• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2191-2208
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• 2022

Motion blur in PET (Positron emission tomography) images induced by respiratory motion will reduce the quality of imaging. Although exiting methods have positive performance for respiratory motion correction in medical practice, there are still many aspects that can be improved. In this paper, an improved 3D unsupervised framework, Res-Voxel based on U-Net network was proposed for the motion correction. The Res-Voxel with multiple residual structure may improve the ability of predicting deformation field, and use a smaller convolution kernel to reduce the parameters of the model and decrease the amount of computation required. The proposed is tested on the simulated PET imaging data and the clinical data. Experimental results demonstrate that the proposed achieved Dice indices 93.81%, 81.75% and 75.10% on the simulated geometric phantom data, voxel phantom data and the clinical data respectively. It is demonstrated that the proposed method can improve the registration and correction performance of PET image.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.21-26
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• 2024

The one of the famous deep learning models for object detection task is you only look once version 5 (YOLOv5) framework based on the one stage architecture. In addition, YOLOv5 model indicated high performance for accurate lesion detection using the bottleneck CSP layer and skip connection function. The purpose of this study was to evaluate the performance of YOLOv5 framework according to various hyperparameters in position emission tomogrpahy (PET) phantom images. The dataset was obtained from QIN PET segmentation challenge in 500 slices. We set the bounding box to generate ground truth dataset using labelImg software. The hyperparameters for network train were applied by changing optimization function (SDG, Adam, and AdamW), activation function (SiLU, LeakyRelu, Mish, and Hardwish), and YOLOv5 model size (nano, small, large, and xlarge). The intersection over union (IOU) method was used for performance evaluation. As a results, the condition of outstanding performance is to apply AdamW, Hardwish, and nano size for optimization function, activation function and model version, respectively. In conclusion, we confirmed the usefulness of YOLOv5 network for object detection performance in nuclear medicine images.

• Clinical Endoscopy
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• v.56 no.2
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• pp.229-238
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• 2023

Background/Aims: Shear wave elastography (SWE) is used for liver fibrosis staging based on stiffness measurements. It can be performed using endoscopic ultrasound (EUS) or a transabdominal approach. Transabdominal accuracy can be limited in patients with obesity because of the thick abdomen. Theoretically, EUS-SWE overcomes this limitation by internally assessing the liver. We aimed to define the optimal technique for EUS-SWE for future research and clinical use and compare its accuracy with that of transabdominal SWE. Methods: Benchtop study: A standardized phantom model was used. The compared variables included the region of interest (ROI) size, depth, and orientation and transducer pressure. Porcine study: Phantom models with varying stiffness values were surgically implanted between the hepatic lobes. Results: For EUS-SWE, a larger ROI size of 1.5 cm and a smaller ROI depth of 1 cm demonstrated a significantly higher accuracy. For transabdominal SWE, the ROI size was nonadjustable, and the optimal ROI depth ranged from 2 to 4 cm. The transducer pressure and ROI orientation did not significantly affect the accuracy. There were no significant differences in the accuracy between transabdominal SWE and EUS-SWE in the animal model. The variability among the operators was more pronounced for the higher stiffness values. Small lesion measurements were accurate only when the ROI was entirely situated within the lesion. Conclusions: We defined the optimal viewing windows for EUS-SWE and transabdominal SWE. The accuracy was comparable in the non-obese porcine model. EUS-SWE may have a higher utility for evaluating small lesions than transabdominal SWE.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.59-67
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• 2009

In this paper, we have proposed the structure of the IMADF(improved modified multiplication-free adaptive filter) to cancel the adaptive noise in biomedical signals. The IMADF structure use the one-step predicted filter in the multiplication-free adaptive digital filter(MADF) structure using the DPCM and Sign algorithm. And then we use the heart phantom model based on the magnetocardiographic (MCG) to test the biomedical signals and analyze the signal of it. Their functions of the heart phantom occur from the multidipole current source. This can play role the same in the real function of the human heart to study it. In the experimental results, the IMADF algorithm has reduced the computational complexity by use of only the addition operation without a multiplier. Also, under the condition of identical stationary-state error, it could obtain the stabled convergence characteristics that the IMADF algorithm is almost same as the sign algorithm, but is better than the MADF algorithm. Here, this algorithm has effective characteristics when the correlation of the input signal is highly.

• Korean Journal of Digital Imaging in Medicine
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• v.14 no.1
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• pp.37-42
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• 2012

Pediatric head and neck phantom, using the rate by focusing distance and grid images, Image J using the Quality Assessment and Dose Area Product compared. X-ray laboratory equipment due to the Philips Digital DIAGNOST a 110 cm FFD set and using ACE Non-grid, focusing distance 110 cm (12 : 1), 140 cm (12 : 1), 180 cm (8 : 1) Focused grid, Acryl Phantom (Fluke Model 76-2 Series Phantom) 15.24 cm, by resolution chart image acquisition, image evaluation program (Image J Ver. 1.4.3.67, USA) imaging experiments were analyzed using. Dose Area Product in the Non Grid 0.028 $mGy{\cdot}cm^2$, focusing distance 110 cm (12 : 1), the 0.129 $mGy{\cdot}cm^2$, 140 cm (12 : 1), the 0.135 $mGy{\cdot}cm^2$, 180 cm (8 : 1) was measured with a 0.110 $mGy{\cdot}cm^2$ Non Grid, focusing distance 110 cm (12 : 1), 140 cm (12 : 1), 180 cm (8 : 1) Image obtained when grid using the image J program focusing distance 110 cm with grid based on the measured SNR and PSNR Non Grid if the SNR the 17.307 dB, PSNR of the 20.002 dB, if the SNR 28.755 dB, PSNR was measured by the 31.451 dB. Image J image analysis through the streets, rather than focusing on grid by the rate that could see an increase in dose. Select the grid by a small dose rate reduction is possible.

