Enhanced radio-photodynamic therapy potential of advanced gold-based nanoclusters for breast cancer treatment.

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.

Design and implementation of continuous bed motion (CBM) in Xtrim preclinical PET scanner for whole-body Imaging: MC simulation and experimental measurements.

PURPOSE: Preclinical PET scanners often have limited axial field-of-view for whole-body (WB) scanning of the small-animal. Step-and-shoot(S&S) acquisition mode requires multiple bed positions (BPs) to cover the scan length. Alternatively, in Continuous Bed Motion(CBM) mode, data acquisition is performed while the bed is continuously moving. In this study, to reduce acquisition time and enhance image quality, the CBM acquisition protocol was optimized and implemented on the Xtrim-PET preclinical scanner for WB imaging. METHODS: The over-scan percentage(OS%) in CBM mode was optimized by Monte Carlo simulation. Bed movement speed was optimized considering ranges from 0.1 to 2.0 mm s-1, and absolute system sensitivities with the optimal OS% were calculated. The performance of the scanner in CBM mode was measured, and compared with S&S mode based on the NEMA-NU4 standard. RESULTS: The optimal trade-off between absolute sensitivity and uniformity of sensitivity profile was achieved at OS-50 %. In comparison to S&S mode with maximum ring differences (MRD) of 9 and 23, the calculated equivalent speeds in CBM(OS-50 %) mode were 0.3 and 0.14 mm s-1, respectively. In terms of data acquisition with equal sensitivity in both CBM(OS-50 %) and S&S(MRD-9) modes, the total scan time in CBM mode decreased by 25.9 %, 47.7 %, 54.7 %, and 58.2 % for scan lengths of 1 to 4 BPs, respectively. CONCLUSION: The CBM mode enhances WB PET scans for small-animals, offering rapid data acquisition, high system sensitivity, and uniform axial sensitivity, leading to improved image quality. Its efficiency and customizable scan length and bed speed make it a superior alternative.

A mechanistic simulation of induced DNA damage in a bacterial cell by X- and gamma rays: a parameter study.

Mechanistic Monte Carlo simulations calculating DNA damage caused by ionizing radiation are highly dependent on the simulation parameters. In the present study, using the Geant4-DNA toolkit, the impact of different parameters on DNA damage induced in a bacterial cell by X- and gamma-ray irradiation was investigated. Three geometry configurations, including the simple (without DNA details), the random (a random multiplication of identical DNA segments), and the fractal (a regular replication of DNA segments using fractal Hilbert curves), were simulated. Also, three physics constructors implemented in Geant4-DNA, i.e., G4EmDNAPhysics_option2, G4EmDNAPhysics_option4, and G4EmDNAPhysics_option6, with two energy thresholds of 17.5 eV and 5-37.5 eV were compared for direct DNA damage calculations. Finally, a previously developed mathematical model of cell repair called MEDRAS (Mechanistic DNA Repair and Survival) was employed to compare the impact of physics constructors on the cell survival curve. The simple geometry leads to undesirable results compared to the random and fractal ones, highlighting the importance of simulating complex DNA structures in mechanistic simulation studies. Under the same conditions, the DNA damage calculated in the fractal geometry was more consistent with the experimental data. All physics constructors can be used alternatively with the fractal geometry, provided that an energy threshold of 17.5 eV is considered for recording direct DNA damage. All physics constructors represent a similar behavior in generating cell survival curves, although the slopes of the curves are different. Since the inverse of the slope of a bacterial cell survival curve (i.e., the D10-value) is highly sensitive to the simulation parameters, it is not logical to determine an optimal set of parameters for calculating the D10-value by Monte Carlo simulation.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

Measurement of Hp(10), Hp(3) and Hp(0.07) to medical staff in endoscopic retrograde cholangiopancreatography, using thermoluminescence dosimetry.

