https://scholar.dgist.ac.kr/handle/20.500.11750/11758 2026-04-28T23:23:30Z https://scholar.dgist.ac.kr/handle/20.500.11750/60243 Title: 포톤카운팅 CT 기반의 물질 분리에서의 이웃간 우도를 활용한 노이즈 제거 장치 및 그 방법 Author(s): 이옥균 Abstract: 본 발명은 포톤카운팅 CT 기반의 물질 분리에서의 이웃간 우도를 활용한 노이즈 제거 장치 및 그 방법에 관한 것이다. 본 발명에 따르면, 노이즈 제거 장치는 최대 우도(Maximum Likelihood)를 기반으로 물질 분리가 완료된 물질별 사이노그램 영상을 획득하는 영상 획득부, 상기 물질별 사이노그램 영상에 포함된 복수의 픽셀 중에서 노이즈 제거를 하고자 하는 특정 픽셀을 선택하고, 광자 계수 검출기(PCD)로부터 상기 특정 픽셀에 대응되는 기준 측정값을 획득하는 측정값 획득부, 상기 특정 픽셀을 중심으로 인접하는 제1 픽셀 및 제2 픽셀을 추출하고, 상기 획득한 기준 측정값을 이용하여 제1 픽셀 및 제2 픽셀에 대응되는 각각의 우도를 계산한 다음, 계산된 우도를 이용하여 특정 픽셀, 제1 픽셀 및 제2 픽셀에 각각의 가중치를 부여하는 분석부, 상기 산출된 가중치를 우도 기반의 양방향 필터(Likelihood-based Bilateral Filter, LBF)에 적용하여 노이즈를 제거하는 필터링부, 그리고 노이즈가 제거된 물질별 사이노그램 영상을 획득하고, 획득한 물질별 사이노그램 영상에 역투영(Filtered Backprojection, FBP) 알고리즘을 적용하여 최종 물질별 영상을 획득하는 제어부를 포함한다. https://scholar.dgist.ac.kr/handle/20.500.11750/59938 Title: Single-Exposure Material Decomposition in Chest Tomosynthesis with a CdTe Photon-Counting Detector Author(s): Lee, Soohyun; Lim, Younghwan; Park, Sungmin; Lee, Okkyun; Cha, Bo Kyung; Cho, Hyosung Abstract: Dual-energy material decomposition enables differentiation of soft tissue and bone in X-ray imaging; however, conventional methods require two sequential exposures, increasing radiation dose, acquisition time, and the risk of misregistration. This study presents a single-exposure material decomposition method using a cadmium telluride (CdTe)-based photon-counting detector (PCD) integrated with digital tomosynthesis (DTS). The proposed framework consists of three key steps: 1) simultaneous acquisition of low- and high-energy projections with dual thresholds (25 and 65 keV), 2) calibration-based decomposition using a PMMA–Al wedge phantom, and 3) projection-domain material separation followed by DTS reconstruction. Validation through both simulation and experimental studies demonstrated accurate separation of soft-tissue and bone components, high projection-level decomposition precision (RMSE: 0.052 for PMMA; 0.012 for Al), and improved perceptual image quality (SSIM: 0.979 and 0.974; PSNR: 37.6 and 38.6 dB). Compared with conventional energy-integrating detector (EID)-based dual-energy methods, the proposed PCD-based approach achieved superior structural preservation, contrast uniformity, and image interpretability. Beyond chest imaging, this single-exposure PCD framework is also applicable to non-medical X-ray applications, such as industrial nondestructive testing, security screening, and material characterization. https://scholar.dgist.ac.kr/handle/20.500.11750/59239 Title: Material decomposition-based improved normalized metal artifact reduction method (MD-NMAR) in photon counting CT Author(s): Nam, Jeonghyeon; Kim, Joonbeom; Ye, Dong Hye; Lee, Okkyun Abstract: Photon counting computed tomography (PCCT) acquires multiple images of different energy ranges from a single computed tomography (CT) scan. It provides us with spatial and spectral information not available from conventional CT, making it possible for further analysis in the metal artifacts reduction (MAR). This study aims to develop a method to reduce metal artifacts in PCCT using material decomposition. We use the normalized MAR (NMAR) method and calibration data to obtain the initial basis material images. We correct the image of soft tissue using the NMAR and then correct the image of hard tissue by performing least squares fitting with virtual monochromatic images (VMIs). The artifact-reduced material images are reverted to the bin-wise images (CT images for each energy bin) and then employed as the improved prior images for the NMAR method to obtain the final MAR results: The metal artifact-reduced bin-wise CT images. In simulation results, the proposed method showed promising results, reducing metal artifacts compared to the original NMAR method applied to bin-wise images. For example, it reduced the root mean squared error values by an average of 6.3% for a dual-energy case. The proposed method also reproduced noticeable improvements in the table-top PCCT experiments compared to the conventional NMAR. 2025-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58248 Title: Noise-matched total-likelihood-based bilateral filter: Experimental feasibility in a benchtop photon-counting CBCT system Author(s): Lee, Okkyun; Kim, Joonbeom Abstract: Purpose : Material decomposition induces substantial noise in basis images and their synthesized computed tomography (CT) images. A likelihood-based bilateral filter was previously developed as a neighborhood filter that effectively reduces noise. However, this method is sensitive to image contrast, and the noise texture needs improvement. It is also necessary to address how to optimally combine filtered basis images to synthesize CT images. This study addressed these issues by introducing total likelihood and a noise-matched condition. Methods: The experimental feasibility of the proposed method was demonstrated in a benchtop photon- counting CT (PCCT) system using the following steps: (1) A calibration process for forward modeling, (2) maximum likelihood (ML)-based material decomposition, which is accurate but suffers from substantial noise, (3) noise reduction by applying a total-likelihood-based filter, and (4) CT image synthesis using the noise- matched condition. The proposed method was compared with conventional neighborhood filters and statistical iterative reconstruction with edge-preserving regularization. Results: The local noise and task-based modulation transfer function (TTF) were analyzed using a test phantom, and the proposed method was found to preserve the spatial resolution better than the other methods, especially in low-contrast regions. In the chicken leg experiment, the proposed method improved the fine structures and background textures in the denoised images and exhibited superior properties in analyzing the noise power spectrum. Conclusion: The proposed method is effective and computationally efficient for noise reduction in PCCT and can potentially replace conventional iterative edge-preserved regularization approaches. 2025-01-31T15:00:00Z