• Journal of Biomedical Engineering Research
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• v.39 no.3
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• pp.116-123
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• 2018

This study is a model experimental study using a phantom to propose an optimized brain CT scan protocol that can reduce the radiation dose of a patient and remain quality of image. We investigate the CT scan parameters of brain CT in clinical medical institutions and to measure the important parameters that determine the quality of CT images. We used 52 multislice spiral CT (SOMATOM Definition AS+, Siemens Healthcare, Germany). The scan parameters were tube voltage (kVp), tube current (mAs), scan time, slice thickness, pitch, and scan field of view (SFOV) directly related to the patient's exposure dose. The CT dose indicators were CTDIvol and DLP. The CT images were obtained while increasing the imaging conditions constantly from the phantom limit value (Q1) to the maximum value (Q4) for AAPM CT performance evaluation. And statistics analyzed with Pearson's correlation coefficients. The result of tube voltage that the increase in tube voltage proportionally increases the variation range of the CT number. And similar results were obtained in the qualitative evaluation of the CT image compared to the tube voltage of 120 kVp, which was applied clinically at 100 kVp. Also, the scan conditions were appropriate in the tube current range of 250 mAs to 350 mAs when the tube voltage was 100 kVp. Therefore, by applying the proposed brain CT scanning parameters can be reduced the radiation dose of the patient while maintaining quality of image.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.158-164
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• 2011

Breast cancer is the most frequently appearing cancer in women, these days. To reduce mortality of breast cancer, periodic check-up is strongly recommended. X-ray mammography is one of powerful diagnostic imaging systems to detect 50~100 um micro-calcification which is the early sign of breast cancer. Although x-ray mammography has very high spatial resolution, it is not easy yet to distinguish cancerous tissue from normal tissues in mammograms and new tissue characterizing methods are required. Recently ultrasound elastography technique has been developed, which uses the phenomenon that cancerous tissue is harder than normal tissues. However its spatial resolution is not enough to detect breast cancer. In order to develop a new elastography system with high resolution we are developing x-ray elasticity imaging technique. It uses the small differences of tissue positions with and without external breast compression and requires an algorithm to detect tissue displacement. In this paper, computer simulation is done for preliminary study of x-ray elasticity imaging. First, 3D x-ray breast phantom for modeling woman's breast is created and its elastic model for FEM (finite element method) is generated. After then, FEM experiment is performed under the compression of the breast phantom. Using the obtained displacement data, 3D x-ray phantom is deformed and the final mammogram under the compression is generated. The simulation result shows the feasibility of x-ray elasticity imaging. We think that this preliminary study is helpful for developing and verifying a new algorithm of x-ray elasticity imaging.

• The Journal of Advanced Prosthodontics
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• v.15 no.5
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• pp.271-280
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• 2023

PURPOSE. This in vitro study aimed to compare the accuracy of the conventional facebow system and the newly developed POP (PNUD (Pusan National University Dental School) Occlusal Plane) bow system for occlusal plane transfer in asymmetric ear position. MATERIALS AND METHODS. Two dentists participated in this study, one was categorized as Experimenter 1 and the other as Experimenter 2 based on their clinical experience with the facebow (1F, 2F) and POP bow (1P, 2P) systems. The vertical height difference between the two ears of the phantom model was set to 3 mm. Experimenter 1 and Experimenter 2 performed the facebow and POP bow systems on the phantom model 10 times each, and the transfer accuracy was analyzed. The accuracy was evaluated by measuring the angle between the reference virtual plane (RVP) of the phantom model and the experimental virtual plane (EVP) of the upper mounting plate through digital superimposition. All data were statistically analyzed using a paired t-test (P < .05). RESULTS. Regardless of clinical experience, the POP bow system (0.53° ± 0.30 (1P) and 0.19° ± 0.18 (2P) for Experimenter 1 and 2, respectively) was significantly more accurate than the facebow system (1.88° ± 0.50 (1F) and 1.34° ± 0.25 (2F), respectively) in the frontal view (P < .05). In the sagittal view, no significant differences were found between the POP bow system (0.92° ± 0.50 (1P) and 0.73° ± 0.42 (2P) for Experimenter 1 and 2, respectively) and the facebow system (0.82° ± 0.49 (1F) and 0.60° ± 0.39 (2F), respectively), regardless of clinical experience (P > .05). CONCLUSION. In cases of asymmetric ear position, the POP bow system may transfer occlusal plane information more accurately than the facebow system in the frontal view, regardless of clinical experience.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.121-122
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• 2002

The main principle of radiation therapy is to deliver optimum dose to tumor to increase tumor cure probability while minimizing dose to critical normal structure to reduce complications. RTP system is required for proper dose plan in radiation therapy treatment. The main goal of this research is to develop dose model for photon, electron, and brachytherapy, and to display dose distribution on patient images with optimum process. The main items developed in this research includes: (l) user requirements and quality control; analysis of user requirement in RTP, networking between RTP and relevant equipment, quality control using phantom for clinical application (2) dose model in RTP; photon, electron, brachytherapy, modifying dose model (3) image processing and 3D visualization; 2D image processing, auto contouring, image reconstruction, 3D visualization (4) object modeling and graphic user interface; development of total software structure, step-by-step planning procedure, window design and user-interface. Our final product show strong capability for routine and advance RTP planning.

• Journal of Acupuncture Research
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• v.40 no.1
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• pp.44-52
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• 2023

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion: The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.