Department of Radiology, Qingdao University Affiliated Women and Children's Hospital, 6 Tongfu Road, Qingdao, Shandong, 266034, China.
Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China.
BMC Med Imaging. 2022 Jun 3;22(1):106. doi: 10.1186/s12880-022-00834-1.
To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children.
Low-dose CT scans of the paranasal sinuses in 25 pediatric patients were retrospectively evaluated. The raw data were reconstructed with three levels of DLIR (high, H; medium, M; and low, L), filtered back projection (FBP), and ASiR-V (30% and 50%). Image noise was measured in both soft tissue and bone windows, and the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were calculated. Subjective image quality at the ethmoid sinus and nasal cavity levels of the six groups of reconstructed images was assessed by two doctors using a five-point Likert scale in a double-blind manner.
The patients' mean dose-length product and effective dose were 36.65 ± 2.44 mGy·cm and 0.17 ± 0.03 mSv, respectively. (1) Objective evaluation: 1. Soft tissue window: The difference among groups in each parameter was significant (P < 0.05). Pairwise comparisons showed that the H group' s parameters were significantly better (P < 0.05) than those of the 50% post-ASiR-V group. 2. Bone window: No significant between-group differences were found in the noise of the petrous portion of the temporal bone or its SNR or in the noise of the pterygoid processes of the sphenoids or their SNRs (P > 0.05). Significant differences were observed in the background noise and CNR (P < 0.05). As the DLIR intensity increased, image noise decreased and the CNR improved. The H group exhibited the best image quality. (2) Subjective evaluation: Scores for images of the ethmoid sinuses were not significantly different among groups (P > 0.05). Scores for images of the nasal cavity were significantly different among groups (P < 0.05) and were ranked in descending order as follows: H, M, L, 50% post-ASiR-V, 30% post-ASiR-V, and FBP.
DLIR was superior to FBP and post-ASiR-V in low-dose CT scans of pediatric paranasal sinuses. At high intensity (H), DLIR provided the best reconstruction effects.
比较深度学习图像重建(DLIR)和自适应统计迭代重建 V(ASiR-V)在儿童鼻窦低剂量 CT 中的图像质量的影响。
回顾性分析 25 例儿童鼻窦低剂量 CT 扫描。原始数据采用高(H)、中(M)、低(L)三个层次的 DLIR、滤波反投影(FBP)和 ASiR-V(30%和 50%)进行重建。在软组织窗和骨窗中测量图像噪声,并计算图像的信噪比(SNR)和对比噪声比(CNR)。由两位医生采用五点 Likert 量表对 6 组重建图像的筛窦和鼻腔水平的主观图像质量进行双盲评估。
患者的平均剂量长度乘积和有效剂量分别为 36.65±2.44 mGy·cm 和 0.17±0.03 mSv。(1)客观评价:1. 软组织窗:各参数组间差异有统计学意义(P<0.05)。两两比较显示,H 组参数明显优于 50% post-ASiR-V 组(P<0.05)。2. 骨窗:颞骨岩部的背景噪声及其 SNR,蝶骨翼突的噪声及其 SNR 无组间差异(P>0.05)。背景噪声和 CNR 有显著差异(P<0.05)。随着 DLIR 强度的增加,图像噪声降低,CNR 提高。H 组图像质量最佳。(2)主观评价:筛窦图像评分组间差异无统计学意义(P>0.05)。鼻腔图像评分组间差异有统计学意义(P<0.05),评分依次降低为 H、M、L、50% post-ASiR-V、30% post-ASiR-V 和 FBP。
DLIR 优于 FBP 和 post-ASiR-V 在儿童鼻窦低剂量 CT 扫描。在高强度(H)下,DLIR 提供了最佳的重建效果。