Radiological Physics and Technology最新文献

Computed tomography (CT) is valuable for assessing left ventricular (LV) function. However, it leads to increased data storage demands and energy consumption. Temporal super resolution (TSR) has the potential to reduce temporal data size while preserving accuracy. This study aimed to determine the feasibility of using TSR for temporal image compression in LV functional analysis. The study included 20 patients who underwent retrospective electrocardiogram (ECG)-gated cardiac CT, from which 20 cardiac phases per patient were acquired. TSR was applied to temporally compressed image data sets, with and without noise reduction (NR), using two NR levels: weak (30%) and strong (70%). Five data sets-including the original uncompressed data and four compressed versions-were analyzed for LV function using fully automated software. Bland-Altman plots and Pearson correlation coefficients were used to assess measurement agreement and reliability. The correlations between the uncompressed and compressed data sets for LV end-systolic volumes (ESVs), end-diastolic volumes (EDVs), and ejection fractions (EFs) were strong (all r = 1.00, 95% CI = 1.00-1.00, all Ps < 0.0001). Bland-Altman analysis showed reduced bias in LV measurements when TSR was applied without NR, while bias increased when NR was applied at both levels. The limits of agreement (LOA) were narrower for EDV but remained wider for ESV and EF. TSR without NR reduced bias but failed to narrow LOA, with EF improving or unchanged in 35% of cases. While this level of consistency is limited, the findings suggest that TSR may preserve functional accuracy under certain conditions.

Clinical magnetic resonance imaging (MRI) is a high-resolution tool widely used for detailed anatomical imaging. However, prolonged scan times often lead to motion artefacts and patient discomfort. Fast acquisition techniques can reduce scan times but often produce noisy, low-contrast images, compromising segmentation accuracy essential for diagnosis and treatment planning. To address these limitations, we developed an end-to-end framework that incorporates BIDS-based data organiser and anonymizer, a GAN-based MR image enhancement model (GAN-MRI), AssemblyNet for brain region segmentation, and an attention-residual U-Net with Guided loss for abdominal and thigh segmentation. Thirty brain scans (5,400 slices) and 32 abdominal (1,920 slices) and 55 thigh scans (2,200 slices) acquired from multiple MRI scanners (GE, Siemens, Toshiba) underwent evaluation. Image quality improved significantly, with SNR and CNR for brain scans increasing from 28.44 to 42.92 (p < 0.001) and 11.88 to 18.03 (p < 0.001), respectively. Abdominal scans exhibited SNR increases from 35.30 to 50.24 (p < 0.001) and CNR from 10,290.93 to 93,767.22 (p < 0.001). Double-blind evaluations highlighted improved visualisations of anatomical structures and bias field correction. Segmentation performance improved substantially in the thigh (muscle: + 21%, IMAT: + 9%) and abdominal regions (SSAT: + 1%, DSAT: + 2%, VAT: + 12%), while brain segmentation metrics remained largely stable, reflecting the robustness of the baseline model. Proposed framework is designed to handle data from multiple anatomies with variations from different MRI scanners and centres by enhancing MRI scan and improving segmentation accuracy, diagnostic precision and treatment planning while reducing scan times and maintaining patient comfort.

A novel technique for the simultaneous evaluation of the radiation dose and the time elapsed after irradiation is described in detail. The proposed method depends on the use of the two signals of the EPR spectrum of irradiated di-sodium tartrate where they possess different responses towards radiation doses and different behaviors toward the time-dependence of the radiation-induced radicals. An empirical formula was used in order to estimate the radiation dose accurately over the first month following the irradiation process. For the estimation of the elapsed time after irradiation, the ratio of the peak-to-peak intensities of the first peak to the second was used. Uncertainties associated with the estimated elapsed time, U_A(t)_, range from 1.5% to 20.78%, while uncertainties associated with the estimated radiation doses range from 0.26% to 4.53%.

