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Updated: Nov 21, 2025
Hybrid µCT-FMT imaging and image analysis
Published on: June 4, 2015
Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with
Yasutaka Ichikawa1, Yoshinori Kanii2, Akio Yamazaki2
1Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan. yasutaka@clin.medic.mie-u.ac.jp.
Purpose:
To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.
Method:
Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent).
Results:
The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT).
Conclusions:
Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.
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