Radiological Physics and Technology最新文献

This study investigated the effectiveness of placing skin-ring structures to enhance the precision of skin dose calculations in patients who had undergone head and neck volumetric modulated arc therapy using the Acuros XB algorithm. The skin-ring structures in question were positioned 2 mm below the skin surface (skin A) and 1 mm above and below the skin surface (skin B) within the treatment-planning system. These structures were then tested on both acrylic cylindrical and anthropomorphic phantoms and compared with the Gafchromic EBT3 film (EBT3). The results revealed that the maximum dose differences between skins A and B for the cylindrical and anthropomorphic phantoms were approximately 12% and 2%, respectively. In patients 1 and 2, the dose differences between skins A and B were 9.2% and 8.2%, respectively. Ultimately, demonstrated that the skin-dose calculation accuracy of skin B was within 2% and did not impact the deep organs.

The aim of this study was to optimise the vessel angle as well as the stack number from the profiles of carbon dioxide digital subtraction angiography (CO₂-DSA) images of a water phantom containing an artificial vessel tilted at different angles which imitate arteries in the body. The artificial vessel was tilted at 0°, 15°, and 30° relative to the horizontal axis with its centre as the pivot point, and CO₂-DSA images were acquired at each vessel tilt angle. The maximum opacity method was used to stack up to four images of the next frame one by one. The signal-to-noise ratio (SNR) was determined from the profile curves. The Wilcoxon rank sum test was used to evaluate whether the profile curve and SNR differed depending on the vessel tilt angle or stack number, and a p-value of less than 0.05 was considered statistically significant. Images acquired at 0° had a significantly lower SNR than images acquired at 15° (p = 0.10). When the vessel angle was 30°, the profile curves were significantly improved (p < 0.05) when two or more images were stacked over the original image. Images with a good SNR were acquired at the vessel tilt angle of 15°, and the shape of the profile curve was improved when two or more images were stacked on the original image. This study demonstrates that the quality of images acquired using CO₂-DSA can be significantly improved through parameter optimisation for image acquisition and post-processing.

In photon-collapsed cone convolution (pCCC) algorithm of the Monaco treatment planning system (TPS), the central-axis energy spectrum is assumed constant throughout the entire irradiation area. To consider lateral variations, an off-axis softening factor is applied to attenuation coefficients during the total energy released per unit mass calculation. We evaluated this method through comparison studies of percentage depth doses (PDDs) and off-axis ratios (OARs) calculated by Monaco and measured for a 6 MV photon beam at various off-axis angles and depths. Significant differences were observed, with relative differences exceeding ± 1%. Therefore, this method may not accurately represent lateral variations of energy spectra. We propose directly implementing energy spectra on both central-axis and off-axis to improve dose calculation accuracy for large field. To this end, we introduce reconstruction of PDDs from monoenergetic depth doses (MDDs) along off-axis angles, thereby estimating energy spectra as functions of radial distance. This method derives energy spectra quickly without significantly increasing the beam modeling time. MDDs were computed through Monte Carlo simulations (DOSRZnrc). The variances between reconstructed and measured PDDs were minimized using the generalized-reduced-gradient method to optimize energy spectra. Reconstructed PDDs along off-axis angles of 0°, 1.15°, 2.29°, 3.43°, 4.57°, 5.71°, 6.84°, 7.97°, 9.09°, 10.2° to estimate energy spectra at radial distances of 0-18 cm in 2 cm increments and OARs calculated using estimated energy spectra at 5, 10, and 20 cm depths, well agreed with measurement (relative differences within ± 0.5%). In conclusion, our proposed method accurately estimates lateral energy spectrum variation, thereby improving dose calculation accuracy of pCCC algorithm.

