Title:An Evaluation Analysis for Computed Tomography Image Quality of Primary
Liver Cancer Lesions Based on Deep Learning Image Reconstruction
Volume: 20
Author(s): Yan Sun, De-zheng Sun and Chun-Lei Han*
Affiliation:
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, Shandong 266011, China
Keywords:
Deep learning image reconstruction (DLIR), Iterative reconstruction, Filtered back projection (FBP), Liver cancer, Contrast-enhanced computed tomography, ASIR-V.
Abstract:
Background:
Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically.
Objective:
The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer
lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative
reconstruction-V (ASIR-V).
Methods:
We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values
of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral
muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast
signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated.
Results:
High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion,
and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion
images among the six groups were statistically significant.
Conclusion:
The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer
patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.
