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Table 1 Subjective evaluation of different reconstruction algorithms

From: Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection

  FBP 50%ASIR-V 100%ASIR-V DL-H F value P value
5 mm images  
 SC 2.47 ± 0.50 3.04 ± 0.19 3.53 ± 0.50* 3.71 ± 0.46* 116.00 < 0.001
 BM 2.25 ± 0.44 3.04 ± 0.19 3.51 ± 0.50* 3.67 ± 0.51* 131.68 < 0.001
 WQ 2.25 ± 0.44 3.05 ± 0.23* 2.87 ± 0.39* 3.64 ± 0.49 129.57 < 0.001
0.625 mm images
 SC 1.51 ± 0.54 2.13 ± 0.55 2.93 ± 0.47* 3.15 ± 0.45* 143.20 < 0.001
 BM 1.05 ± 0.23 1.78 ± 0.42 2.44 ± 0.50 3.02 ± 0.36 146.06 < 0.001
 WQ 1.04 ± 0.19 1.82 ± 0.39 2.42 ± 0.50 3.04 ± 0.33 148.45 < 0.001
  1. SC: Clarity level of sulci/cisterns boundaries; BM: boundaries between the white and gray matters; WQ: whole image quality
  2. *Without significant statistical differences for the numbers with * between the paired comparison