Radiological Physics and Technology最新文献

In ²⁰¹Tl myocardial perfusion single-photon emission computed tomography (SPECT), gastric wall uptake can impact the inferior wall. This study aimed to evaluate the effectiveness and usefulness of the masking on un-smoothed image (MUS) method for ²⁰¹Tl myocardial perfusion SPECT. A hemispherical gastric wall phantom was created to simulate the gastric fundus located closest to the myocardium, and the activity was enclosed to achieve an SPECT count ratio against the myocardium equivalent to that observed in clinical practice. The minimum values of the defect chip in the circumferential profile curve were compared for six SPECT count ratios and seven gap distances. In the conventional method, increasing SPECT count ratios or gap distances interfered with myocardial perfusion SPECT evaluation. Artifacts were less apparent when the MUS method was applied. The MUS method effectively suppressed the gastric wall uptake on ²⁰¹Tl myocardial perfusion SPECT.

We aimed to develop a technique to precisely measure the shrinkage of immobilization sheets (ISs) using a three-dimensional (3D) tracking of multiple points on IS using a near-infrared camera. A thermoplastic sheet and an elastomer sheet were used in this study. The inter-marker distance (IMD) of neighboring marker pairs and the triangular area (TA) formed by neighboring three markers were analyzed as a function of time since molding each IS. Thermal distance ratio (TDR), IMD normalized to IMD after 48 h, and thermal area ratio (TAR), TA normalized to TA after 48 h, were analyzed using an exponential function. The 3D visualization of the initial shrinkage amplitude (ISA) was created for each IS. The mean ISA and the time constant (TC) in the exponential function (ISA, TC) for horizontal and vertical pairs were (0.34 $\pm$ 0.06, 3.9 $\pm$ 0.9) and (0.60 $\pm$ 0.05, 7.6 $\pm$ 0.9) for HFT and (0.13 $\pm$ 0.02, 25.0 $\pm$ 4.7) and (0.06 $\pm$ 0.03, 4.7 $\pm$ 3.5) for SF, respectively. The mean (ISA, TC) for HFT and SF were (0.76 $\pm$ 0.07, 7.1 $\pm$ 0.9) and (0.22 $\pm$ 0.03, 17.7 $\pm$ 4.2), respectively. Horizontal pairs showed smaller ISA and shorter TC than vertical pairs for HFT, while horizontal pairs showed larger ISA and longer TC than vertical pairs for SF, possibly due to different chemical characteristics of each material under the effect of mechanical force. The mean TDR and TAR are considered useful for evaluating the gross property of IS. The visualized distributions of ISA are considered useful to provide spatial information for investigating relationships between actual handlings and shrinkage of IS.

Image quality, in addition to radiation dose, is the most important physical parameter in digital mammography. Image quality should be periodically monitored using the CDMAM phantom. The aim of this study is to investigate the effect of the number of analyzed images on the result of threshold image contrast measurements using the CDMAM phantom in different versions. The images obtained using two versions of the CDMAM phantom, i.e., 3.4 and 4.0, were analyzed. The image analysis was performed and repeated 10 times for 2, 4, 6, 8, 12, 16, 24, and 32 images from a pool of 43 images, separately for each phantom. For the CDMAM 3.4 phantom, a statistical difference was demonstrated between the following groups: S2 vs S6 (p < 0.006), S6 vs S16 (p < 0.001), S6 vs S24 (p < 0.002), S6 vs S32 (p < 0.021), S8 vs S16 (p < 0.019), S8 vs S24 (p < 0.048). For the CDMAM 4.0 phantom, a statistically significant difference was demonstrated between all groups and the N2 group (p < 0.000). For the CDMAM 3.4 phantom, the most favorable number of images required for analysis cannot be clearly determined. For the CDMAM 4.0 phantom, it is recommended to perform 24 images for analysis. Particular attention should be paid when determining the threshold image contrast for a disk diameter of 0.1 mm, as this parameter is used during exposure automation control.

