Gunma Prefectural College of Health Sciences

Breast cancer is the most frequently diagnosed cancer among women. Accurate diagnosis and effective management rely heavily on high-quality positron emission tomography (PET) imaging. A novel time-of-flight (TOF)-enhanced deep learning reconstruction (DLR) technique has recently been introduced for the Omni Legend (GE Healthcare) PET/CT system. However, its clinical utility in breast cancer imaging has not yet been fully established. This study aims to assess the impact of the DLR method on 18F-FDG PET/CT imaging in patients with breast cancer. This retrospective study included 30 female breast cancer patients who underwent 18F-FDG PET/CT using the Omni Legend system. PET images were reconstructed using the Bayesian penalized likelihood (BPL) method and a DLR method with three TOF enhancement levels: low (L-DLR), medium (M-DLR), and high (H-DLR). Image quality was evaluated using liver noise level (Noise) and lesion signal-to-background ratios (SBR). Percentage changes in these metrics between BPL and each DLR setting were calculated. The four reconstruction methods were compared using the Friedman test with Bonferroni correction. P-values < 0.05 were used to denote statistical significance. Noise values for BPL, L-DLR, M-DLR, and H-DLR were 0.08, 0.06, 0.06, and 0.08, respectively (P < 0.001), whereas SBR values were 3.75, 3.85, 4.09, and 4.39, respectively (P < 0.001). Compared with BPL, L-DLR and M-DLR significantly reduced Noise by 33.20

Background: Recently, deep learning (DL)-based noise reduction (DLNR) has been introduced in clinically used digital radiography (DR) systems, reporting superior performance over conventional algorithms. However, DLNR algorithms often operate as "black boxes" with nonlinear behavior, making it essential to understand the impact of such processing on image quality under different imaging conditions. Purpose: This study aimed to quantitatively evaluate the image quality of a commercial DLNR algorithm for DR referred to as intelligent noise reduction (INR). Specifically, we compared its noise reduction performance with that of a conventional rule-based algorithm (conventional noise reduction, Con-NR) using frequency-domain metrics with detailed noise power spectrum (NPS) analysis. Methods: The NPS was used to assess the spatial-frequency-dependent behavior of both INR and Con-NR across varying dose levels and different objects. In this work, we introduced a supplementary metric-the NPS improvement factor (NPSIF)-to quantify noise suppression across frequency ranges and facilitate direct comparison between methods. Results: The DL-based algorithm achieved substantial noise reduction at low-dose settings compared with the conventional method, although its advantages were less pronounced at higher dose levels. The NPSIF effectively captured frequency-specific differences, thereby offering insights into the strengths and limitations of each technique. Conclusions: The dose-dependent performance of the DL-based algorithm suggests sensitivity to the characteristics of the training data used to develop the DL model. The findings demonstrate distinct differences in the noise suppression behavior between DL-based and conventional methods in DR and underscore the importance of detailed frequency-domain evaluation for understanding advanced image processing. Further research is warranted to integrate noise analysis with diagnostic performance metrics to comprehensively assess clinical utility.

The half-value layer (HVL), an indicator of X-ray quality, is defined as the thickness of an aluminum (Al) filter that reduces the air kerma by half and is used to calculate the backscatter coefficient. HVL is determined from the attenuation curve of air kerma using log-linear interpolation. However, there are no references detailing the specific measurement method. This study aims to investigate the impact of varying the interval of Al filters used in the log-linear interpolation on the HVL, using Monte Carlo simulations. The Monte Carlo simulation was performed using Particle and Heavy Ion Transport code System (PHITS) Ver. 3.29. A photon point source was placed in the air, and the rectangular irradiation field was set to 5×5 cm at the detector position. The detector, simulated as a volume of 1 cm3 of air, was positioned 100 cm from the source. The Al filter thickness for the HVL was varied in increments of 0.1 mm. The HVL was calculated by linear interpolation, and the relative error was determined based on the minimum Al spacing. The X-ray tube voltages used were those of the RQR series (40, 50, 60, 70, 80, 90, 100, 120, and 150 kV). The beam qualities obtained from the measurements and the Monte Carlo simulation system were consistent with those specified for the RQR series in IEC 61267 within ±3.5%. The relative error of HVL for each tube voltage determined by simulation ranged from -0.5 to 4.6%, with a mean±standard deviation (median) of 1.12±0.95% (0.88%). The relative error was larger when the difference between the Al filter combinations was large and the interpolation coefficient α was 0.5. When the filter spacing is less than half the HVL, the accuracy of log-linear interpolation in HVL is less than ±1.5% relative error.

The demand for bedside radiography is increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in these settings remains heavily reliant on the expertise and experience of radiographers. To address this issue, a novel flat panel detector (FPD) integrated with an automatic exposure control (AEC) system has been developed. This study aims to experimentally evaluate the fundamental performance of this system and clarify its clinical utility, including its potential limitations. The dependency of the AEC performance on object thickness and tube voltage was investigated using acrylic phantoms. To simulate clinical scenarios, the AEC response was examined using a chest phantom. Additionally, the effects of source-to-image distance and oblique X-ray incidence on the AEC performance were also evaluated using a quality-control test device. Our results elucidated the behavior of the exposure index (EI) and image quality under varying tube voltage and object thickness. In clinical conditions, the introduction of the AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both the AEC response and image quality, such as sensor positioning, imaging distance, and beam angle. These findings demonstrate that the AEC-equipped FPD system maintains consistent image quality while effectively reducing the radiation dose under various simulated imaging conditions. Our results also underscore the importance of accounting for environmental factors that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation.

Background Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide. Early detection of breast cancer using high-quality mammography is essential for improving prognosis and treatment outcomes. Digital breast tomosynthesis with synthesized two-dimensional mammography (SM) reduces dose and tissue overlap; however, its comprehensive physical evaluations remain limited.Purpose This study quantitatively compared the image quality of conventional digital mammography (DM) and SM acquired using the AMULET Innovality System.Methods Polymethyl methacrylate, American College of Radiology, and contrast-detail (CD) mammography phantoms were imaged 10 times in both DM and SM modes. Image quality metrics, including signal-to-noise ratio (SNR) maps, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), and CD curves, were evaluated.Results DM demonstrated higher SNR uniformity and suppressed low- and high-frequency noise, and the CNR values were 1.3-2 times greater than those of SM across all object sizes. MTF analysis showed superior resolution in DM (MTF(f50), 4.0 cycles/mm) compared with that of the SM (2.5 cycles/mm). CD curve analysis confirmed the better detectability of fine structures in DM, with IQFinv$IQ{{F}_{inv}}$ values of 134.9 for DM and 56.7 for SM.Conclusions Quantitative phantom-based evaluation demonstrated that DM showed superior performance to SM in several physical image quality metrics, including SNR uniformity, noise characteristics, spatial resolution, and contrast-detail detectability. These findings indicate systematic differences in physical image quality between DM and SM; however, further observer and clinical studies are required to clarify how these differences translate to diagnostic performance in clinical practice.