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Table 3 Objective measurement 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

P

5 mm images

 GM CT

32.58 ± 2.19

32.74 ± 2.36

32.77 ± 2.33

32.46 ± 2.31

0.23

0.88

 GM SD

4.51 ± 1.15

3.35 ± 0.84

2.28 ± 0.62*

2.26 ± 0.50*

93.83

 < 0.001

 WM CT

25.75 ± 2.66

25.49 ± 2.82

25.52 ± 2.84

25.56 ± 2.73

0.10

0.96

 WM SD

4.28 ± 0.84

3.16 ± 0.60

2.21 ± 0.57

2.00 ± 0.34

159.18

 < 0.001

 GM SNR

7.67 ± 1.83

10.35 ± 2.44

15.40 ± 4.29*

15.03 ± 3.13*

82.28

 < 0.001

 WM SNR

6.20 ± 1.12

8.28 ± 1.46

12.05 ± 2.42

13.07 ± 2.24

159.09

 < 0.001

 CNR

1.61 ± 0.60

2.30 ± 0.79

3.33 ± 1.10*

3.30 ± 1.06*

46.08

 < 0.001

0.625 mm images

 GM CT

32.57 ± 2.62

32.71 ± 2.68

32.76 ± 2.57

32.64 ± 2.52

0.06

0.98

 GM SD

8.23 ± 1.68

5.74 ± 1.18

3.43 ± 0.80*

3.35 ± 0.68*

221.42

 < 0.001

 WM CT

25.36 ± 3.00

25.37 ± 3.06

25.42 ± 3.00

25.47 ± 2.85

0.02

1.00

 WM SD

8.07 ± 1.57

5.60 ± 1.13

3.38 ± 0.81*

3.11 ± 0.58*

247.66

 < 0.001

 GM SNR

4.11 ± 0.86

5.94 ± 1.31

10.09 ± 2.53*

10.09 ± 1.96*

157.54

 < 0.001

 WM SNR

3.24 ± 0.67

4.68 ± 0.97

7.86 ± 1.80

8.40 ± 1.45

201.80

 < 0.001

 CNR

0.91 ± 0.41

1.33 ± 0.55

2.21 ± 0.85*

2.26 ± 0.77*

54.59

 < 0.001

  1. SG: 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