In therapy with Synchrony® mounted on Radixact®, the fiducial marker (FM) and adrenal gland metastasis, which shift with respiratory phase, require margin compensation for high-dose prescriptions. Although compensation is critical, no studies have examined the margin to compensate for the respiratory phase shift. Therefore, we aimed to suggest the compensating margin for the FM and adrenal metastasis shift along with respiratory phase. We used images from four-dimensional computed tomography (4DCT) taken twice and gated CT taken once before therapy initiation with available contour data for FM and adrenal gland metastasis in each image. The distance between the FM and the center of the gross tumor volume (GTV) in each image of a ten-set 4DCT was defined as the correlating association, and a relative cumulative frequency distribution was created based on it. The values of the margins compensating for respiratory displacement were obtained from the relative cumulative frequency distribution in the right-left/posterior-anterior/superior-inferior (S-I) directions. In cases wherein the FM was placed inside the GTV, the margin values decreased in the S-I direction.

To quantify radiation dose reduction in radiotherapy treatment-planning CT (RTCT) using a deep learning-based reconstruction (DLR; AiCE) algorithm compared with adaptive iterative dose reduction (IR; AIDR). To evaluate its potential to inform RTCT-specific diagnostic reference levels (DRLs). In this single-institution retrospective study, 4-part RTCT scans (head, head and neck, lung, and pelvis) were acquired on a large-bore CT. Scans reconstructed with IR (n = 820) and DLR (n = 854) were compared. The 75th-percentile CTDI_vol and DLP (CTDI_IR, DLP_IR vs. CTDI_DLR, DLP_DLR) were determined per site. Dose reduction rates were calculated as (CTDI_DLR - CTDI_IR)/CTDI_IR × 100% and similarly for DLP. Statistical significance was assessed by the Mann-Whitney U-test. DLR yielded CTDI_vol reductions of 30.4-75.4% and DLP reductions of 23.1-73.5% across sites (p < 0.001), with the greatest reductions in head and neck RTCT (CTDI_vol: 75.4%; DLP: 73.5%). Variability also narrowed. Compared with published national DRLs, DLR achieved 34.8 mGy and 18.8 mGy lower CTDI_vol for head and neck versus UK-DRLs and Japanese multi-institutional data, respectively. DLR substantially lowers RTCT dose indices, providing quantitative data to guide RTCT-specific DRLs and optimize clinical workflows.

Rotational cerebral angiography requires accurate dosimetry. The National Cancer Institute Dosimetry System for Radiography and Fluoroscopy (NCIRF), a Monte Carlo-based dosimetry software, can evaluate the organ dose (OD) and effective dose (ED) with higher accuracy than the conventional Monte Carlo software (PCXMC). We estimated the OD and ED for three-dimensional digital subtraction angiography (3D-DSA) and cone beam computed tomography (CBCT) using the NCIRF, reflecting dose variations during rotational cerebral angiography. The 3D-DSA and CBCT simulation parameters were obtained by rotational imaging of a physical head phantom using the Artis Q biplane system. The air kerma area product for each projection was determined based on the ratio of the tube current-time product for each projection; the NCIRF was used with male and female voxel-type reference computational phantoms. To validate the simulation results, the lens dose of the phantom was measured using radiophotoluminescence glass dosimeters and compared to the simulated lens dose. The highest ODs were delivered to the brain: 8.8 mGy (males) and 11.6 mGy (females) in 3D-DSA and 50.0 mGy (males) and 59.4 mGy (females) in CBCT. The EDs were 0.27 mSv (males) and 0.35 mSv (females) in 3D-DSA and 1.49 mSv (males) and 1.83 mSv (females) in CBCT. Lens doses differed within 8.0% between measurements and simulations, with 45.9-65.5% overestimation in simulations that did not account for dose variability. Simulations that considered dose variability using the NCIRF more accurately estimated OD and ED in rotational cerebral angiography.