Endoscopic retrograde cholangiopancreatography (ERCP) is widely used in the diagnosis and treatment of pancreatic and bile duct disorders. The procedure is performed under the guidance of fluoroscopy. This study aims to investigate the dose received by staff in the Endoscopy Department of AbuAli Sina Medicine and Organ Transplant Hospital in Shiraz. The dosimetry was performed using thermoluminescent dosemeters (TLD), type TLD-100. The values of Hp(10), Hp(3) and Hp(0.07) were estimated for the staff for 2 months. According to the results obtained in this study, the equivalent dose of the gastroenterologist's body, eye lenses and hands was 0.045 ± 0.001 mSv, 0.111 ± 0.014 mSv and 0.357 ± 0.034 $\mathrm{mSv}$, respectively. This study showed that the annual radiation exposure for ERCP department staff of Abu Ali Sinai Hospital is less than the annual dose limit. However, if the principles of proper radiation protection and individual dosimetry are followed, the dose to staff members can be reduced.

Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM.

This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet backbones with atrous convolution in DeepLabV3+ structure. The Grad-CAM tool was also used to interpret the effect of augmentation and network optimization on segmentation performance. A wide investigation was performed to optimize the network hyperparameters. In addition, the study examined 35 different models to evaluate different data augmentation techniques. The results of the study indicated that incorporating data augmentation techniques and optimization can improve the performance of segmenting brain LGG tumors up to 10%. Our extensive investigation of the data augmentation techniques indicated that enlargement of data from 90° and 225° rotated data,up to down and left to right flipping are the most effective techniques. MobilenetV2 as the backbone,"Focal Loss" as the loss function and "Adam" as the optimizer showed the superior results. The optimal model (DLG-Net) achieved an overall accuracy of 96.1% with a loss value of 0.006. Specifically, the segmentation performance for Whole Tumor (WT), Tumor Core (TC), and Enhanced Tumor (ET) reached a Dice Similarity Coefficient (DSC) of 89.4%, 70.1%, and 49.9%, respectively. Simultaneous visual and quantitative assessment of data augmentation and network optimization can lead to an optimal model with a reasonable performance in segmenting the LGG tumors.

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study.

PURPOSE: Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks. METHODS: The pre-trained p2p-U-Net-GAN and HighRes3D architectures were fine-tuned with PET-only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL-assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)-derived S-values to be used for internal dosimetry. RESULTS: A fair comparison with manual low-dose CT-aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89-0.93, 0.98-0.99, and 0.87-0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S-values varied between 0.1% and 6% with a maximum attributed to the stomach. CONCLUSION: The findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL-based methods provide automated and fast segmentation of whole-body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F-FDG-PET image segmentation.

Monte Carlo Dosimetric Study of Percutaneous Vertebroplasty and Brachytherapy for the Treatment of Spinal Metastases.

Background: Percutaneous vertebroplasty employs bone cement for injecting into the fractured vertebral body (VB) caused by spinal metastases. Radioactive bone cement and also brachytherapy seeds have been utilized to suppress the tumor growth in the VB. Objective: This study aims to investigate the dose distributions of low-energy brachytherapy seeds, and to compare them to those of radioactive bone cement, by Monte Carlo simulation. Material and Methods: In this simulation study, nine CT scan images were imported in Geant4. For the simulation of brachytherapy, I-125, Cs-131, or Pd-103 seeds were positioned in the VB, and for the simulation of vertebroplasty, the VB was filled by a radioactive cement loaded by P-32, Ho-166, Y-90, or Sm-153 radioisotopes. The dose-volume histograms of the VB, and the spinal cord (SC) were obtained after segmentation, considering that the reference dose is the minimum dose covered 95% of the VB. Results: The SC sparing was improved by using beta-emitting cement because of their steep gradient dose distribution. I-125 seeds and Y-90 radioisotope showed better VB coverage for brachytherapy and vertebroplasty techniques, respectively. Pd-103 seeds and P-32 radioisotope showed better SC sparing for brachytherapy and vertebroplasty, respectively. The minimum mean doses that covered 100% of the VB were 62.0%, 56.5%, and 45.0% for I-125, Cs-131, and Pd-103 seeds, and 28.3%, 28.6%, 32.9%, and 17.7%, for P-32, Ho-166, Y-90, and Sm-153 sources, respectively. Conclusion: I-125 and Cs-131 seeds may be useful for large tumors filling the entire VB, and also for the extended tumors invading multiple vertebrae. Beta-emitting bone cement is recommended for tumors located near the SC.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations.

OBJECTIVE: To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). METHODS: In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. RESULTS: This study included 37 women (mean age 56.3 years; age range 36-83 years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging. CONCLUSIONS: The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network.

