Pixel-Grouping G(E) Functions for Estimating Dose Rates from Unknown Source Distributions with a Position-Sensitive Detector.

Estimating accurate radiation doses when a radioactive source's location is unknown can protect workers from radiation exposure. Unfortunately, depending on a detector's shape and directional response variations, conventional G(E) function can be prone to inaccurate dose estimations. Therefore, this study estimated accurate radiation doses regardless of source distributions, using the multiple G(E) function groups (i.e., pixel-grouping G(E) functions) within a position-sensitive detector (PSD), which records the response position and energy inside the detector. Investigations revealed that, compared with the conventional G(E) function when source distributions are unknown, this study's proposed pixel-grouping G(E) functions improved dose estimation accuracy by more than 1.5 times. Furthermore, although the conventional G(E) function produced substantially larger errors in certain directions or energy ranges, the proposed pixel-grouping G(E) functions estimate doses with more uniform errors at all directions and energies. Therefore, the proposed method estimates the dose with high accuracy and provides reliable results regardless of the location and energy of the source.

Ghost imaging with Bayesian denoising method.

We propose a Bayesian denoising method to improve the quality of ghost imaging. The proposed method achieved the highest PSNR and SSIM in both binary and gray-scale targets with fewer measurements. Experimentally, it obtained a reconstructed image of a USAF target where the PSNR and SSIM of the image were up to 12.80 dB and 0.77, respectively, whereas those of traditional ghost images were 7.24 dB and 0.28 with 3000 measurements. Furthermore, it was robust against additive Gaussian noise. Thus, this method could make the ghost imaging technique more feasible as a practical application.

Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning.

Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties.

Discrete Convolution-Based Energy Spectrum Configuring Method for the Analysis of the Intrinsic Radiation of 176Lu.

There has been considerable interest in inorganic scintillators based on lutetium due to their favorable physical properties. Despite their advantages, lutetium-based scintillators could face issues because of the natural occurring radioisotope of 176Lu that is contained in natural lutetium. In order to mitigate its potential shortcomings, previous works have studied to understand the energy spectrum of the intrinsic radiation of 176Lu (IRL). However, few studies have focused on the various principal types of photon interactions with matter; in other words, only the full-energy peak according to the photoelectric effect or internal conversion have been considered for understanding the energy spectrum of IRL. Thus, the approach we have used in this study considers other principal types of photon interactions by convoluting each energy spectrum with combinations for generating the spectrum of the intrinsic radiation of 176Lu. From the results, we confirm that the method provides good agreement with the experiment. A significant contribution of this study is the provision of a new approach to process energy spectra induced by mutually independent radiation interactions as a single spectrum.

Reconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder.

Plastic scintillation detectors are widely utilized in radiation measurement because of their unique characteristics. However, they are generally used for counting applications because of the energy broadening effect and the absence of a photo peak in their spectra. To overcome their weaknesses, many studies on pseudo spectroscopy have been reported, but most of them have not been able to directly identify the energy of incident gamma rays. In this paper, we propose a method to reconstruct Compton edges in plastic gamma spectra using an artificial neural network for direct pseudo gamma spectroscopy. Spectra simulated using MCNP 6.2 software were used to generate training and validation sets. Our model was trained to reconstruct Compton edges in plastic gamma spectra. In addition, we aimed for our model to be capable of reconstructing Compton edges even for spectra having poor counting statistics by designing a dataset generation procedure. Minimum reconstructible counts for single isotopes were evaluated with metric of mean averaged percentage error as 650 for 60Co, 2000 for 137Cs, 3050 for 22Na, and 3750 for 133Ba. The performance of our model was verified using the simulated spectra measured by a PVT detector. Although our model was trained using simulation data only, it successfully reconstructed Compton edges even in measured gamma spectra with poor counting statistics.

Uncertainty Estimation of the Dose Rate in Real-Time Applications Using Gaussian Process Regression.

Major standard organizations have addressed the issue of reporting uncertainties in dose rate estimations. There are, however, challenges in estimating uncertainties when the radiation environment is considered, especially in real-time dosimetry. This study reports on the implementation of Gaussian process regression based on a spectrum-to-dose conversion operator (i.e., G(E) function), the aim of which is to deal with uncertainty in dose rate estimation based on various irradiation geometries. Results show that the proposed approach provides the dose rate estimation as a probability distribution in a single measurement, thereby increasing its real-time applications. In particular, under various irradiation geometries, the mean values of the dose rate were closer to the true values than the point estimates calculated by a G(E) function obtained from the anterior-posterior irradiation geometry that is intended to provide conservative estimates. In most cases, the 95% confidence intervals of uncertainties included those conservative estimates and the true values over the range of 50-3000 keV. The proposed method, therefore, not only conforms to the concept of operational quantities (i.e., conservative estimates) but also provides more reliable results.

