Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Clin Radiol. 2021 Feb;76(2):155.e15-155.e23. doi: 10.1016/j.crad.2020.10.011. Epub 2020 Nov 19.
To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).
Thirty-six patients were evaluated retrospectively. All patients underwent contrast-enhanced chest CT and thin-section images were reconstructed using filtered back projection (FBP); ASiR-V (60% and 100% blending setting); and DLIR (low, medium, and high settings). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated objectively. Two independent radiologists evaluated ASiR-V 60% and DLIR subjectively, in comparison with FBP, on a five-point scale in terms of noise, streak artefact, lymph nodes, small vessels, and overall image quality on a mediastinal window setting (width 400 HU, level 60 HU). In addition, image texture of ASiR-Vs (60% and 100%) and DLIR-high was analysed subjectively.
Compared with ASiR-V 60%, DLIR-med and DLIR-high showed significantly less noise, higher SNR, and higher CNR (p<0.0001). DLIR-high and ASiR-V 100% were not significantly different regarding noise (p=0.2918) and CNR (p=0.0642). At a higher DLIR setting, noise was lower and SNR and CNR were higher (p<0.0001). DLIR-high showed the best subjective scores for noise, streak artefact, and overall image quality (p<0.0001). Compared with ASiR-V 60%, DLIR-med and DLIR-high scored worse in the assessment of small vessels (p<0.0001). The image texture of DLIR-high was significantly finer than that of ASIR-Vs (p<0.0001).
DLIR-high improved the objective parameters and subjective image quality by reducing noise and streak artefacts and providing finer image texture.
评估纵隔窗设置下深度学习图像重建（DLIR）与自适应统计迭代重建（ASiR-V）在胸部 CT 图像中的图像质量。
回顾性评估 36 例患者。所有患者均行增强胸部 CT 检查，采用滤波反投影（FBP）、ASiR-V（60%和 100%混合设置）和 DLIR（低、中、高设置）重建薄层图像。客观评估图像噪声、信噪比（SNR）和对比噪声比（CNR）。两位独立的放射科医生在纵隔窗设置（400HU 宽，60HU 级）下，以 5 分制评估 ASiR-V 60%和 DLIR 的噪声、条纹伪影、淋巴结、小血管和整体图像质量，并与 FBP 进行比较。此外，还对 ASiR-V（60%和 100%）和 DLIR-高的图像纹理进行主观分析。
与 ASiR-V 60%相比，DLIR-中、DLIR-高的噪声明显降低，SNR 和 CNR 更高（p<0.0001）。DLIR-高和 ASiR-V 100%的噪声（p=0.2918）和 CNR（p=0.0642）无显著差异。在更高的 DLIR 设置下，噪声更低，SNR 和 CNR 更高（p<0.0001）。DLIR-高在噪声、条纹伪影和整体图像质量方面的主观评分最好（p<0.0001）。与 ASiR-V 60%相比，DLIR-中、DLIR-高对小血管的评估较差（p<0.0001）。DLIR-高的图像纹理明显优于 ASiR-Vs（p<0.0001）。
DLIR-高通过降低噪声和条纹伪影，提供更精细的图像纹理，改善了客观参数和主观图像质量。
