Radiological Physics and Technology最新文献

Medical radiation plays a crucial role in diagnostic imaging; however, any exposure carries potential risks. The thyroid gland, due to its proximity to the imaging field, is particularly vulnerable to radiation during CT brain scans. This study aims to evaluate the effectiveness of lead thyroid shields in reducing the estimated absorbed dose to the thyroid gland during CT brain imaging. This cross-sectional study was conducted at a tertiary care hospital in Sri Lanka over a 3-month period. Adult patients referred for contrast-enhanced CT (CECT) brain scans, who underwent both non-contrast and contrast-enhanced imaging, were included. The estimated absorbed dose to the thyroid gland was calculated using a Dose i-R Electronic Personal Dosimeter. Radiation dose measurements were taken with and without a 0.5 mm lead thyroid shield by placing the dosimeter both above and behind the shield. The sample consisted of 32 patients. The mean (SD) effective radiation dose during the procedures was calculated as 2.325 (0.118) mGy using a standard conversion factor of 0.0021. Without the thyroid shield, the mean (SD) estimated absorbed dose was 0.748 (0.178) mGy, which decreased to 0.352 (0.113) mGy with the lead thyroid shield. There was a statistically significant reduction in estimated absorbed dose with the thyroid shielding. There was a significant reduction in the estimated absorbed dose to the thyroid region with the use of the lead thyroid shield in patients undergoing CT brain studies. These findings highlight the effectiveness of lead thyroid shielding in minimizing radiation exposure to the thyroid region.

The aim of this study is to evaluate the impact of dynamic jaw width adjustment in tomotherapy on hippocampal sparing, target dose conformity, and treatment efficiency in hippocampal-avoidance whole-brain radiotherapy (HA-WBRT), in accordance with RTOG 0933 guidelines. A retrospective study of 60 patients treated with HA-WBRT was conducted. CT-MRI fusion facilitated accurate hippocampal delineation. Treatment plans were created using Accuray Precision TPS and delivered on the Radixact Tomotherapy system with three jaw widths (1 cm, 2.5 cm, and 5 cm), fixed pitch (0.215), and modulation factor (3.0). The prescription dose was 30 Gy in 10 fractions. Evaluation metrics included PTV coverage (D98%, V95%, D2%, Dmax), homogeneity index (HI), conformity index (CI), hippocampal and lens doses, and beam-on time (BOT). Plan verification was performed with ArcCHECK using 3%/3 mm and 3%/2 mm gamma criteria. The 1 cm jaw achieved the best PTV coverage (D98% = 29.22 Gy, V95% = 98.71%), with HI = 0.09, CI = 0.99, and superior hippocampal sparing (Dmax = 14.91 Gy, Dmin = 7.57 Gy), but had the longest BOT (1165 s). Wider jaws (2.5 cm, 5 cm) reduced BOT (480 s, 280 s) but slightly compromised conformity and increased OAR doses, all within limits. Jaw width selection in Helical Tomotherapy influences dose distribution characteristics and treatment delivery efficiency in hippocampus-sparing WBRT. A 1 cm jaw width provides superior dosimetric conformity and enhanced hippocampal sparing, albeit at the cost of increased BOT. In contrast, wider jaw widths (2.5 cm and 5 cm) improve delivery efficiency but result in modest reductions in dose precision and organ-at-risk sparing. Therefore, jaw width selection should be carefully individualized based on clinical objectives, balancing the trade-off between organ preservation and treatment efficiency.

This study compares the dosimetric performance of Base Dose Optimization (BDO) and Gradient-Based Optimization (GBO) for extended target volumes in Total Body Irradiation (TBI). The focus is on overlapping regions using the Rando Phantom. The study evaluates dose distribution, conformity, homogeneity, and sensitivity to positional deviations. The Rando Phantom was used for treatment planning with both BDO and GBO plans. Positional shifts of ± 5 mm and ± 10 mm were introduced to assess uncertainties. Dosimetric metrics included mean dose, minimum dose, maximum dose, homogeneity index (HI), and conformity index (CI). Positional deviations and their impact on dose variations were analyzed. Quality assurance was performed using OSLDs and an array detector. The BDO plan delivered higher mean doses (103.6%-108.7%) and hotspot values, with a maximum of 133.7%. In contrast, the GBO plan produced a more uniform dose distribution (99.6%-100.3%) with lower hotspots, peaking at 112%. The BDO plan achieved better uniformity (HI 0.025-0.103) and higher conformity (CI 0.938-0.981). The GBO plan showed greater variability (HI 0.143-0.253) and slightly lower conformity (CI 0.941-0.964). Positional shifts revealed that the BDO plan was highly sensitive, with overdoses of + 46.60% and underdoses of - 47.29%. The GBO plan showed smaller deviations (+ 11.62% overdose, - 11.73% underdose). Gamma analysis demonstrated higher pass rates for the GBO plan. The BDO plan excels in target coverage and conformity but is sensitive to positional shifts. The GBO plan offers better uniformity and robustness, supporting its use in complex clinical scenarios. Further refinement of both approaches could improve clinical applicability and patient outcomes.

We evaluated the effectiveness of aluminum interspace grids with varying grid ratios, conventional 10:1 (r10) and 14:1 (r14) and experimental 17:1 (r17), in terms of image quality of digital radiography for phantom thicknesses of 20 to 30 cm. The signal-to-noise improvement factor (SIF) and signal-difference-to-noise ratio (SDNR) were measured at tube voltages of 80-110 kV. An acrylic object and a bone equivalent object were used for the SDNR measurements. While the grid ratio had a positive impact on SIF, its effect on SDNR was not remarkable: SDNR was not higher with r17 than with r14 for the acrylic object. For the bone-like object, it exhibited some meager, or even negative, improvements with r14 and r17 compared with r10. These results can be attributed to reduced contrast caused by beam hardening due to higher grid ratios. Consequently, the grid ratio should be chosen considering the reduction in contrast.

Accurate signal-to-noise ratio (SNR) measurement is essential for evaluating image quality in magnetic resonance imaging (MRI). While the subtraction-map method provides precise SNR measurements, it requires two consecutive acquisitions, limiting its clinical applicability. This study aims to develop and validate a method for practical SNR measurement using clinical MRI images. The proposed method generates an SNR map by computing a noise-only image from a single MRI image using pixel shifting and edge component removal. The accuracy of our method was compared with the subtraction-map method in three evaluations: (1) optimization of a key parameter for edge component removal, (2) analysis of spatial resolution and SNR level effects, and (3) validation using brain MRI images. The study included brain MRI from 188 patients, and SNR measurements were performed on the resulting images. Correlation coefficients and Bland-Altman analysis were used for comparisons. Parameter optimization identified an optimal threshold for separating noise and edge components. Higher spatial resolution improved accuracy, whereas lower resolution and low SNR conditions led to overestimation. In clinical MRI, the proposed method showed a strong correlation with the subtraction-map method (Spearman r = 0.96), and the highest average error rate was 8.1% in T1-weighted images. Bland-Altman analysis demonstrated good agreement across sequences and regions. This method enables practical SNR estimation from a single image, eliminating the need for repeated acquisitions. While limitations remain in low-SNR or structurally complex regions, the method shows promise as a practical tool for retrospective and routine clinical image quality assessments.

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.