Dedicated breast positron emission tomography (dbPET) has higher spatial resolution than whole-body PET and can detect smaller lesions. Therefore, it is expected to be useful in detecting early stage breast cancer and assessing treatment efficacy. However, dbPET images suffer leading to a relative increase in noise from reduced sensitivity. In a previous study, optimized noise reduction for each region was achieved by applying multiple convolutional neural networks (CNNs). However, CNN processing was performed in a two-dimensional (2D) slice plane, which resulted in image blurring when the image was observed from multiple directions using maximum intensity projection (MIP). In this study, we aimed to further reduce noise and improve visibility by extending multiple CNNs to the three-dimensional (3D) processing and optimizing them for each region. To train the CNN, data with acquisition times of 1 and 7 min were used as the input and teacher images, respectively. Furthermore, 3D volume data were used as the input, and the system was designed to output volume data after noise reduction processing. Quantitative evaluation of the proposed multiple 3D direction-denoising filter showed better performance than that of the 2D filter. Furthermore, the visibility of the MIP images improved. In addition, the quantitative evaluation of the maximum standardized uptake value (SUV_MAX) was conducted using a phantom; the results confirmed that the proposed noise reduction method ensured maintaining the reproducibility of SUV_MAX. These results indicate that the proposed method is effective for noise reduction in dbPET images.

Positron emission tomography (PET) images can be compromised by respiratory motion, leading to a decreased standardized uptake value (SUV) of the lesion and overestimation of the metabolic tumor volume (MTV). This study aimed to determine the optimal settings for auto-gating, a deviceless respiratory synchronization technique, using advanced intelligent clear-IQ engines (AiCE) and clear adaptive low-noise method (CaLM) reconstruction conditions. We performed phantom and clinical studies on 57 patients with pulmonary lesions. We acquired images at various %count settings (nongated, 30%, 50%, and 70%) using both AiCE and CaLM. In each setting, we measured the SUVmax, SUVpeak, and MTV of the lesions and calculated and compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) in the liver. Additionally, we visually assessed lesion blurring and image noise to confirm whether the quantitative evaluation was consistent with the visual evaluation. Considering our findings, the optimal auto-gating setting at an acquisition time of 180 s is 50% for the lower lobe in AiCE and for both the lower and middle lobes in CaLM.

Cone-beam computed tomography (CBCT) is commonly utilized in radiation therapy to visualize soft tissues and bone structures. This study aims to develop a machine learning model that predicts optimal, patient-specific CBCT doses that minimize radiation exposure while maintaining soft tissue image quality in prostate radiation therapy. Phantom studies evaluated the relationship between dose and two image quality metrics: image standard deviation (SD) and contrast-to-noise ratio (CNR). In a prostate-simulating phantom, CNR did not significantly decrease at doses above 40% compared to the 100% dose. Based on low-contrast resolution, this value was selected as the minimum clinical dose level. In clinical image analysis, both SD and CNR degraded with decreasing dose, consistent with the phantom findings. The structural similarity index between CBCT and planning computed tomography (CT) significantly decreased at doses below 60%, with a mean value of 0.69 at 40%. Previous studies suggest that this level may correspond to acceptable registration accuracy within the typical planning target volume margins applied in image-guided radiotherapy. A machine learning model was developed to predict CBCT doses using patient-specific metrics from planning CT scans and CBCT image quality parameters. Among the tested models, support vector regression achieved the highest accuracy, with an R² value of 0.833 and a root mean squared error of 0.0876, and was therefore adopted for dose prediction. These results support the feasibility of patient-specific CBCT imaging protocols that reduce radiation dose while maintaining clinically acceptable image quality for soft tissue registration.

The aim of the study was to evaluate the degree of error between Monte Carlo simulations of pediatric lens dose outside the scan range and measured values obtained with a dosimeter. Two types of computed tomography (CT) equipment and three pediatric anthropomorphic phantoms were used, each with a nanoDot optically stimulated luminescence dosimeter (nanoDot OSLD; Landauer, Inc., Glenwood, IL, USA) mounted on its left and right lenses. The scatter dose measurements obtained from the nanoDot were compared with those predicted by the particle and heavy ion transport code system, which served as a Monte Carlo simulation tool during pediatric chest CT examinations. The error rate between the mean measured dose and the simulated dose was within 1.5% for Aquilion Genesis and within 8.0% for Revolution. We evaluated the degree of error between Monte Carlo simulations of pediatric lens dose outside the scan range and measured values obtained with a dosimeter. The Monte Carlo simulations tended to underestimate the error.