The cumulative activity map estimation are essential tools for patient specific dosimetry with high accuracy, which is estimated using biokinetic models instead of patient dynamic data or the number of static PET scans, owing to economical and time-consuming points of view. In the era of deep learning applications in medicine, the pix-to-pix (p2 p) GAN neural networks play a significant role in image translation between imaging modalities. In this pilot study, we extended the p2 p GAN networks to generate PET images of patients at different times according to a 60 min scan time after the injection of F-18 FDG. In this regard, the study was conducted in two sections: phantom and patient studies. In the phantom study section, the SSIM, PSNR, and MSE metric results of the generated images varied from 0.98-0.99, 31-34 and 1-2 respectively and the fine-tuned Resnet-50 network classified the different timing images with high performance. In the patient study, these values varied from 0.88-0.93, 36-41 and 1.7-2.2, respectively and the classification network classified the generated images in the true group with high accuracy. The results of phantom studies showed high values of evaluation metrics owing to ideal image quality conditions. However, in the patient study, promising results were achieved which showed that the image quality and training data number affected the network performance. This study aims to assess the feasibility of p2 p GAN network application for different timing image generation.

MEASUREMENT OF ENTRANCE SKIN DOSE AND ABSORBED DOSE TO DIFFERENT ORGANS IN DUAL-ENERGY X-RAY ABSORPTIOMETRY SCANS USING THERMOLUMINESCENCE DOSIMETRY.

Thermoluminescence dosimetry is considered as an effective method in estimating the absorbed doses to organs in different imaging modalities. The present study focuses on dosimetry in dual-energy X-ray absorptiometry scans, for patients, and phantoms in various imaging centres. The cubical LiF (Mg, Ti) thermoluminescence dosemeters were inserted inside the holes of the Rando phantom slabs, to measure the absorbed dose to different organs in the whole body and lumbar scans. According to the results the maximum entrance skin dose was found to be 202.06 µGy for Hologic discovery W, which uses the fan beam scanning mode. The Norland XR-800 device took the scans with a much lower dose, as it uses the pencil beam for scanning the patients. The results of the study show that the radiation beam type, patient thickness, imaging technique and scan time may affect the radiation dose received by patient.

Inactivation of SARS-CoV-2 by charged particles for Future Vaccine Production Applications: A Monte Carlo study.

The world is still suffering from the SARS-CoV-2 pandemic, and the number of infected people is still growing in many countries in 2022. Although great strides have been made to produce effective vaccines, efforts in this field should be accelerated, particularly due to the emergence of new variants. Using inactivated viruses is a conventional method of vaccine production. High levels of ionizing radiation can effectively inactivate viruses. Recently, studies on SARS-CoV-2 irradiation using low-LET radiations (e.g., gamma rays) have been performed. However, there are insufficient studies on the impact of charged particles on the inactivation of this virus. In this study, a realistic structure of SARS-CoV-2 is simulated by using Geant4 Monte Carlo toolkit, and the effect of electrons, protons, alphas, C-12, and Fe-56 ions on the inactivation of SARS-CoV-2 is investigated. The simulation results indicated that densely ionizing (high-LET) particles have the advantage of minimum number of damaged spike proteins per single RNA break. The RNA breaks induced by hydroxyl radicals produced in the surrounding water medium were significant only for electron beam radiation. Hence, indirect RNA breaks induced by densely ionizing particles is negligible. From a simulation standpoint, alpha particles (with energies up to 30 MeV) as well as C-12 ions (with energies up to 80 MeV/n), and Fe-56 ions (with any energy) can be introduced as particles of choice for effective SARS-CoV-2 inactivation.

Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights.