Integrated Circuit Design for Radiation-Hardened Charge-Sensitive Amplifier Survived up to 2 Mrad.

According to the continuous development of metal-oxide semiconductor (MOS) fabrication technology, transistors have naturally become more radiation-tolerant through steadily decreasing gate-oxide thickness, increasing the tunneling probability between gate-oxide and channel. Unfortunately, despite this radiation-hardened property of developed transistors, the field of nuclear power plants (NPPs) requires even higher radiation hardness levels. Particularly, total ionizing dose (TID) of approximately 1 Mrad could be required for readout circuitry under severe accident conditions with 100 Mrad around a reactor in-core required. In harsh radiating environments such as NPPs, sensors such as micro-pocket-fission detectors (MPFD) would be a promising technology to be operated for detecting neutrons in reactor cores. For those sensors, readout circuits should be fundamentally placed close to sensing devices for minimizing signal interferences and white noise. Therefore, radiation hardening ability is necessary for the circuits under high radiation environments. This paper presents various integrated circuit designs for a radiation hardened charge-sensitive amplifier (CSA) by using SiGe 130 nm and Si 180 nm fabrication processes with different channel widths and transistor types of complementary metal-oxide-semiconductor (CMOS) and bipolar CMOS (BiCMOS). These circuits were tested under γ-ray environment with Cobalt-60 of high level activity: 490 kCi. The experiment results indicate amplitude degradation of 2.85%-34.3%, fall time increase of 201-1730 ns, as well as a signal-to-noise ratio (SNR) of 0.07-11.6 dB decrease with irradiation dose increase. These results can provide design guidelines for radiation hardening operational amplifiers in terms of transistor sizes and structures.

Radioisotope Identification and Nonintrusive Depth Estimation of Localized Low-Level Radioactive Contaminants Using Bayesian Inference.

Obtaining the in-depth information of radioactive contaminants is crucial for determining the most cost-effective decommissioning strategy. The main limitations of a burial depth analysis lie in the assumptions that foreknowledge of buried radioisotopes present at the site is always available and that only a single radioisotope is present. We present an advanced depth estimation method using Bayesian inference, which does not rely on those assumptions. Thus, we identified low-level radioactive contaminants buried in a substance and then estimated their depths and activities. To evaluate the performance of the proposed method, several spectra were obtained using a 3 × 3 inch hand-held NaI (Tl) detector exposed to Cs-137, Co-60, Na-22, Am-241, Eu-152, and Eu-154 sources (less than 1µCi) that were buried in a sandbox at depths of up to 15 cm. The experimental results showed that this method is capable of correctly detecting not only a single but also multiple radioisotopes that are buried in sand. Furthermore, it can provide a good approximation of the burial depth and activity of the identified sources in terms of the mean and 95% credible interval in a single measurement. Lastly, we demonstrate that the proposed technique is rarely susceptible to short acquisition time and gain-shift effects.

A Bayesian Approach for Remote Depth Estimation of Buried Low-Level Radioactive Waste with a NaI(Tl) Detector.

This study reports on the implementation of Bayesian inference to improve the estimation of remote-depth profiling for low-level radioactive contaminants with a low-resolution NaI(Tl) detector. In particular, we demonstrate that this approach offers results that are more reliable because it provides a mean value with a 95% credible interval by determining the probability distributions of the burial depth and activity of a radioisotope in a single measurement. To evaluate the proposed method, the simulation was compared with experimental measurements. The simulation showed that the proposed method was able to detect the depth of a Cs-137 point source buried below 60 cm in sand, with a 95% credible interval. The experiment also showed that the maximum detectable depths for weakly active 0.94-µCi Cs-137 and 0.69-µCi Co-60 sources buried in sand was 21 cm, providing an improved performance compared to existing methods. In addition, the maximum detectable depths hardly degraded, even with a reduced acquisition time of less than 60 s or with gain-shift effects; therefore, the proposed method is appropriate for the accurate and rapid non-intrusive localization of buried low-level radioactive contaminants during in situ measurement.

Estimation of ambient dose equivalent rate with a plastic scintillation detector using the least-square and first-order methods-based G(E) function.

Dose-rate monitoring instruments measure the ambient dose equivalent and hence are crucial for protecting workers from radiation exposure. Although plastic scintillation detectors (PSDs) are ideal equipment for dosimetry, they are rarely used owing to the lower detection efficiency than other scintillation detectors. In this study, we acquired ten types of G(E) functions to utilize a PSD in spectroscopic dosimetry using the least-square and first-order methods. The energy response of PSD was much improved in terms of dose evaluation.

Pulse height estimation and pulse shape discrimination in pile-up neutron and gamma ray signals from an organic scintillation detector using multi-task learning.

