International Journal
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SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Accepted)
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Deep Learning Referral Suggestion and Tumour Discrimination using Explainable Artificial Intelligence applied to Multiparametric MRI, European Radiology, 2023 (Accepted)
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Intelligent Noninvasive Meningioma Grading with a Fully Automatic Segmentation using Interpretable Multiparametric Deep Learning, European Radiology, 2023 (Accepted)
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Digestive Organ Recognition in Video Capsule Endoscopy based on Temporal Segmentation Network, MICCAI, 2022
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CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation, Medical Image Analysis, 2022
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Ultra-thin crystaline silicon-based strain gauges with deep learning algorithms for silent speech interfaces, Nature Communications, 2022
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Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering, Diagnostics, 2022
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Fully Automatic Quantification of Transient Severe Respiratory Motion Artifact of Gadoxetate Disodium Enhanced MRI during Arterial Phase, Medical Physics, 2022
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M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
[Journal Link]
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Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. European Radiology, 2022.
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[BlochGAN]
Fat-saturated Image Generation from Multi-contrast MRIs Using Generative Adversarial Networks with Bloch Equation-based Autoencoder Regularization. Medical Image Analysis, Volume 73, October 2021, 102198
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Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. -with Facebook AI & NYU. IEEE Transactions on Medical Imaging, (In press)
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Lee, J., Kim, S., Park, I., Eo, T., Hwang, D. (2021). Relevance-CAM: Your Model Already Knows Where to Look. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14944-14953
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Quantitative analysis of the mouth opening movement of temporomandibular joint disorder patients according to disc position using computer vision: a pilot study. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY , 2021.
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Jun, Y., Shin, H., Eo, T., Hwang, D. (2021). Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5270-5279
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Jun, Y., Shin, H., Eo, T., Kim, T., Hwang, D. (2021). Deep Model-based Magnetic Resonance Parameter Mapping Network (DOPAMINE) for Fast T1 Mapping Using Variable Flip Angle Method. Medical Image Analysis,
70, 102017
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Park, Y.*, Jun, Y.*, Lee, Y. , Han, K., An, C., Ahn, S.**, Hwang, D.**, Lee, S.(2021). Robust Performance of Deep Learning for Automatic Detection and Segmentation of Brain Metastases Using Three-dimensional Black-Blood and Three-dimensional Gradient Echo Imaging. European Radiology, ( In press )
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Shin, H., Lee, J., Eo, T., Jun, Y., Kim, S., Hwang, D. (2020). The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging. Journal of the Korean Society of Radiology, 81(6), 1305-1333.
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Eom, J., Park, I., Kim, S., Jang, H., Hwang, D. (2021). Deep-learned Spike Representations and Sorting via an Ensemble of Auto-encoders. Neural Networks, 134, 131-142.
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Kim, S., Jang, H., Jang, J., Lee, Y., Hwang, D. (2020). Deep-learned Short Tau
Inversion Recovery Imaging Using Multi Contrast Magnetic Resonance Images. Magnetic Resonance in Medicine, 84(6), 2994-3008.
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Eo, T., Shin, H., Jun, Y., Kim, T., Hwang, D. (2020). Accelerating Cartesian MRI by Domain-Transform Manifold Learning in Phase-Encoding Direction. Medical Image Analysis, 63, 101689.
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Jang, H., Bang, K., Jang, J., Hwang, D. (2020). Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation. IEEE Access, 8, 38554-38567.
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Park, I., Eom, J., Jang, H., Kim, S., Park, S., Huh, Y., Hwang, D. (2019). Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings. Applied Sciences, 10(1), 301.
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Hwang, D., Kim, D. (2019). Special Features on Intelligent Imaging and Analysis. Applied Sciences, 9(22), 4804.
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Kim, T., Kim, G., Kim, H., Yoon, H., Kim, T., Jun, Y., Shin, T., Kang, S., Cheon, J., Hwang, D., Min, B., Shim, W. (2019). Megahertz-wave-transmitting conducting polymer electrode for device-to-device integration. Nature Communications, 10(1), 653.
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Jun, Y., Eo, T., Shin, H., Kim, T., Lee, H., Hwang, D. (2019). Parallel Imaging in Time-of-Flight Magnetic Resonance Angiography Using Deep Multi-Stream Convolutional Neural Networks. Magnetic Resonance in Medicine, 81(6), 3840-3853.
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Lee, Y., Kim, S., Suh, J., Hwang, D. (2018). Learning Radiologist’s Step-by-Step Skill for Cervical Spinal Injury Examination: Line drawing, Prevertebral Soft Tissue Thickness Measurement, and Detection of the Swelling in Radiographs. IEEE Access, 6, 55492-55500.
