Radiological Physics and Technology最新文献

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10^-3 mm²/s at 0 °C) was imaged at various b-values (0, 1000, 2000, and 4000 s/mm²) using a 3 T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0 mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000 s/mm². Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm²; however, its effectiveness was diminished at 4000 s/mm². There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5 mm and combined b-values of 0 and 4000 s/mm², the ADC values were 0.97 × 10^-3and 0.98 × 10^-3mm²/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.

The purpose of this study was to evaluate, using simulated images with known property values, how accurately some computer applications for calculating modulation transfer function (MTF), task transfer function (TTF), or noise power spectrum (NPS) in computed tomography (CT) based on widely known techniques produce their results. Specifically, they were three applications applicable to the wire method for MTF calculation, two applications corresponding to the circular edge (CE) and linear edge (LE) methods for TTF, and one application using a two-dimensional Fourier transform for NPS, which are collectively integrated with the software 'CTmeasure' provided by the Japanese Society of CT Technology. Images for the calculation with radial symmetry were generated based on a roll-off type filter function. The accuracy of each application was evaluated by comparing the calculated property with the true one. The calculated MTFs for the wire method accurately matched the true ones with percentage errors of smaller than 1.0%. In contrast, the CE and LE methods presented relatively large errors of up to 50% at high frequencies, whereas the NPS's errors were up to 30%. A closer investigation revealed, however, that these errors were attributable not to the applications but to the insufficiencies in the measurement techniques commonly employed. By improving the measurement conditions to minimize the effects of the insufficiencies, the errors notably decreased, whichvalidated the calculation techniques in the applications we used.

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF_10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner.

Previous radiation protection-measure studies for medical staff who perform X-ray fluoroscopy have employed simulations to investigate the use of protective plates and their shielding effectiveness. Incorporating directional information enables users to gain a clearer understanding of how to position protective plates effectively. Therefore, in this study, we propose the visualization of the directional vectors of scattered rays. X-ray fluoroscopy was performed; the particle and heavy-ion transport code system was used in Monte Carlo simulations to reproduce the behavior of scattered rays in an X-ray room by reproducing a C-arm X-ray fluoroscopy system. Using the calculated results of the scattered-ray behavior, the vectors of photons scattered from the phantom were visualized in three dimensions. A model of the physician was placed on the directional vectors and dose distribution maps to confirm the direction of the scattered rays toward the physician when the protective plate was in place. Simulation accuracy was confirmed by measuring the ambient dose equivalent and comparing the measured and calculated values (agreed within 10%). The directional vectors of the scattered rays radiated outward from the phantom, confirming a large amount of backscatter radiation. The use of a protective plate between the patient and the physician's head part increased the shielding effect, thereby enhancing radiation protection for the physicians compared to cases without the protective plate. The use of directional vectors and the surrounding dose-equivalent distribution of this method can elucidate the appropriate use of radiation protection plates.

This study aims to evaluate the effect of pre-reconstruction process for low tube voltage computed tomography (CT) on image quality of filtered back projection (FBP) reconstruction. Small and large quality assurance water phantoms (19 and 33 cm diameter) were scanned on a third-generation dual-source CT with 70 kVp and 120 kVp at various dose levels. Image quality was assessed in terms of the noise power spectrum (NPS) and task-based transfer function (TTF). NPSs and TTFs in the small phantom were comparable between 70 and 120 kVp protocols. In the large phantom, the curves of the NPS changed and the TTF decreased even at the high-dose levels for 70 kVp protocol compared to 120 kVp protocol. Our results indicated that the pre-reconstruction process is performed in low tube voltage CT for large objects even for the FBP reconstruction and has an effect on the image quality.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.

