arXiv subjects

Linxi Shi

Publications and source records attributed to Linxi Shi.

4 recordsLinked to original sources

arXiv2026

Simulation analysis of a direct/indirect conversion dual-layer X-ray detector for early identification of coronary artery calcium

Coronary artery calcification (CAC) is a strong predictor of future cardiovascular events, and CAC burden is commonly used to stratify patient cardiovascular risk. Although CAC is typically quantified using computed tomography, routine chest radiography offers an opportunity for lower-dose, widely available opportunistic screening. Single-exposure dual-energy chest radiography using multilayer flat-panel detectors can generate low- and high-energy images without motion artifacts and provide material decomposition parameters, including calcification density. In this work, we investigate a direct/indirect dual-layer detector consisting of an amorphous selenium (a-Se) direct-conversion top layer and a cesium iodide scintillator-based indirect-conversion bottom layer for enhanced visualization of small CAC. A cascaded linear systems model (CLSM) simulated task-based detectability for a 1 mm calcification embedded in soft tissue. A complementary image-domain contrast-to-noise ratio (CNR) analysis using CT-derived patient anatomy with inserted 1 mm calcifications evaluated CNR as a function of a-Se thickness and dual-energy weighting factor. The CLSM analysis revealed peak task-based detectability at an a-Se thickness of 500-550 um and a tissue-cancellation weighting factor of 1.08. Detectability remained within a few percent of this maximum across a broad range of a-Se thicknesses (300-1000 um). The image-domain CNR analysis yielded maximum CNR at 300 um a-Se and weighting factor of 0.93, with a smaller range of 200-500 um giving acceptable CNR. These results indicate that near-equal weighting of the two detector layers improves anatomical-noise suppression and CAC conspicuity, while a 300 um a-Se top layer provides a practical design compromise between detectability, operating voltage, and manufacturability.

physics.med-ph↗

arXiv2026

Toward opportunistic coronary artery calcium assessment from dual-layer dual-energy chest radiography using deep learning: a simulation study

Background: Coronary artery calcium (CAC) predicts cardiovascular risk. Dual-energy (DE) chest radiography may enable accessible opportunistic CAC assessment by separating calcifications from overlapping bone. Purpose: To evaluate multi-task deep learning for CAC localization and CT-referenced score prediction and assess projection, image representation, and exposure effects. Methods: This retrospective simulation study used 550 calcium-scoring CT exams with reference CAC masks and Agatston scores. Dual-layer DE radiographs were simulated with a physics-based detector model. A modified nnU-Net jointly predicted CAC localization and scores from continuous probability maps. Comparisons included AP, 45°, and lateral views; lateral single-energy (SE), DE bone, and high-pass filtered DE bone images; and dose-matched models at 70, 50, and 24 mAs. Patient-level splitting reserved 10% for independent testing; five-fold models provided averaged inference. Metrics included Dice and AUC at CAC thresholds >0, >100, >400, and >1000, with bootstrap uncertainty and paired DeLong tests with Holm-Bonferroni correction. Results: Lateral views achieved Dice 0.34 versus 0.23 (AP) and 0.22 (45°), with AUCs for CAC >100 of 0.86, 0.81, and 0.77, respectively; AUC differences were not significant after correction. Lateral DE bone outperformed SE (Dice: 0.33 vs. 0.19; AUC for CAC >100: 0.86 vs. 0.64), with significant AUC advantages at >100, >400, and >1000. High-pass filtering added little classification benefit. Dice decreased with exposure, while AUC differences across doses were not significant. Conclusions: Simulation results support lateral dual-layer DE bone imaging for opportunistic CAC assessment, with significant advantages over SE. Physical and clinical validation is needed.

physics.med-ph↗

arXiv2022

Single-Shot Quantitative X-ray Imaging Using a Primary Modulator and Dual-Layer Detector

Purpose: Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free dual-energy (DE) imaging with scatter correction in a single shot. Methods: The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. For validation, we set up SSQI on our tabletop system and imaged acrylic and copper slabs with known thicknesses, estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. Results: The root mean squared error (RMSE) of SSQI estimation was 0.13 cm for acrylic and 0.04 mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them and reduced RMSE in material-specific images by 38% to 92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. Conclusions: We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The simplicity of SSQI may enable its widespread adoption, including radiography and dynamic imaging such as real-time image guidance and cone-beam CT.

physics.med-ph↗

arXiv2022

Patient-specific mean teacher UNet for enhancing PET image and low-dose PET reconstruction on RefleXion X1 biology-guided radiotherapy system

The RefleXion X1 is the first biology-guided radiotherapy (BgRT) system. Its dual 90-degree PET detector collects fewer pair production events compared to a full-ring diagnostic PET system. In the proposed BgRT workflow, a short scan is acquired before treatment delivery to ensure image quality and consistency. The shorter scan time, a quarter of the simulation scan time, also leads to fewer coincidence events and hence reduced image quality. In this study, we proposed a patient-specific mean teacher UNet (MT-UNet) to enhance PET image quality and low-dose PET reconstruction on RefleXion X1. PET/CT scans of nine cancer patients were acquired using RefleXion X1. Every patient had one simulation scan. Five patients had additional scans acquired during the first and the final treatment fractions. Treatment scans were acquired using the same imaging protocol as the simulation scan. For each scan, we reconstructed a full-dose image and evenly split coincidence events into four sessions to reconstruct four quarter-dose PET images. For each patient, our proposed MT-UNet was trained using quarter-dose and full-dose images of the simulation scan. For the image quality enhancement task, we applied nine trained MT-UNets to full-dose simulation PET images of the nine patients to generate enhanced images, respectively. The enhanced images were compared with the original full-dose images using CNR and SNR. For the low-dose image reconstruction task, we applied five trained MT-UNets to ten quarter-dose treatment images of five patients to predict full-dose images, respectively. The predicted and ground truth full-dose images were compared using SSIM and PSNR. We also trained and evaluated patient-specific UNets for model comparison. Our proposed patient-specific MT-UNet achieved better performance in improving the quality of RefleXion low-dose and full-dose images compared to the patient-specific UNet.

physics.med-ph↗