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Jang, H., Bang, K., Jang, J., Hwang, D. (2018). Inverse Tone Mapping Operator Using Sequential Deep Neural Networks Based on Human Visual System. IEEE Access, 6, 52058-52072.
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Kim, S., Bae, W., Masuda, K., Chung, C., and Hwang, D. (2018). Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net. Applied Sciences, 8(9), 1656.
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Jang, J., Jang, H., Eo, T., Bang, K., and Hwang, D. (2018). No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features. Applied Sciences, 8(9), 1688.
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Kim, S., Bae, W., Masuda, K., Chung, C., Hwang, D. (2018). Semi-Automatic Segmentation of Vertebral Bodies in MR images of Human Lumbar Spines. Applied Sciences. 8(9), 1586.
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Oh, D., Kim, S., Park, D., Choi, S., Song, H., Choi, Y., Moon, S., Baek, J., Hwang, D. (2018). Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based parameter selection method. Medical Physics, 45(9), 4133-4144.
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Jun, Y., Eo, T., Kim, T., Shin, H., Hwang, D.*, Bae, S., Park, Y., Lee, H., Choi, B., Ahn, S. (2018). Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Scientific Reports, 8: 9450.
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Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H., & Hwang, D. (2018). KIKI-net: Cross-Domain Convolutional Neural Networks for Reconstructing Undersampled Magnetic Resonance Images. Magnetic Resonance in Medicine, 80(5), 2188-2201.
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Jang, J., Bang, K., Jang, H., & Hwang, D. (2018). Quality Evaluation of No-reference MR Images Using Multidirectional Filters and Image Statistics. Magnetic Resonance in Medicine, 80(3), 914-924.
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Lee, Y., & Hwang, D. (2018). Periodicity-based nonlocal-means denoising method for electrocardiography in low SNR non-white noisy conditions. Biomedical Signal Processing and Control, 39, 284-293.
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Kim, Y., Oh, D., & Hwang, D. (2017). Small-scale noise-like moiré pattern caused by detector sensitivity inhomogeneity in computed tomography. Optics Express, 25(22), 27127-27145.
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Song, K. I., Chu, J. U., Park, S. E., Hwang, D., & Youn, I. (2017). Ankle-Angle Estimation from Blind Source Separated Afferent Activity in the Sciatic Nerve for Closed-Loop Functional Neuromuscular Stimulation System. IEEE Transactions on Biomedical Engineering, 64(4), 834-843.
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Eo, T., Kim, T., Jun, Y., Lee, H., Ahn, S. S., Kim, D. H., & Hwang, D. (2017). High‐SNR multiple T2 (*)‐contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics. Journal of Magnetic Resonance Imaging, 45(6), 1835-1845.
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Hwang, D., Kim, S., Abeydeera, N. A., Statum, S., Masuda, K., Chung, C. B., ... & Bae, W. C. (2016). Quantitative magnetic resonance imaging of the lumbar intervertebral discs. Quantitative Imaging in Medicine and Surgery, 6(6), 744-755.
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Park, H. S., Hwang, D., & Seo, J. K. (2016). Metal artifact reduction for polychromatic x-ray CT based on a beam-hardening corrector. IEEE transactions on medical imaging, 35(2), 480-487.
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Kim, S., & Hwang, D. (2015). Murmur-adaptive compression technique for phonocardiogram signals. Electronics Letters, 52(3), 183-184.
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Nam, Y., Lee, J., Hwang, D., & Kim, D. H. (2015). Improved estimation of myelin water fraction using complex model fitting. NeuroImage, 116, 214-221.
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Park, S. E., Song, K. I., Suh, J. K. F., Hwang, D., & Youn, I. (2015). A time-course study of behavioral and electrophysiological characteristics in a mouse model of different stages of Parkinson's disease using 6-hydroxydopamine. Behavioural brain research, 284, 153-157.
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Gho, S. M., Liu, C., Li, W., Jang, U., Kim, E. Y., Hwang, D., & Kim, D. H. (2014). Susceptibility map‐weighted imaging (SMWI) for neuroimaging. Magnetic resonance in medicine, 72(2), 337-346.
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Kim, Y., Baek, J., & Hwang, D. (2014). Ring artifact correction using detector line-ratios in computed tomography. Optics express, 22(11), 13380-13392.
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Hwang, D., & Zeng, G. L. (2014). Special issue on medical imaging. Biomedical Engineering Letters, 4(1), 1-2.
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Chu, J. U., Song, K. I., Shon, A., Han, S., Lee, S. H., Kang, J. Y., ... & Youn, I. (2013). Feedback control of electrode offset voltage during functional electrical stimulation. Journal of neuroscience methods, 218(1), 55-71.
