Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging : Comparing to adaptive statistical iterative reconstruction algorithm
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). CNR was not statistically different between AV-70, DL-M, and DL-H (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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Journal of X-ray science and technology - 29(2021), 6 vom: 21., Seite 1009-1018 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Shuo [VerfasserIn] |
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| Links: |
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| Themen: |
Adaptive statistical iterative reconstruction (ASIR-V) |
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| Anmerkungen: |
Date Completed 31.03.2022 Date Revised 01.04.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/XST-210953 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM331116219 |
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| 100 | 1 | |a Yang, Shuo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging |b Comparing to adaptive statistical iterative reconstruction algorithm |
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| 500 | |a Date Completed 31.03.2022 | ||
| 500 | |a Date Revised 01.04.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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). CNR was not statistically different between AV-70, DL-M, and DL-H (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%) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adaptive statistical iterative reconstruction (ASIR-V) | |
| 650 | 4 | |a Computed tomography (CT) | |
| 650 | 4 | |a Deep learning image reconstruction (DLIR) | |
| 650 | 4 | |a Image quality evaluation | |
| 700 | 1 | |a Bie, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Guodong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xingchao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Changlei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Hai |e verfasserin |4 aut | |
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