Feasibility of dose calculation for treatment plans using electron density maps from a novel dual-layer detector spectral CT simulator.

BACKGROUND: Conventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans. METHODS: 30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software. RESULTS: In terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%. CONCLUSION: Compared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

Computational model of radiation oxygen effect with Monte Carlo simulation: effects of antioxidants and peroxyl radicals.

PURPOSE: Oxygen plays a crucial role in radiation biology. Antioxidants and peroxyl radicals affect the oxygen effect greatly. This study aims to establish a computational model of the oxygen effect and explore the effect attributed to antioxidants and peroxyl radicals. MATERIALS AND METHODS: Oxygen-related reactions are added to our track-structure Monte Carlo code NASIC, including oxygen fixation, chemical repair by antioxidants and damage migration from base-derived peroxyl radicals. Then the code is used to simulate the DNA damage under various oxygen, antioxidant and damage migration rate conditions. The oxygen enhancement ratio(OER) is calculated quantifying by the number of double-strand breaks for each condition. The roles of antioxidants and peroxyl radicals are examined by manipulating the relevant parameters. RESULTS AND CONCLUSIONS: Our results indicate that antioxidants are capable of rapidly restoring DNA radicals through chemical reactions, which compete with natural and oxygen fixation processes. Additionally, antioxidants can react with peroxyl radicals derived from bases, thereby preventing the damage from migrating to DNA strands. By quantitatively accounting for the impact of peroxyl radicals and antioxidants on the OER curves, our study establishes a more precise and comprehensive model of the radiation oxygen effect.

A semi-supervised learning-based quality evaluation system for digital chest radiographs.

BACKGROUND: Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential step in medical examinations. However, manual evaluation can be time-consuming, labor-intensive, and prone to interobserver differences, making it less reliable. PURPOSE: To alleviate the workload of radiographic technologists and enhance the efficiency of radiograph quality evaluation, it is crucial to develop rapid and reliable quality evaluation methods and establish a set of quantitative evaluation standards. To address this, we have proposed a quality evaluation system for digital radiographs that utilizes deep learning techniques to achieve fast and precise evaluation. METHODS: The evaluation of frontal chest radiograph quality involves assessing patient positioning through semantic segmentation and foreign body detection. For lung, scapula, and clavicle segmentation in digital chest radiographs, a residual connection-based convolutional neural network π-ResUNet, was proposed. Criteria for patient positioning evaluation were established based on the segmentation and manual evaluation results. A convolutional neural network, FasterRCNN, was utilized to detect and localize foreign bodies in digital chest radiographs. To enhance the performance of both neural networks, a semi-supervised learning (SSL) strategy was implemented by incorporating a consistency loss that leverages a large number of unlabeled digital radiographs. We also trained the network using the fully supervised learning (FSL) strategy and compared their performance on the test set. The ChestXRay-14 and object-CXR datasets were used throughout the process. RESULTS: By comparing with the manual annotation, the proposed network, trained using the SSL method, achieved a high Dice similarity coefficient (DSC) of 0.96, 0.88, and 0.88 for lung, scapula, and clavicle segmentation, respectively, outperforming the network trained with the FSL method. In addition, for foreign body detection, the proposed SSL method was superior to the FSL method, achieving an AUC (Area under receiver operating characteristic curve, Area under ROC curve) of 0.90 and an FROC (Free-response ROC) of 0.77 on the test dataset. CONCLUSIONS: The experimental results show that our proposed system is well-suited for radiograph quality evaluation, with the semi-supervised learning method further improving the network's performance. The proposed method can evaluate the quality of a chest radiograph from two aspects-patient positioning and foreign body detection-within 1 s, offering a promising tool in radiograph quality evaluation.

An Active Dose Measurement Device for Ultra-short, Ultra-intense Laser Facilities.

