• Imaging Science in Dentistry
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• v.52 no.4
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.426-434
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• 2022

Bubbles generated by various causes in the ocean are known to persist for long periods of time. Although the volume occupied by bubbles in the ocean is small, the presence of bubbles in ocean due to resonance and attenuation greatly affects the acoustic properties. Accordingly, bistatic reverberation experiment was performed in the ocean where artificial bubbles exist. A number of transducers and receivers were installed on 6 buoys arranged in a hexagonal shape, and blowing agents were dropped in the center of the buoy to generate bubbles. For reverberation modeling that reflects acoustic characteristics changed by bubbles, the spatial distribution of bubbles was estimated using video data and received signals. A measurement-based bubble spectral shape was used, and it was assumed that the bubble density within the spatial distribution of the estimated bubble was the same. As a result, it was confirmed that the bubble reverberation was simulated in a time similar to the measured data regardless of the bubble density, and the bubble reverberation level similar to the measured data was simulated at a void fraction of about 10^-7 ~ 10^-6.8.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.4
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• pp.34-39
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• 2013

For orthopedic artificial metal stainless steel, the magnetization of the magnetic field due to the presence of the titanium was to observe the change. Magnetic field meter (Tesla meters) a certain distance (ISO Centre) 1.5 Tesla magnetic field, the magnetization in the center with the passage of time were measured. Therefore, these artificial metal clip shape and magnetization of the sample size to produce a ferromagnetic material for comparison is the experimental dependence. For comparison of the experimental dependence of the magnetization, thereby producing a test piece size such as shaping prosthetic metal Clip is a ferromagnetic material. The experimental results, the metal orthopedic implants, there was no change in the magnetization indicated by ferromagnetic material in its natural state. However, in a magnetic field of 1.5T (Tesla), showed a sensitivity that is magnetized rapidly compared to the ferromagnetic material. In conclusion, high in the order of Clip, Stainless, of Titanium, the degree of dependence of the magnetization intensity of magnetization was the order Stainless, Titanium, of Clip in a magnetic field.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.259-270
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• 2022

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1022-1037
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• 2022

It is important to develop a transducer that generates uniform output power through frequency control of the HIFU at 4 MHz frequency for the high intensity focused ultrasound (HIFU) skin diseases treatment. In this paper, a 4 MHz frequency band HIFU system for skin disease treatment was designed, manufactured and developed. In HIFU, even for the ultrasonic vibrator in the 4 MHz frequency band, the characteristics of the output power of the HIFU are different depending on the difference in the thickness of the PZT material. Through the development of a system amplifier, the sound output of the HIFU transducer was improved to more than 48 W and uniform output power control was possible. And, it is possible to control the output power even in a frequency band of 4.0 to 4.7 MHz, which is wider than 4.0 MHz, and shows the resonance frequency of the transducer. The maximum output power for each frequency was 49.969 W and the minimum value was 48.018 W. The maximum output power compared to the minimum output power is 49.969 W, which is uniform within 4.1%. It was confirmed that the output power of the HIFU through the amplifier can be uniformly controlled in the 4 MHz frequency band.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.4
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• pp.355-364
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• 2010

Filiform hairs that respond to movements of the surrounding medium are the mechanoreceptors commonly found in arthropods and vertebrates. In these creatures, the filiform hairs function as a sensory system for raider detection. Parametric analyses of the motion response of filiform hairs are conducted by using a mathematical model of an artificial flow sensor to understand the possible operating ranges of a microfabricated device. It is found that the length and diameter of the sensory hair are the major parameters that determine the mechanical sensitivities and responses in a mean flow with an oscillating component. By changing the hair length, the angular displacement, velocity, and acceleration could be detected in a wide range of frequencies. Although the torques due to drag and virtual mass are very small, they are also very influential factors on the hair motion. The resonance frequency of the hair decreases as the length and diameter of the hair increase.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• ETRI Journal
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• v.30 no.6
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• pp.844-849
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• 2008

A new transcutaneous energy transmission (TET) system was developed for transmitting electrical power to an implanted device, such as an artificial heart in a patient's body. This new design can maintain a stable output voltage independent of the load resistance. The system includes a compensation capacitor to reduce energy loss and increase power transfer efficiency. Experimental results show that the output voltage of the receiving coil changes very little as the load resistance varies from 14.8 ${\Omega}$ to 15 $k{\Omega}$, which corresponds to a change in output power from 0.1 to 97 W.