Title:Image Quality Improvement of Low-dose Abdominal CT using Deep Learning
Image Reconstruction Compared with the Second Generation Iterative
Reconstruction
Volume: 20
Author(s): Hyo-Jin Kang, Jeong Min Lee*, Sae Jin Park, Sang Min Lee, Ijin Joo and Jeong Hee Yoon
Affiliation:
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
Keywords:
Deep learning image reconstruction, CT, Image quality, Radiation dose, Iterative reconstruction, Abdomen
Abstract:
Background:
Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is
controversial.
Objectives:
To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the
second generation of adaptive statistical iterative reconstruction (ASiR-V).
Aims:
This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.
Methods:
In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and
routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were
reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M,
and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image
quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were
compared.
Results:
The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6
mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were
significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms.
DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for
AV30.
Conclusion:
DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.