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Kwon, O. I., Woo, E. J., Du, Y. P., & Hwang, D. (2013). A tissue-relaxation-dependent neighboring method for robust mapping of the myelin water fraction. NeuroImage, 74, 12-21.
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Chu, J. U., Song, K. I., Han, S., Lee, S. H., Kang, J. Y., Hwang, D., ... & Youn, I. (2013). Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction. Physiological measurement, 34(5), 541.
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Jang, U., Nam, Y., Kim, D. H., & Hwang, D. (2013). Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising. Neuroimage, 70, 308-316.
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Kang, B., Choi, O., Kim, J. D., & Hwang, D. (2013). Noise reduction in magnetic resonance images using adaptive non-local means filtering. Electronics Letters, 49(5), 324-326.
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Chu, J. U., Song, K. I., Han, S., Lee, S. H., Kim, J., Kang, J. Y., ... & Youn, I. (2012). Improvement of signal-to-interference ratio and signal-to-noise ratio in nerve cuff electrode systems. Physiological measurement, 33(6), 943.
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Lee, Y. J., Lee, P. J., Kim, K. S., Park, W., Kim, K. D., Hwang, D., & Lee, J. W. (2012). Toothbrushing region detection using three-axis accelerometer and magnetic sensor. IEEE Transactions on Biomedical Engineering, 59(3), 872-881.
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Jang, U., & Hwang, D. (2012). High‐quality multiple T2 (*) contrast MR images from low‐quality multi‐echo images using temporal‐domain denoising methods. Medical physics, 39(1), 468-474.
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Hwang, D., Lee, J. W., & Zeng, G. L. (2011). SPECT reconstruction with sub‐sinogram acquisitions. International Journal of Imaging Systems and Technology, 21(3), 247-252.
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Hwang, D., Chung, H., Nam, Y., Du, Y. P., & Jang, U. (2011). Robust mapping of the myelin water fraction in the presence of noise: synergic combination of anisotropic diffusion filter and spatially regularized nonnegative least squares algorithm. Journal of Magnetic Resonance Imaging, 34(1), 189-195.
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Hwang, D., Kim, D. H., & Du, Y. P. (2010). In vivo multi-slice mapping of myelin water content using T2* decay. NeuroImage, 52(1), 198-204.
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Hwang, D., & Du, Y. P. (2009). Improved myelin water quantification using spatially regularized non‐negative least squares algorithm. Journal of Magnetic Resonance Imaging, 30(1), 203-208.
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Du, Y. P., Chu, R., Hwang, D., Brown, M. S., Kleinschmidt‐DeMasters, B. K., Singel, D., & Simon, J. H. (2007). Fast multislice mapping of the myelin water fraction using multicompartment analysis of T decay at 3T: A preliminary postmortem study. Magnetic Resonance in Medicine, 58(5), 865-870.
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Nagarajan, S. S., Portniaguine, O., Hwang, D., Johnson, C., & Sekihara, K. (2006). Controlled support MEG imaging. NeuroImage, 33(3), 878-885.
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Hwang, D., & Zeng, G. L. (2005). Convergence study of an accelerated ML-EM algorithm using bigger step size. Physics in medicine and biology, 51(2), 237.
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Hwang, D., & Zeng, G. L. (2005). Reduction of noise amplification in SPECT using smaller detector bin size. IEEE transactions on nuclear science, 52(5), 1417-1427.
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Hwang, D., & Zeng, G. L. (2005). A new simple iterative reconstruction algorithm for SPECT transmission measurement. Medical physics, 32(7), 2312-2319.
U.S. Patents
11. US 10,168,405 B2 (2019/01/01), METHOD AND APPARATUS FOR QUANTIFYING PROPERTIES OF AN OBJECT THROUGH MAGNETIC
RESONANCE IMAGING (MRI)
10. US 9,989,609 B2 (2018/06/05), METHOD AND APPARATUS FOR ADJUSTING THE PARAMETERS OF A MAGNETIC RESONANCE IMAGE
9. US 9,977,109 B2 (2018/05/22), MAGNETIC RESONANCE IMAGING APPARATUS AND OPERATING METHOD FOR THE SAME
8. US 9,928,576 B2 (2018/03/27), DENOISING METHOD AND APPARATUS FOR MULTI-CONTRAST MRI.
7. US 9,759,796 B2 (2017/09/12), METHOD AND APPARATUS FOR ELIMINATING NOISE IN MAGNETIC RESONANCE IMAGES.
6. [Germany] Patent Granted: METHOD AND APPARATUS FOR ELIMINATING NOISE IN MAGNETIC RESONANCE IMAGES.
5. [France] Patent Granted: METHOD AND APPARATUS FOR ELIMINATING NOISE IN MAGNETIC RESONANCE IMAGES.