Deep-learning-based methods can improve robustness against individual variations in computed tomography (CT) images of the sternocleidomastoid muscle, which is a challenge when using conventional methods based on probabilistic atlases are used for automatic segmentation. Thus, this study proposes a novel multiclass learning approach for the joint segmentation of the sternocleidomastoid and skeletal muscles in CT images, and it employs a two-dimensional U-Net architecture. The proposed method concurrently learns and segmented segments the sternocleidomastoid muscle and the entire skeletal musculature. Consequently, three-dimensional segmentation results are generated for both muscle groups. Experiments conducted on a dataset of 30 body CT images demonstrated segmentation accuracies of 82.94% and 92.73% for the sternocleidomastoid muscle and entire skeletal muscle compartment, respectively. These results outperformed those of conventional methods, such as the single-region learning of a target muscle and multiclass learning of specific muscle pairs. Moreover, the multiclass learning paradigm facilitated a robust segmentation performance regardless of the input image range. This highlights the method's potential for cases that present muscle atrophy or reduced muscle strength. The proposed method exhibits promising capabilities for the high-accuracy joint segmentation of the sternocleidomastoid and skeletal muscles and is effective in recognizing skeletal muscles, thus, it holds promise for integration into computer-aided diagnostic systems for comprehensive musculoskeletal analysis. These findings are expected to enhance medical image analysis techniques and their applications in clinical decision support systems.

Proton dose calculation in media other than water may be of interest for either research purposes or clinical practice. Current study aims to quantify the required parameters for analytical proton dosimetry in muscle, bone, and PMMA. Required analytical dosimetry parameters were extracted from ICRU-49 report and Janni study. Geant4 Toolkit was also used for Bragg curve simulation inside the investigated media at different proton energies. Calculated and simulated dosimetry data were compared using gamma analysis. Simulated and calculated Bragg curves are consistent, a fact that confirms the validity of reported parameters for analytical proton dosimetry inside considered media. Furthermore, derived analytical parameters for these media are different from those of water. Listed parameters can be reliably utilized for analytical proton dosimetry inside muscle, bone, and PMMA. Furthermore, accurate proton dosimetry inside each medium demands dedicated analytical parameters and one is not allowed to use the water coefficients for non-water media.

This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.

The modulation transfer function (MTF) is a fundamental tool for assessing the sharpness of digital breast tomosynthesis (DBT) systems and is primarily measured using edge devices. We compared the MTF of a Senographe Pristina DBT system using four-edge devices. These devices were composed of stainless steel with a thickness of 0.6, 0.8, and 1.0 mm, and 1.0 mm tungsten, based on different international guidelines. We evaluated spatial frequencies at MTFs of 0.5 (MTF50%) and 0.1 (MTF10%). The collimator-equipped and non-collimator configurations of the DBT were compared. We found no appreciable differences between scan and chest wall-nipple directions. Both MTF50% (2.90-2.99 cycles/mm) and MTF10% (6.69-6.94 cycles/mm) demonstrated minimal variation across the different edge devices. The collimator-equipped system exhibited an MTF50% that was approximately 5% higher than that of the non-collimator configuration. The choice of the edge device did not appreciably impact the MTF.

The purpose of current study was to assess the impact of ALA-coated gold nanoclusters (Au NPs) on the combined therapeutic effects of radiotherapy (RT) and photodynamic therapy (PDT) on healthy MCF-10A and MCF-7 breast cancer cells. The Au NPs were covered with ALA using PEG polymer, resulting in the synthesis of Au@ALA NPs. The successful synthesis of the final NPs was confirmed through FTIR, XRD, TEM, and UV-Vis tests. MCF-10A and MCF-7 cell lines were treated with different concentrations of Au@ALA NPs and exposed to irradiation of 2 and 4 Gy (using MV X-ray) and 630 nm laser light irradiation. Cytotoxicity was assessed using a multifaceted approach involving the MTT assay, real-time PCR, and colony forming assay. The findings revealed that the damage inflicted by Au@ALA NPs on cancerous tissue was significantly greater than that on normal tissue. The cytotoxic effects of all experimental groups exhibited a direct correlation with increasing concentrations and radiation doses. The combination of Au@ALA NPs with RT doses of 2 and 4 Gy resulted in a reduction in cell viability by a factor of 1.58 (P = 0.001) and 1.73 (P = 0.004), respectively. Furthermore, the simultaneous intervention of NPs with PDT and RT at doses of 2 and 4 Gy led to a decrease in cell viability by a factor of 2.10 (P = 0.001) and 3.08 (P = 0.001) in turn. Furthermore, the real-time PCR and colonogenic assay results demonstrated that the combined treatment significantly increased phosphorylation of ATM and expression of TP53, indicating an adequate synergistic effect on breast cancer cells. The concurrent application of Au@ALA NPs in RT and PDT successfully enhanced the radiosensitization of breast cancer cells to megavoltage RT and PDT.

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.