医学
霍恩斯菲尔德秤
核医学
成像体模
图像质量
图像噪声
工件(错误)
光子计数
感兴趣区域
放射科
探测器
光学
计算机断层摄影术
物理
人工智能
计算机科学
图像(数学)
作者
Greta Thater,Inéz Frerichs,Sylvia Büttner,Stefan O. Schoenberg,Matthias F. Froelich,Isabelle Ayx
标识
DOI:10.1097/rti.0000000000000822
摘要
Purpose: Computed tomography (CT) is crucial in oncologic imaging for precise diagnosis and staging. Beam-hardening artifacts from contrast media in the superior vena cava can degrade image quality and obscure adjacent structures, complicating lymph node assessment. This study examines the use of virtual monoenergetic reconstruction with photon-counting detector CT (photon-counting CT) to mitigate these artifacts. Materials and Methods: The retrospective study included 50 patients who underwent thoracoabdominal scans. Virtual monoenergetic reconstructions at nine keV levels (60 to 140 keV) were analyzed for Hounsfield Unit (HU) stability, image noise, and artifact index in various regions of interest (ROIs): mediastinal adipose tissue (ROI 1 to 3) and vascular stations (ROI 4 to 6) were compared with reference tissue (ROI 7 to 8). The diagnostic image quality of the keV levels was assessed using a 5-point Likert Scale. Results: Lower keV values (60 to 80) exhibited higher image noise and lower HU stability in mediastinal adipose tissue compared with higher energies, with optimal noise reduction observed at 130 keV (ROI 1 to 3). HU stability in vascular structures (ROI 4 to 6) significantly improved above 80 keV, with the best performance at 140 keV. Artifact levels decreased progressively from 60 to 140 keV. Visually, keV levels of 110 keV (96% Likert ≥4) and 120 keV (60% Likert 4) were rated most diagnostically valuable, consistent with technical findings. Conclusion: Virtual monoenergetic reconstructions with photon-counting CT effectively reduce beam-hardening artifacts near the superior vena cava, enhancing the visualization of lymph nodes and adjacent structures. This technology advances oncologic imaging by improving diagnostic accuracy in areas previously affected by artifact-related image degradation.
科研通智能强力驱动
Strongly Powered by AbleSci AI