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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