PURPOSE: To assess the effectiveness of deep learning algorithms in automated segmentation of magnetic resonance brain images for determining the enhanced tumor, the peri-tumoral edema, the necrotic/ non-enhancing tumor, and Normal tissue volumes. METHODS AND MATERIALS: A new deep neural network algorithm, Deep-Net, was developed for semantic segmentation of the glioblastoma tumors in MR images, using the Deeplabv3+ architecture, and the pre-trained Resnet18 initial weights. The MR image Dataset used for training the network was taken from the BraTS 2020 training set, with the ground truth labels for different tumor subregions manually drawn by a group of expert neuroradiologists. In this work, two multi-modal MRI scans, i.e., T1ce and FLAIR of 293 patients with high-grade glioma (HGG), were used for deep network training (Deep-Net). The performance of the network was assessed for different hyper-parameters, to obtain the optimum set of parameters. The similarity scores were used for the evaluation of the optimized network. RESULTS: According to the results of this study, epoch #37 is the optimum epoch giving the best global accuracy (97.53%), and loss function (0.14). The Deep-Net sensitivity in the delineation of the enhanced tumor is more than 90%. CONCLUSIONS: The results indicate that the Deep-Net was able to segment GBM tumors with high accuracy.

DESIGN AND FABRICATION OF A PEDIATRIC THYROID PHANTOM FOR USE IN RADIO-IODINE UPTAKE MEASUREMENT, IMAGE QUALITY CONTROL AND DOSIMETRY.

Estimating internal contamination from 131I for children in nuclear accidents is a crucial subject in the radiation protection field. Throughout this paper, an urgent and simple method was proposed for measuring 131I inside the pediatric thyroid gland by constructing a neck and thyroid phantom. For this purpose, CT scan images of healthy child's thyroids were obtained, and the sizes of different parts were determined by a 3D slicer image processing software. Girls with the body surface area between 0.95 and 1.05 were involved in this study. The fabricated phantom is composed of 5 cylindrical slabs of 2-cm thickness, and several small holes were constructed for TLD dosemeters near the thyroid gland and all other parts of the neck. The phantom was constructed utilizing a 3D printer with acrylonitrile butadiene styrene plastic. The thyroid phantom was filled with radioiodine-131, and calibration curves were plotted for contamination estimation. A nodular thyroid phantom was also constructed. The nodular phantom or the resolution phantom has 4 removable parts containing cylindrical holes with diameters of 3, 6, 9 and 12 mm. These holes on the thyroid glands can be filled with different activities of radionuclides to serve as hot and cold spots for quality control of nuclear medicine images. The results show that the designed phantom is applicable in different fields such as nuclear image quality and resolution tests, dosimetry and iodine thyroid uptake estimation in nuclear medicine departments, and nuclear emergency monitoring.

MEASUREMENT OF OPERATIONAL DOSIMETRY QUANTITIES FOR NUCLEAR MEDICINE STAFF.

According to the ALARA principle, exposure to radiation should be reduced as low as reasonably achievable. This principle is very important in nuclear medicine (NM), and different investigations have been performed by establishing protocols and standards for staff protection. This study aims to measure the operational quantities, personal dose equivalent, Hp (10), Hp (0.07) and Hp (3) for NM staff in Shiraz hospitals, and comparison with dose limits. Two types of dosimeters, TLD-100 and GR-200, were used in this study. In the first step, the calibration of dosimeters was performed using different phantoms. Then, a group of dosimeters was prepared and used for 1 month on the heads, wrists and chests of the staff for measurement of Hp (3), Hp (0.07) and Hp (10), respectively. The obtained values of Hp (10) were compared with the results of their personal dosimetry, film badge. The results of this study show good consistency in the measurements using the two dosimeters.

A New Dual-purpose Quality Control Dosimetry Protocol for Diagnostic Reference-level Determination in Computed Tomography.

A diagnostic reference level is an advisory dose level set by a regulatory authority in a country as an efficient criterion for protection of patients from unwanted medical exposure. In computed tomography, the direct dose measurement and data collection methods are commonly applied for determination of diagnostic reference levels. Recently, a new quality-control-based dose survey method was proposed by the authors to simplify the diagnostic reference-level determination using a retrospective quality control database usually available at a regulatory authority in a country. In line with such a development, a prospective dual-purpose quality control dosimetry protocol is proposed for determination of diagnostic reference levels in a country, which can be simply applied by quality control service providers. This new proposed method was applied to five computed tomography scanners in Shiraz, Iran, and diagnostic reference levels for head, abdomen/pelvis, sinus, chest, and lumbar spine examinations were determined. The results were compared to those obtained by the data collection and quality-control-based dose survey methods, carried out in parallel in this study, and were found to agree well within approximately 6%. This is highly acceptable for quality-control-based methods according to International Atomic Energy Agency tolerance levels (±20%).