We developed a multi-tasking deep learning model for simultaneous pulse height estimation and pulse shape discrimination for pile-up n/γ signals. Compared with single-tasking models, our model showed better spectral correction performance with higher recall for neutrons. Further, it achieved more stable neutron counting with less signal loss and a lower error rate in the predicted gamma ray spectra. Our model can be applied to a dual radiation scintillation detector to discriminatively reconstruct each radiation spectrum for radioisotope identification and quantitative analysis.

Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra.

Radioisotope identification using a plastic scintillation detector has been a challenging issue because of the poor spectral resolution and low cross-sections of these types of detectors when used for photoelectric absorption. In this paper, we propose an algorithm that identifies a single radioisotope and multiple radioisotopes from the gamma spectrum of a plastic scintillator using an artificial neural network. The spectra were simulated using Monte Carlo N-Particle Transport Code 6 to formulate the training set, and the spectra were measured by a two-inch EJ-200 to create the test set (1440 spectra in total). The ANN-based algorithm presented here ensures an identification accuracy of 98.9% for a single radioisotope and 99.1% for multiple radioisotopes. Even if the spectra were intentionally shifted by 36 keV for low and high energies, the trained ANN predicts radioisotopes with high accuracy. In addition, we have determined the minimal required number of detected counts to identify the radioisotope with 5% false negative and false positive.

Portal pyaemia secondary to colonic perforation.

Portal pyaemia or pylephlebitis is a form of septic (often suppurative) thrombophlebitis of the portal venous system. It may develop as a complication of intra-abdominal sepsis, such as diverticulitis or appendicitis. Patients typically present with a high fever that is sometimes accompanied by jaundice. We report a case of portal pyaemia associated with multiple liver abscesses and discuss the medical and surgical management of this condition.

EVALUATION OF EYE LENS DOSE TO WORKERS IN THE STEAM GENERATOR AT THE KOREAN OPTIMIZED POWER REACTOR 1000.

ICRP (2011) revised the dose limit to the eye lens to 20 mSv/y based on a recent epidemiological study of radiation-induced cataracts. Maintenance of steam generators at nuclear power plants is one of the highest radiation-associated tasks within a non-uniform radiation field. This study aims to evaluate eye lens doses in the steam generators of the Korean OPR1000 design. The source term was characterized based on the CRUD-specific activity, and both the eye lens dose and organ dose were simulated using MCNP6 combined with an ICRP voxel phantom and a mesh phantom, respectively. The eye lens dose was determined to be 5.39E-02-9.43E-02 Sv/h, with a negligible effect by beta particles. As the effective dose was found to be 0.81-1.21 times the lens equivalent dose depending on the phantom angles, the former can be used to estimate the lens dose in the SG of the OPR1000 for radiation monitoring purposes.

Radiation safety analysis for the A-BNCT facility in Korea.

A Proton Accelerator based Boron Neutron Capture Therapy (A-BNCT) facility is under development in Korea. Neutron beams for treatment are produced from a beryllium (Be) target and an 8 mA, 10 MeV proton beam. The purpose of the research is a radiation shielding analysis and an activation analysis for the facility design satisfying the radiation safety requirements as well as obtaining an operating license for the radiation facility according to a domestic nuclear commissioning procedure. The radiation shielding analysis was performed using the MCNPX computational particle transport code. The radiation source terms in the facility were evaluated and utilized in the shielding calculations. The minimum concrete thickness satisfying the designated dose rate of 5 µSv/h for the worker's area and 0.25 µSv/h for the public area were estimated and applied to the design. For an assessment of the radiation safety inside the facility, the dose rates were evaluated at several positions, such as behind the shielding door, around the primary barriers near the radiation sources, and in the penetrations of the ducts. The dose rate distribution was mapped for verification of the radiation safety for the entire facility. An activation analysis was carried out for the concrete walls, air, target assembly, beryllium target, and cooling water using FISPACT-2010 code. Concentrations of the activation products and dose rate induced by the radionuclides after shutdown were evaluated for the purpose of safe operation of the facility. The results were reviewed with the radiation safety regulations in Korea. As a result, it was proved that the final facility design satisfies the safety requirements.

PLASTIC SCINTILLATOR FOR RADIATION DOSIMETRY.

Inorganic scintillators, composed of high-atomic-number materials such as the CsI(Tl) scintillator, are commonly used in commercially available a silicon diode and a scintillator embedded indirect-type electronic personal dosimeters because the light yield of the inorganic scintillator is higher than that of an organic scintillator. However, when it comes to tissue-equivalent dose measurements, a plastic scintillator such as polyvinyl toluene (PVT) is a more appropriate material than an inorganic scintillator because of the mass energy absorption coefficient. To verify the difference in the absorbed doses for each scintillator, absorbed doses from the energy spectrum and the calculated absorbed dose were compared. From the results, the absorbed dose of the plastic scintillator was almost the same as that of the tissue for the overall photon energy. However, in the case of CsI, it was similar to that of the tissue only for a photon energy from 500 to 4000 keV. Thus, the values and tendency of the mass energy absorption coefficient of the PVT are much more similar to those of human tissue than those of the CsI.

