Open AccessImpact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm
Author(s) -
Shuo Yang,
Yifan Bie,
Guodong Pang,
Xingchao Li,
Kaixuan Zhao,
Changlei Zhang,
Hai Zhong
Publication year - 2021
Publication title -
journal of x-ray science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
eISSN - 1095-9114
pISSN - 0895-3996
DOI - 10.3233/xst-210953
Subject(s) - contrast to noise ratio , iterative reconstruction , medicine , image quality , algorithm , magnetic resonance imaging , nuclear medicine , image noise , contrast (vision) , reconstruction algorithm , tomography , radiology , mathematics , artificial intelligence , computer science , image (mathematics)
OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose. METHODS: The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared. RESULTS: For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%). CONCLUSION: Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.