The effect of tandem-ovoid titanium applicator on points A, B, bladder, and rectum doses in gynecological brachytherapy using 192Ir.

PURPOSE: The dosimetry procedure by simple superposition accounts only for the self-shielding of the source and does not take into account the attenuation of photons by the applicators. The purpose of this investigation is an estimation of the effects of the tandem and ovoid applicator on dose distribution inside the phantom by MCNP5 Monte Carlo simulations. MATERIAL AND METHODS: In this study, the superposition method is used for obtaining the dose distribution in the phantom without using the applicator for a typical gynecological brachytherapy (superposition-1). Then, the sources are simulated inside the tandem and ovoid applicator to identify the effect of applicator attenuation (superposition-2), and the dose at points A, B, bladder, and rectum were compared with the results of superposition. The exact dwell positions, times of the source, and positions of the dosimetry points were determined in images of a patient and treatment data of an adult woman patient from a cancer center. The MCNP5 Monte Carlo (MC) code was used for simulation of the phantoms, applicators, and the sources. RESULTS: The results of this study showed no significant differences between the results of superposition method and the MC simulations for different dosimetry points. The difference in all important dosimetry points was found to be less than 5%. CONCLUSIONS: According to the results, applicator attenuation has no significant effect on the calculated points dose, the superposition method, adding the dose of each source obtained by the MC simulation, can estimate the dose to points A, B, bladder, and rectum with good accuracy.

EFFECTIVE DOSE IN TWO DIFFERENT DENTAL CBCT SYSTEMS: NEWTOM VGi AND PLANMECA 3D MID.

Cone beam computed tomography, CBCT, is a kind of CT scanner producing conical diverging X-rays, in which a large area of a two-dimensional detector is irradiated in each rotation. Different investigations have been performed on dosimetry of dental CBCT. As there is no special protocol for dental CBCT, CT scan protocols are used for dosimetry. The purpose of this study is measurement of dose to head and neck organs in two CBCT systems, i.e. Planmeca 3D Mid (PM) and NewTom VGi (NT), using thermoluminescence dosimetry and Rando phantom. The thermoluminescent dosimetry (TLD)-100 chips were put at the position of different organs of the head and neck. Two TLD-100 chips were inserted at each position, the dose values were measured for several different field sizes, i.e. 8 × 8, 12 × 8 and 15 × 15 cm2 for NewTom, and 10 × 10 and 20 × 17 cm2 for Planmeca systems. According to the results, the average effective dose in PM is much more than the NT system in the same field size, because of the greater mAs values. For routine imaging protocols used for NT, the effective dose values are 70, 73 and 121 µSv for 8 × 8, 12 × 8 and 15 × 15 cm2 field sizes, respectively. In PM, the effective dose in 10 × 10 cm2 and 17 × 20 cm2 is 259 and 341 µSv, respectively.

Developing an Optimum Protocol for Thermoluminescence Dosimetry with GR-200 Chips using Taguchi Method.

Thermoluminescence dosimetry (TLD) is a powerful technique with wide applications in personal, environmental and clinical dosimetry. The optimum annealing, storage and reading protocols are very effective in accuracy of TLD response. The purpose of this study is to obtain an optimum protocol for GR-200; LiF: Mg, Cu, P, by optimizing the effective parameters, to increase the reliability of the TLD response using Taguchi method. Taguchi method has been used in this study for optimization of annealing, storage and reading protocols of the TLDs. A number of 108 GR-200 chips were divided into 27 groups, each containing four chips. The TLDs were exposed to three different doses, and stored, annealed and read out by different procedures as suggested by Taguchi Method. By comparing the signal-to-noise ratios the optimum dosimetry procedure was obtained. According to the results, the optimum values for annealing temperature (°C), Annealing Time (s), Annealing to Exposure time (d), Exposure to Readout time (d), Pre-heat Temperature (°C), Pre-heat Time (s), Heating Rate (°C/s), Maximum Temperature of Readout (°C), readout time (s) and Storage Temperature (°C) are 240, 90, 1, 2, 50, 0, 15, 240, 13 and -20, respectively. Using the optimum protocol, an efficient glow curve with low residual signals can be achieved. Using optimum protocol obtained by Taguchi method, the dosimetry can be effectively performed with great accuracy.