• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1747-1753
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• 2022

Compton imaging is the main method for locating radioactive hot spots emitting high-energy gamma-ray photons. In particular, this imaging method is crucial when the photon energy is too high for coded-mask aperture imaging methods to be effective or when a large field of view is required. Reconstruction of the photon source requires advanced Compton event processing algorithms to determine the exact position of the source. In this study, we introduce a novel method based on a Deep Learning algorithm with a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained on simulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. We show that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms, while computation time is significantly reduced and sensitivity is improved by a factor of ~5 in the Caliste configuration.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2204-2212
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• 2022

Recently, the demand for alpha imaging detectors for quantifying the distributions of alpha particles has increased in various fields. This study aims to reconstruct a high-resolution image from an alpha imaging detector by applying a super-spatial resolution method combined with the maximum-likelihood expectation maximization (MLEM) algorithm. To perform the super-spatial resolution method, several images are acquired while slightly moving the detector to predefined positions. Then, a forward model for imaging is established by the system matrix containing the mechanical shifts, subsampling, and measured point-spread function of the imaging system. Using the measured images and system matrix, the MLEM algorithm is implemented, which converges towards a high-resolution image. We evaluated the performance of the proposed method through the Monte Carlo simulations and phantom experiments. The results showed that the super-spatial resolution method was successfully applied to the alpha imaging detector. The spatial resolution of the resultant image was improved by approximately 12% using four images. Overall, the study's outcomes demonstrate the feasibility of the super-spatial resolution method for the alpha imaging detector. Possible applications of the proposed method include high-resolution imaging for alpha particles of in vitro sliced tissue and pre-clinical biologic assessments for targeted alpha therapy.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3700-3708
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• 2023

The stochastic origin ensembles method with resolution recovery (SOE-RR) has been proposed to reconstruct proton-induced prompt gammas (PGs), and the reconstructed PG image was used for range verification. However, due to low detection efficiency, the number of valid events is low. Such a low-count condition can degrade the accuracy of the SOE-RR method for proton range verification. In this study, we proposed two strategies to improve the reconstruction of the SOE-RR algorithm for low-count PG imaging. We also studied the number of iterations and repetitions required to achieve reliable range verification. We simulated a proton beam (10⁸ protons) irradiated on a water phantom and used a two-layer Compton camera to detect 4.44-MeV PGs. Our simulated results show that combining the SOE-RR algorithm with restricted volume (SOE-RR-RV) can reduce the error of the estimation of the Bragg peak position from 5.0 mm to 2.5 mm. We also found that the SOE-RR-RV algorithm initialized using a back-projection image could improve the convergence rate while maintaining accurate range verification. Finally, we observed that the improved SOE-RR algorithm set for 60,000 iterations and 25 repetitions could provide reliable PG images. Based on the proposed reconstruction strategies, the SOE-RR algorithm has the potential to achieve a positioning error of 2.5 mm for low-count PG imaging.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2349-2351
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• 2010

Photo Acoustic Tomography (PAT) is a hybrid imaging modality which combines high contrast of optical imaging and spatial resolution of ultrasound imaging, thus it is suitable to image biological tissue noninvasively. Laser-induced photoacoustic signals were measured from a sample by means of an unfocused ultrasound transducer, then PAT image was reconstructed based on a universal back-projection algorithm. To evaluate the feasibility of our system, phantom test was performed, consequently, the PAT images obtained using our system showed highly analogous shape and volume with those of the phantom. This result demonstrated that our system can provide a powerful tool for imaging the substructure of biological tissue in non-invasive manner.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.519-541
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• 2001

Magnetic Resonance Electrical Impedance Tomography(MREIT) is a new medical imaging technique for the cross-sectional conductivity distribution of a human body using both EIT(Electrical Impedance Tomography) and MRI(Magnetic Resonance Imaging) system. MREIT system was designed to enhance EIT imaging system which has inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. MREIT utilizes a recent CDI (Current Density Imaging) technique of measuring the internal current density by means of MRI technique. In this paper, a mathematical modeling for MREIT and image reconstruction method called the alternating J-substitution algorithm are presented. Computer simulations show that the alternating J-substitution algorithm provides accurate high-resolution conductivity images.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.7
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• pp.656-665
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• 2015

In this paper, we improved the clarity and quality of the radar imaging by applying motion compensation for time-frequency transform in B-ISAR imaging. The proposed motion compensation algorithm using UWB radar is verified. B-ISAR algorithm procedure and time-frequency transform for improved motion compensation are provided for theoretical ground. The image was created by a UWB Radar B-ISAR imaging algorithm method. Also, creating a B-ISAR imaging algorithm for motion compensation of time-frequency transformation method was used. The B-ISAR Imaging algorithm is implemented using STFT(Short-Time Fourier Transform), GWT(Gabor Wavelet Transform), and WVD(Wigner-Ville Distribution) approaches. The performance of STFT is compared with the GWT and WVD algorithms. It is found that the WVD image shows more clarity and decreased spread phenomenon than other methods.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.650-654
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• 2000

Particle Imaging Velocimetry (PIV) is becoming one of essential methods to measure velocity fields of fluid flows. In this paper, a genetic algorithm capable of tracking same particle pairs on two separated images is introduced. The fundamental of the developed technique is based on that on-to-one correspondence is found between two tracer particles selected in two image planes by taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm is expressed by physical distances between the selected tracer particles. The capability of the developed genetic algorithm is verified by a computer simulation on a farced vortex flow.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.243-250
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• 1998

Reconstruction aspects of spiral scan imaging for ultra fast magnetic resonance imagine(MRI) have been investigated with polar and rectangular coordinates-based reconstruction. For the reconstruction of the spiral scan imaging, acquired data in spiral trjectory should be converted to polar or rectangular grids, where interpolation techniques are used. Various reconstruction algorithms for spiral scan imaging are tested, and reconstructed image qualities are compared with computed phantom. An improved reconstruction algorithm with dc-offset correction in projection domain is proposed, which provides the best reconstructed image quality from the simulation. Image artifact with existing algorithms is completely removed with the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.530-535
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• 2016

In this paper, the TTSE (Target size and Triangulation-based target Size Estimator) algorithm is proposed to estimate static target size in an imaging environment. The target size information is an important factor for accurate imaging target tracking. However, the imaging sensor cannot generate distance between the missile and target to calculate the target size. To overcome the problem, we propose the TTSE algorithm, which is based on target size and triangulation. The proposed method performance is tested in a target intercept scenario. The experiment results show that the proposed algorithm has better performance than the conventional algorithm (ET-TSE) for accurate CCD target size estimation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11C
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• pp.898-904
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• 2008

In this paper, we propose a fast algorithm to generate the array of elemental image in a computer generated integral imaging system. It generates the array of elemental image using depth information, needs less computing time to produce the result by using the concept of boundary area and computing the voxel within boundary area. By comparing the computing time of proposed algorithm with that of the existing algorithm theoretically and experimently, we proved the efficiency of this algorithm.