CALCULATION OF GAMMA SPECTRA IN A PLASTIC SCINTILLATOR FOR ENERGY CALIBRATIONAND DOSE COMPUTATION.

Plastic scintillation detectors have practical advantages in the field of dosimetry. Energy calibration of measured gamma spectra is important for dose computation, but it is not simple in the plastic scintillators because of their different characteristics and a finite resolution. In this study, the gamma spectra in a polystyrene scintillator were calculated for the energy calibration and dose computation. Based on the relationship between the energy resolution and estimated energy broadening effect in the calculated spectra, the gamma spectra were simply calculated without many iterations. The calculated spectra were in agreement with the calculation by an existing method and measurements.

Artifacts associated with implementation of the Grangeat formula.

To compensate for image artifacts introduced in approximate cone-beam reconstruction, exact cone-beam reconstruction algorithms are being developed for medical x-ray CT. Although the exact cone-beam approach is theoretically error-free, it is subject to image artifacts due to the discrete nature of numerical implementation. We report a study on image artifacts associated with the Grangeat algorithm as applied to a circular scanning locus. Three types of artifacts are found, which are thorn, wrinkle, and V-shaped artifacts. The thorn pattern is created by inappropriate extrapolation into the shadow zone in the radon domain. If the shadow zone is filled in with continuous data, the thorn artifacts along the boundary of the shadow zone can be removed. The wrinkle appearance arises if interpolated first derivatives of the radon data are not smooth between adjacent detector planes. In particular, the nearest-neighbor interpolation method should not be used. If the number of projections is not small, the bilinear interpolation method is effective to suppress the wrinkle artifacts. The V-shaped artifacts on the meridian plane come from the line integrations through the transition zones where derivative data change abruptly. Two remedies are to increase the sampling rate and suppress data noise.

Characterization of dual layer phoswich detector performance for small animal PET using Monte Carlo simulation.

A positron emission tomograph dedicated to small animal imaging should have high spatial resolution and sensitivity, and dual layer scintillators have been developed for this purpose. In this study, simulations were performed to optimize the order and the length of each crystal of a dual layer phoswich detector, and to evaluate the possibility of measuring signals from each layer of the phoswich detector. A simulation tool GATE was used to estimate the sensitivity and resolution of a small PET scanner. The proposed scanner is based on dual layer phoswich detector modules arranged in a ring of 10 cm diameter. Each module is composed of 8 x 8 arrays of phoswich detectors consisting of LSO and LuYAP with a 2 mm x 2 mm sensitive area coupled to a Hamamatsu R7600-00-M64 PSPMT. The length of the front layer of the phoswich detector varied from 0 to 10 mm at 1 mm intervals, and the total length (LSO + LuYAP) was fixed at 20 mm. The order of the crystal layers of the phoswich detector was also changed. Radial resolutions were kept below 3.4 mm and 3.7 mm over 8 cm FOV, and sensitivities were 7.4% and 8.0% for LSO 5 mm-LuYAP 15 mm, and LuYAP 6 mm-LSO 14 mm phoswich detectors, respectively. Whereas, high and uniform resolutions were achieved by using the LSO front layer, higher sensitivities were obtained by changing the crystal order. The feasibilities for applying crystal identification methods to phoswich detectors consisting of LSO and LuYAP were investigated using simulation and experimentally derived measurements of the light outputs from each layer of the phoswich detector. In this study, the optimal order and lengths of the dual layer phoswich detector were derived in order to achieve high sensitivity and high and uniform radial resolution.

Characterization of silicon photomultipliers at National Nano-Fab Center for PET-MR.

The silicon photomultipliers (SiPMs) were fabricated for magnetic resonance compatible positron emission tomography (PET) applications using customized CMOS processes at National NanoFab Center. Each micro-cell consists of a shallow n+/p well junction on a p-type epitaxial wafer and passive quenching circuit was applied. The size of the SiPM is 3 × 3 mm(2) and the pitch of each micro-cell is 65 µm. In this work, several thousands of SiPMs were packaged and tested to build a PET ring detector which has a 60 mm axial and 390 mm radial field of view. I-V characteristics of the SiPMs are shown good uniformity and breakdown voltage is around 20 V. The photon detection efficiency was measured via photon counting method and the maximum value was recorded as 16% at 470 nm. The gamma ray spectrum of a Ge-68 isotope showed nearly 10% energy resolution at 511 keV with a 3 × 3 × 20 mm(3) LYSO crystal.