Automatic exposure control (AEC) in digital radiography adjusts exposure time based on the chosen milliamperage (mA) and the patient's anatomical characteristics, ensuring the delivery of an appropriate radiation dose for optimal image quality. This study aimed to evaluate the reproducibility of AEC systems in general X-ray machines under various conditions. AEC reproducibility was assessed in two general X-ray machines: the SIEMENS Multix Top and the DRGEM GXR-40S. Both systems offer three sensitivity settings (high, medium, and low). A stack of Thai ten-baht coins, consisting of one and five layers, was used as a test object and placed directly over the AEC sensor. Ten exposures were carried out for repeated measurements. Differences in mAs values and coefficients of variation (CV) were calculated, and statistical analysis was performed using the Mann-Whitney U test. Results showed that mAs values changed in response to tube voltage, sensitivity setting, object thickness, and sensor position; however, these variations remained within acceptable limits. A higher mAs value was observed at lower tube voltages (80-81 kVp), a lower sensitivity setting (D or Slow), and a five-layer coin thickness. No significant differences were observed at higher tube voltage (100 kVp) and higher sensitivity (H or Fast; p > 0.05). In conclusion, AEC reproducibility testing showed mean mAs ranges of 0.51-3.25 with a maximum CV of 2.60% for SIEMENS, and 0.37-1.62 with a maximum CV of 3.37% for DRGEM. Both systems met international standard guidelines, with a CV below 5.00%, as recommended by AAPM Report No. 150, confirming consistent mAs values under various conditions.

Purpose: The purpose of this study was to evaluate the predictive value of MRI-based texture features for assessing stroke risk from vulnerable carotid plaques.

Method: Among patients diagnosed with carotid artery plaque by MRI, 10 patients with whom Time-to-Event for atherothrombotic stroke could be obtained were enrolled. Radiomics features were extracted from T1/T2-weighted black-blood images and cervical 3D time-of-flight images. Additionally, this investigation employed the extraction of 16 Gray-Level Fluid Zone Matrix (GLFZM) features, specifically developed for this analysis. Wall shear stress (WSS), a biomechanical characteristic, was also subjected to calculation. These features served as the basis for developing clinical models, radiomics-plaque models, radiomics-lumen models, GLFZM models, WSS models, and combined models. The performance of each model was evaluated using regression analysis by calculating mean squared error (MSE). As one aspect of the robustness of each model, we evaluated the models using Cox proportional hazard models and concordance indices (CI) derived from synthetic data generated with the noise scale.

Result: The LOOCV MSE and mean CI values were: clinical model (2.58 × 10⁶, 0.65), radiomics-plaque model (4.62 × 10⁶, 0.75), radiomics-lumen model (3.30 × 10⁶, 0.81), GLFZM model (2.00 × 10⁶, 0.74), WSS model (2.47 × 10⁶, 0.46), and combined model (1.48 × 10⁶, 0.78). The combined model demonstrated the minimal MSE.

Conclusion: This study demonstrated via preliminary simulations that analyzed clinical variables, radiomic features (plaque and lumen), texture features indicative of flow velocity (GLFZM), and biomechanical features (WSS) as model predictors, the potential utility of texture analysis in forecasting ischemic events in cerebral infarction resulting from vulnerable carotid plaques.

Pituitary adenomas, ranging from subtle microadenomas to mass-effect macroadenomas, pose diagnostic challenges for radiologists due to increasing scan volumes and the complexity of dynamic contrast-enhanced MRI interpretation. A hybrid CNN-LSTM model was trained and validated on a multi-center dataset of 2,163 samples from Tehran and Babolsar, Iran. Transfer learning and preprocessing techniques (e.g., Wiener filters) were utilized to improve classification performance for microadenomas (< 10 mm) and macroadenomas (> 10 mm). The model achieved 90.5% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.92, and 89.6% sensitivity (93.5% for microadenomas, 88.3% for macroadenomas), outperforming standard CNNs by 5-18% across metrics. With a processing time of 0.17 s per scan, the model demonstrated robustness to variations in imaging conditions, including scanner differences and contrast variations, excelling in real-time detection and differentiation of adenoma subtypes. This dual-path approach, the first to synergize spatial and temporal MRI features for pituitary diagnostics, offers high precision and efficiency. Supported by comparisons with existing models, it provides a scalable, reproducible tool to improve patient outcomes, with potential adaptability to broader neuroimaging challenges.