https://scholar.dgist.ac.kr/handle/20.500.11750/11759 Mon, 31 Aug 2026 12:54:27 GMT 2026-08-31T12:54:27Z https://scholar.dgist.ac.kr/handle/20.500.11750/60617 Title: Subspace-based dual quadratic discriminant analysis for motor signal classification using functional near-infrared spectroscopy Author(s): Lee, Seungjun; Eom, Taein; Kim, Kihwan; Lee, Okkyun Abstract: Functional near-infrared spectroscopy (fNIRS) has been used as a non-invasive modality for brain-computer interfaces (BCIs). Feature extraction is crucial for computationally efficient and performance-effective brain signal classification in fNIRS-BCI. For example, mean, peak, slope, and variance in the oxygenated hemoglobin (HbO) signal were typically used. However, there are limits to these features to exploit all the information in the measurements, and optimizing feature extraction in multi-channel signals is challenging. This paper proposes a subspace-based dual quadratic discriminant analysis (SD-QDA) to address these issues. It comprises dual quadratic and linear terms, and we optimized them for maximum discriminability within the training dataset. The difference between orthogonal projections was utilized in the quadratic terms to mitigate the small sample size problem, enabling the effective use of all multi-channel signals. We validated the method using simulation and experimental datasets (right-hand squeezing and imagining) and showed that it improved the classification accuracy compared to conventional methods. We also demonstrated that using the deoxygenated hemoglobin (HbR) signal in conjunction with HbO significantly improved the performance of the proposed method in the experimental datasets, thereby fully exploiting the fNIRS measurements in potential BCI applications. The proposed method exhibited the (maximum) accuracy for the motor signal (actual movement) at 84.5% on average and 74.7% for the motor signal imagination. Mon, 31 Aug 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60617 2026-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60548 Title: Approximate Cramér-Rao lower bound analyses for semi-optimal energy thresholds selection in photon counting CT Author(s): Lee, Okkyun Abstract: Background Selecting energy thresholds in photon counting CT (PCCT) is crucial for reducing noise in material decomposition. Theoretically, it can be determined by analyzing the Cram & eacute;r-Rao lower bound (CRLB). However, the CRLB analysis is challenging in practice without complete knowledge of system models.Purpose This study aims to develop a practical method for selecting semi-optimal energy thresholds in PCCT without the system models.Methods We proposed approximate CRLB approaches, linear and nonlinear. It only needs to generate look-up tables of measured counts using a threshold scan for reference slab phantoms, which roughly model the largest basis line integrals in the object. We also introduced total noise, which summarizes the noise in the basis line integrals and can be calculated from the look-up table; one can select energy thresholds that yield the minimum total noise. The proposed methods were validated using the PcTK toolbox in simulation studies for two- and three-material decomposition for various numbers of energy thresholds. The proposed methods were compared to the theoretically optimal CRLB analysis and the condition of equally distributed counts (EDC). It was also validated using a bench-top PCCT system for two-material decomposition with two energy thresholds.Results In simulation studies, all methods showed similar total noises for two-material decomposition. For the three-material decomposition, the proposed methods showed lower or similar noise levels compared to the EDC condition. All methods showed higher noise than the optimal one, except the proposed nonlinear approach, which exhibited the optimal noise level at four energy bins. In experiments, the proposed linear method achieved a near-optimal performance in a two-material decomposition, while the EDC condition increased noise levels by 15% and 18% for epoxy resin and CaHA, respectively.Conclusions The proposed energy threshold selection methods are practical and effective: they require only a threshold scan of the reference phantoms and are computationally efficient. It outperformed the existing EDC condition in both simulation and experimental studies. It also showed a near-optimal noise level across various conditions, except in the three-material with three-energy-bin case. Tue, 30 Jun 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60548 2026-06-30T15:00:00Z 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. Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59938 2026-04-30T15:00:00Z 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. Sun, 31 Aug 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59239 2025-08-31T15:00:00Z