This study quantitatively evaluated the impact of differences in computed tomography (CT) numbers and elemental compositions between commercially available brain-tissue-equivalent density plugs (BDPs) and actual brain tissue on dose calculations in a radiation therapy treatment planning system (RTPS). The mass density and elemental composition of BDP were analyzed using elemental analysis and X-ray fluorescence spectroscopy. The CT numbers of the BDP and actual brain tissue were measured and compared, with effective atomic numbers (EANs) calculated based on compositional analysis and the International Commission on Radiological Protection Publication 110 data for brain tissues. The theoretical CT numbers were derived using the stoichiometric CT number calibration (SCC) method. The dose calculations were performed using the modified CT number-to-relative electron density (RED) and mass density (MD) conversion tables in Eclipse v16.1, employing AAA and Acuros XB algorithms, employing the physical material table in AcurosXB_13.5. The dose metrics D_2%, D_50%, and D_98% were evaluated. Significant differences in elemental composition were found, particularly in carbon (73.26% in BDP vs. 14.3% in brain tissue) and oxygen (12.52% in BDP vs. 71.3% in brain tissue). The EANs were 6.6 for BDP and 7.4 for brain tissue. The mean CT numbers were 23.30 HU for the BDP and 37.30 HU for brain tissue, a 14 HU discrepancy. Nevertheless, dose calculation deviations were minimal, typically within ± 0.2%, with a maximum discrepancy of 0.6% for D_98%. Although CT numbers and elemental compositions exhibited notable differences, their impact on dose calculations in the evaluated RTPS algorithms was negligible.

Clinical measurement of the maximum slope (MS) using ultrafast dynamic contrast-enhanced (UF-DCE) breast magnetic resonance imaging (MRI) is typically performed by placing a region of interest (ROI) in the most enhanced area within a lesion. However, previous studies have not clarified whether visually identified enhanced areas consistently exhibit the highest MS values. These ROI-based MS measurements require MS maps to ensure appropriate ROI placement. However, generating MS maps requires specialized software capable of pixel-by-pixel MS calculations, which are available only at a few facilities. Therefore, this study proposed a simplified method for generating MS maps. This method involves subtracting consecutive UF-DCE images, applying temporal maximum intensity projection, normalizing the resulting image by dividing it by the pre-contrast image signal intensity, and converting it to a slope by dividing it by the temporal resolution. The MS maps generated using the proposed method were compared with those obtained using a robust pixel-by-pixel curve-fitting method, in addition to the final-phase UF-DCE images. In all cases with breast lesions (n = 13), the signal intensity distributions on the proposed MS maps closely resembled those on the curve-fitting maps, with a significantly higher similarity than those on the final-phase UF-DCE images (p < 0.001). The derived mean absolute error of MS values after regression-based modification was 0.78 ± 0.72 (%/s). The proposed method improves the reliability of ROI placement in conventional ROI-based MS measurements and supports the direct quantification of MS values from map pixel data.

The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom. This visual evaluation requires observation of the entire image of the phantom and confirmation of the absence of apparent artifacts. Because the evaluation is visually performed, inter-observer differences might exist among evaluators for difficult cases. Therefore, the workload of the staff who perform approval tasks must be reduced, and objective evaluation methods are needed. Thus, we attempted to develop an artificial-intelligence-based objective method for anomaly detection. Three artificial intelligence methods for anomaly detection were developed, and their accuracy was evaluated using AutoEncoder, AnoGAN, and a method combining feature extraction using AlexNet and a one-class support vector machine. In total, 10,207 normal images from 128 facilities and 594 abnormal images from eight facilities, all of which were submitted as part of application for amyloid PET certification, were used. Group five-fold cross-validation was employed for artificial intelligence training and evaluation. In addition, the performance of each artificial intelligence method was assessed using receiver operating characteristic analysis. The areas under the curve for anomaly detection using AutoEncoder, AnoGAN, and the method combining feature extraction using AlexNet and a one-class support vector machine were 0.80 ± 0.04, 0.77 ± 0.03, and 0.99 ± 0.01, respectively. Artificial intelligence effectively distinguished between normal and abnormal images with high accuracy. In the future, its practical implementation is anticipated to reduce the workload in the approval work for the Japanese site qualification program for amyloid PET.