TY - JOUR
T1 - Image quality assessment of abdominal CT by use of new deep learning image reconstruction
T2 - Initial experience
AU - Jensen, Corey T.
AU - Liu, Xinming
AU - Tamm, Eric P.
AU - Chandler, Adam G.
AU - Sun, Jia
AU - Morani, Ajaykumar C.
AU - Javadi, Sanaz
AU - Wagner-Bartak, Nicolaus A.
N1 - Funding Information:
Supported by an institutional cancer center support grant from the National Institutes of Health/National Cancer Institute under award number P30CA016672.
Publisher Copyright:
© American Roentgen Ray Society.
PY - 2020/7
Y1 - 2020/7
N2 - OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April–May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92–94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.
AB - OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April–May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92–94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.
KW - Artificial intelligence
KW - Deep learning
KW - Iterative reconstruction
KW - Radiation reduction
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U2 - 10.2214/AJR.19.22332
DO - 10.2214/AJR.19.22332
M3 - Article
C2 - 32286872
AN - SCOPUS:85089804813
SN - 0361-803X
VL - 215
SP - 50
EP - 57
JO - American Journal of Roentgenology
JF - American Journal of Roentgenology
IS - 1
ER -