ABSTRACT: Ultra-short, ultra-intense laser facilities could produce ultra-intense pulsed radiation fields. Currently, only passive detectors are fit for dose measurement in this circumstance. Since the laser device could generate a dose up to tens of mSv outside the chamber in tens of picoseconds, resulting in a high instantaneous dose rate of ~107 Sv s-1, it is necessary to perform real-time dose measurement to ensure the safety of nearby workers. Due to fast response and excellent radiation resistance, a diamond-based dose measurement device was designed and developed, and its dose-rate response and its feasibility for such occasions were characterized. The measurement results showed that the detector had a good dose-rate linearity in the range of 3.39 mGy h-1 to 10.58 Gy h-1 for an x-ray source with energy of 39 keV to 208 keV. No saturation phenomenon was observed, and the experimental results were consistent with the results obtained from Monte Carlo simulation. The charge collection efficiency was about 80%. Experimental measurements and simulations with this dose measurement device were carried out based on the "SG-II" laser device. The experimental and simulation results preliminarily verified the feasibility of using the diamond detector to measure the dose generated by ultra-short, ultra-intense laser devices. The results provided valuable information for the follow-up real-time dose measurement work of ultra-short, ultra-intense laser devices.

Development of liquid phase microextraction based on manual shaking and ultrasound-assisted emulsification method for analysis of organochlorine pesticides in aqueous samples.

A novel method using sample preparation method, "ultrasound-assisted emulsification microextraction" (USAEME) with manual shaking, coupled with gas chromatography using and an electron capture detector (GC-ECD) was developed for the analysis of organochlorine pesticides (OCPs) in aqueous samples. The apparatus is simple and easy to operate. After manual shaking for 10s, ultrasound was used to accelerate emulsification of the organic solvent (1-decanol) in aqueous solution. Only 10 µL of the low-toxicity extraction solvent is used in this method; no dispersive solvent is required and the total extraction time is ∼4 min. Manual shaking before ultrasound-assisted emulsification enhances the extraction efficiency by >100%. The effects of horizontal and vertical orientation as well as the location of the sample within the ultrasonic bath were studied. After centrifugation, we used an improved solvent collection system (ISCS) to reduce the amount of extraction solvent required. A 1 µL sample of the extract was injected into the GC column. Under optimum conditions, the linear range of the method is 5-2500 ngL(-1) for most of the OCPs, and the limit of detection of the method ranged from 0.6 to 2.9 ngL(-1).The relative recoveries ranged from 75 to 107% for sea water and from 70 to 99% for field fresh water. The method, which provides good enrichment factors, low LODs and minimization of the consumption of organic solvent, provides a rapid, simple and environment-friendly procedure for determining OCPs in aqueous samples.

Improved solvent collection system for a dispersive liquid-liquid microextraction of organochlorine pesticides from water using low-density organic solvent.

In this study, the organochlorine pesticides (OCPs) levels in lake and tap water samples were determined by a dispersive liquid-liquid microextraction method using a low-density organic solvent and an improved solvent collection system (DLLME-ISCS). This method used a very small volume of a solvent of low toxicity (11 µL of 1-nonanol and 400 µL of methanol) to extract OCPs from 10 mL water samples prior to the analysis by GC. After centrifugation in the dispersive liquid-liquid microextraction, there was a liquid organic drop floating between the water surface and the glass wall of the centrifuge tube. The liquid organic drop (with some water phase) was transferred into a microtube (3 mm×15 mm) with a syringe. The organic and aqueous phases were separated in the microtube immediately. Then, 1 µL of the organic solvent (which was in the upper portion of liquid in the microtube) was easily collected by a syringe and injected into the GC-ECD system for the analysis. Under optimum conditions, the linear range of this method was 5-5000 ng/L for most of the analytes. The correlation coefficient was higher than 0.997. Enrichment factors ranged from 1309 to 3629. The relative recoveries ranged from 73 to 119% for lake water samples. The LODs of the method ranged from 0.7 to 9.4 ng/L. The precision of the method ranged from 1.0 to 10.8% for lake water.