https://scholar.dgist.ac.kr/handle/20.500.11750/11759 2026-04-08T23:43:23Z 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 https://scholar.dgist.ac.kr/handle/20.500.11750/57428 Title: UNet-based multi-organ segmentation in photon counting CT using virtual monoenergetic images Author(s): Baek, Sumin; Ye, Dong Hye; Lee, Okkyun Abstract: Background: Multi-organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector-based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation performance. Purpose: We propose UNet-based multi-organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information effectively. Methods: The proposed method consists of the following steps: Noise reduction in bin-wise images, image-based material decomposition, generating VMIs, and deep learning-based segmentation. VMIs are synthesized for various x-ray energies using basis images. The UNet-based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual- and quad-energy bins. We compared it to the conventional PCCT-based segmentation, which uses only the (noise-reduced) bin-wise images. The experiments were conducted on two cases by adjusting the dose levels. Results: The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin-wise images from dual-energy bins; 3D UNet). Conclusions: The proposed method using VMIs improves training stability for multi-organ segmentation in PCCT, particularly when the number of energy bins is small. © 2024 American Association of Physicists in Medicine. 2024-12-31T15:00:00Z