Solid-state detectors (SSDs) may be used along with a lead collimator for half-value layer (HVL) measurement using computed tomography (CT) with or without a tin filter. We aimed to compare HVL measurements obtained using three SSDs (AGMS-DM+ , X2 R/F sensor, and Black Piranha) with those obtained using the single-rotation technique with lead apertures (SRTLA). HVL measurements were performed using spiral CT at tube voltages of 70-140 kV without a tin filter and 100-140 kV (Sn 100-140 kV) with a tin filter in increments of 10 kV. For SRTLA, a 0.6-cc ionization chamber was suspended at the isocenter to measure the free-in-air kerma rate ( ${\dot{K}}_{\text{air}}$ ) values. Five apertures were made on the gantry cover using lead sheets, and four aluminum plates were placed on these apertures. HVLs in SRTLA were obtained from ${\dot{K}}_{\text{air}}$ decline curves. Subsequently, SSDs inserted into the lead collimator were placed on the gantry cover and used to measure HVLs. Maximum HVL differences of AGMS-DM+ , X2 R/F sensor, and Black Piranha with respect to SRTLA without/with a tin filter were - 0.09/0.6 (only two Sn 100-110 kV) mm, - 0.50/ - 0.6 mm, and - 0.17/(no data available) mm, respectively. These values were within the specification limit. SSDs inserted into the lead collimator could be used to measure HVL using spiral CT without a tin filter. HVLs could be measured with a tin filter using only the X2 R/F sensor, and further improvement of its calibration accuracy with respect to other SSDs is warranted.

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural network (DCNN) reconstructed by transfer learning of a DCNN constructed using Gallium-67 (⁶⁷Ga) images. The initial DCNN was constructed using U-Net to optimize the display of ⁶⁷Ga images (493 cases/986 images), and a DCNN with transposed weight coefficients was reconstructed for the optimization of whole-body SRS images (133 cases/266 images). A DCNN was constructed for each observer using reference display conditions estimated in advance. Furthermore, to eliminate information loss in the original image, a grayscale linear process is performed based on the DCNN output image to obtain the final linearly corrected DCNN (LcDCNN) image. To verify the usefulness of the proposed method, an observer study using a paired-comparison method was conducted on the original, reference, and LcDCNN images of 15 cases with 30 images. The paired comparison method showed that in most cases (29/30), the LcDCNN images were significantly superior to the original images in terms of display conditions. When comparing the LcDCNN and reference images, the number of LcDCNN and reference images that were superior to each other in the display condition was 17 and 13, respectively, and in both cases, 6 of these images showed statistically significant differences. The optimized SRS images obtained using the proposed method, while reflecting the observer's preference, were superior to the conventional manually adjusted images.

An accurate and reliable patient-specific quality assurance (PSQA) is crucial to ensure the safety and precision of Stereotactic body radiation therapy (SBRT) in treating Hepatocellular carcinoma (HCC). This study examines the effectiveness of a novel hybrid 3D-printed hybrid coaxial cylindrical phantom for PSQA in the SBRT of HCC. The study compared three different point dose verification techniques for PSQA: a traditional solid water phantom, two dimensional detector array I'MatriXX, and a newly developed hybrid 3D-printed phantom. Thirty SBRT HCC liver cases were examined using these techniques, and point doses were measured and compared to planned doses using the perpendicular composite method with solid water and I'MatriXX phantoms. Unlike the other two methods, the point dose was compared in true composite geometry using the hybrid 3D-printed phantom, which enhanced the accuracy and consistency of PSQA. The study aims to assess the statistical significance and accuracy of the hybrid 3D-printed phantom compared to other methods. The results showed all techniques complied with the institutional threshold criteria of within ± 3% for point-dose measurement discrepancies. The hybrid 3D-printed phantom was found to have better consistency with a lower standard deviation than traditional methods. Statistical analysis using Student's t-test revealed the statistical significance of the hybrid 3D-printed phantom technique in patient-specific point-dose assessments with a p-value < 0.01. The hybrid 3D-printed phantom developed institutionally is cost-effective and easy to handle. It has been proven to be a valuable tool for PSQA in SBRT for the treatment of HCC and has demonstrated its practicality and reliability.