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Table 2 Comparison of reconstruction time, image noise, signal-to-noise ratio, contrast-to-noise ratio, and visual evaluation of coronary arteries between super-resolution deep learning reconstruction and model-based iterative reconstruction

From: Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience

Parameter

SR-DLR

MBIR

p-value

Reconstruction time, s

97 (88–109)

173 (164–187)

< 0.01

Image noise, HU

   

 Ascending aorta

22.5 (20.5–31.8)

29.1 (26.2–36.6)

< 0.01

 Left atrium

22.4 ± 4.0

30.1 ± 5.4

< 0.01

 Septal wall of the ventricle

20.5 ± 3.6

23.7 ± 3.8

< 0.01

 All locations

22.1 (19.3–24.9)

27.4 (24.2–31.2)

< 0.01

SNR

16.3 (11.8–21.8)

13.7 (9.9–18.4)

0.01

CNR

24.4 (15.5–30.2)

19.2 (14.1–23.2)

< 0.01

Overall quality (scale 1–4)

4.0 (4.0–4.0)

3.0 (3.0–4.0)

< 0.01

  1. SR-DLR, super-resolution deep learning reconstruction; MBIR, model-based iterative reconstruction; SNR, signal-to-noise ratio; CNR, contrast-to-noise ratio