4. [UK] Patent Granted: METHOD AND APPARATUS FOR ELIMINATING NOISE IN MAGNETIC RESONANCE IMAGES.
3. US 9,506,896 B2 (2016/11/29), METHOD AND APPARATUS FOR DETECTING AN ENVELOPE FOR ULTRASONIC SIGNALS.
2. US 9,241,671 B2 (2016/01/26), APPARATUS AND METHOD FOR REMOVING NOISE FROM BIOSIGNALS.
1. US 9,119,546 B2 (2015/09/01), R-PEAK DETECTION APPARATUS AND CONTROL METHOD THEREOF.
Domestic Patents
49. 컴퓨터 단층촬영 영상 재구성 장치 및 방법
48. 자기 공명 영상 장치 및 자기 공명 영상 장치의 재촬영 제어 방법
47. 심전도 신호를 이용한 사용자 인증 방법 및 장치
46. 자기 공명 영상 장치 및 자기 공명 영상 장치의 재촬영 제어 방법
45. 뇌종양 판별 영상 생성 학습 장치 및 방법과 뇌종양 판별 영상 생성 장치 및 방법 그리고 이에 관한 기록 매체
44. 의료 영상에 기반하여 상태를 진단하는 장치 및 방법
43. 언더샘플링된 자기 공명 영상의 재구성 장치 및 방법
42. 화질 평가 장치 및 방법과 이에 관한 기록매체
41. 합성곱 신경망 딥 러닝 기반 경추 연조직의 측정 방법
40. 후처리 기반 자기공명영상의 파라미터 조정 방법 및 장치
39. 컴퓨터 단층 촬영 영상의 아티팩트를 보정하는 방법 및 장치
38. 단일 엑스레이를 이용한 병변 위치측정 기술
37. 움직임 추적을 위한 관심영역 설정 장치 및 방법과 그 기록매체
36. 자기 공명 영상 장치에서 위상 펼침 방법 및 그 방법을 사용하는 자기 공명 영상 장치
35. 자기 공명 영상을 통해 대상체의 속성을 정량화하기 위한 방법 및 장치
34. 복소수 신호 모델링을 이용한 다중 자기공명영상에서의 위상 복원 방법
33. 움직임 추적을 위한 ROI 설정 방법
32. 자기공명영상의 파라미터 조정 방법 및 장치
31. 자기 공명 영상 장치 및 그 동작방법
30. 초음파 신호의 포락선 검출 방법 및 그 장치
29. 다중 대조도 자기공명영상에서 잡음 제거 방법 및 장치
28. 초음파 신호의 포락선 검출 방법 및 그 장치
27. 종양 추적 방법 및 그 장치
26. 자기 공명 영상 장치 및 그 동작방법
25. 후처리 기반 자기공명영상의 파라미터 조정 방법 및 장치
24. 자기공명영상의 파라미터 조정 방법 및 장치
23. 자기공명영상 잡음 제거 방법 및 장치
22. 종양특성에 기반한 종양 추적 방법 및 그 장치
21. 자기공명영상 잡음 제거 방법 및 장치
20. 다중 대조도 자기공명영상에서 잡음 제거 방법 및 장치
19. 초음파 신호의 포락선 검출 방법 및 그 장치
18. 자기 공명 영상 장치 및 그 동작방법
17. R 피크 검출을 위한 심전도 장치 및 방법
16. 듀얼 촬영 방식 컴퓨터 단층 촬영장치 및 이를 이용한 컴퓨터 단층 촬영방법
15. 듀얼 프로브가 구비된 초음파 촬영장치 및 이를 이용한 초음파 촬영방법
14. ECG 신호를 이용한 환자의 청진위치정보 획득 장치 및 이를 이용한 환자의 청진위치정보 획득 방법
13. 생체 신호의 노이즈 제거 장치 및 방법
12. 자기공명영상 잡음 제거 방법 및 장치
11. R - 피크 검출 장치 및 그 제어 방법
10. 생체신호를 이용한 초음파 진단 시스템 및 진단 방법
09. ECG 신호 검출 시스템 및 방법
08. 보정용 팬텀을 이용한 CT 영상 획득장치 및 획득방법
07. 심전도 신호를 이용한 사용자 인증 방법 및 장치
06. 생체 신호의 노이즈 제거 장치 및 방법
05. R - 피크 검출 장치 및 그 제어 방법
04. 의료용 융합영상 획득장치 및 획득방법
03. 실시간 단층 영상 생성장치, 생성방법 및 실시간 단층 영상을 이용한 의료장치
02. 단층 영상 생성 장치 및 방법
01. 의료용 융합영상 획득장치 및 획득방법