the downward trend in dose rates in radiography and fluoroscopy.

Purpose: The aim of this study was to clarify the artifacts that occurred in the non-activity signal with computed tomography (CT)-based attenuation correction (CTAC) error due to image misregistration. Methods: We used a cylindrical phantom containing a test tube with a diameter of 15 mm as the non-activity signal part. Positron emission tomography (PET) images were acquired for 30 minutes using the phantom with water in the non-activity signal part and ¹⁸F-fluoro-2-deoxy-d-glucose (¹⁸F-FDG) (5.3 kBq/ml) in the background area. CT scanning was performed by replacing the water with contrast agents at different dilutions to obtain arbitrary CT numbers (–1000 to 1000). The PET images were attenuation-corrected individually by the CT images in which the CT number of the non-activity signal part had changed. The relationship between the CT numbers and the CTAC artifact was determined by measuring the PET value in the non-activity signal part of the PET images and comparing C_i. Results: As the CT number of the CT images increased, C_i of the artifact increased. The CT number and C_i had a correlation of y=1.48x+2.86×10³ (R² =0.99) when CTAC was performed in units of CT numbers above 0 for PET data of water (0 HU) and a correlation of y=3.15x+6.26×10³ (R² =0.97) when CTAC was performed in units of CT numbers below 0 for PET data of air (–1000 HU). Although the original CT image was air, the artifacts due to CTAC errors with different Hounsfield units showed larger changes. In particular, positive artifacts were recognized in the PET images after CTAC depending on the Hounsfield units. Conclusions: When the CT number was different from the original in CTAC, the PET value was different. CTAC should be performed with caution as there may be image misregistration.