Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density
Objective: This study aimed to compare the quality of reconstructed images by deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) techniques at different scan doses using a phantom with liver density. Methods: The Gammex computed tomography (CT) pha...
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| Format: | Article |
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Editorial Office of Computerized Tomography Theory and Application
2025-07-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.056 |
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| _version_ | 1849704738520563712 |
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| author | Zhijie PAN Ling LIU Qingyao LI Tingting QU Shuai ZHANG Xueqian XIE |
| author_facet | Zhijie PAN Ling LIU Qingyao LI Tingting QU Shuai ZHANG Xueqian XIE |
| author_sort | Zhijie PAN |
| collection | DOAJ |
| description | Objective: This study aimed to compare the quality of reconstructed images by deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) techniques at different scan doses using a phantom with liver density. Methods: The Gammex computed tomography (CT) phantom with a standard liver-density insert (ρew=1.06) was scanned at six different radiation doses (CTDIvol): 30, 20, 15, 10, 7.5, and 4.5 mGy). Images obtained at each dose were reconstructed using DLIR and ASIR-V. Image quality was analyzed through the imQuest software. The quality of reconstructed images by DLIR at 4.5 mGy (lowest radiation dose) and ASIR-V at 15 mGy (recommended scan dose) were compared using the Bland–Altman method. Results: Across the six doses, DLIR significantly outperformed ASIR-V in key metrics, such as noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and detectability index (\begin{document}$d' $\end{document}). Bland–Altman analysis indicated that the quality of reconstructed images by DLIR at 4.5 mGy was significantly better to those by ASIR-V at 15 mGy. The noise level of DLIR images at 4.5 mGy was (17.41±0.32), which is significantly lower than that of ASIR-V at 15 mGy (21.17±0.67). At 4.5 mGy, DLIR SNR, CNR, and d’ were (3.21±0.24), (3.42±0.35), and (8.81±0.63), respectively, which are significantly higher than that of ASIR-V at 15 mGy (2.69±0.14), (2.87±0.11), and (5.61±1.28), respectively. Conclusion: In CT scan of focal liver-density lesions using a phantom, DLIR significantly improved the SNR, CNR, and d’ values and reduced image noise compared to ASIR-V. DLIR was able to achieve better quality image reconstruction at 4.5 mGy than the conventional ASIR-V reconstruction at 15 mGy. |
| format | Article |
| id | doaj-art-6289ca64f9294d7b8a87f57b623e4e6d |
| institution | DOAJ |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-6289ca64f9294d7b8a87f57b623e4e6d2025-08-20T03:16:39ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-07-0134467768510.15953/j.ctta.2024.0562024.056Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver DensityZhijie PAN0Ling LIU1Qingyao LI2Tingting QU3Shuai ZHANG4Xueqian XIE5Department of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, ChinaGE Healthcare, CT Research Center, Shanghai 210000, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, ChinaGE Healthcare, CT Research Center, Shanghai 210000, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, ChinaObjective: This study aimed to compare the quality of reconstructed images by deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) techniques at different scan doses using a phantom with liver density. Methods: The Gammex computed tomography (CT) phantom with a standard liver-density insert (ρew=1.06) was scanned at six different radiation doses (CTDIvol): 30, 20, 15, 10, 7.5, and 4.5 mGy). Images obtained at each dose were reconstructed using DLIR and ASIR-V. Image quality was analyzed through the imQuest software. The quality of reconstructed images by DLIR at 4.5 mGy (lowest radiation dose) and ASIR-V at 15 mGy (recommended scan dose) were compared using the Bland–Altman method. Results: Across the six doses, DLIR significantly outperformed ASIR-V in key metrics, such as noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and detectability index (\begin{document}$d' $\end{document}). Bland–Altman analysis indicated that the quality of reconstructed images by DLIR at 4.5 mGy was significantly better to those by ASIR-V at 15 mGy. The noise level of DLIR images at 4.5 mGy was (17.41±0.32), which is significantly lower than that of ASIR-V at 15 mGy (21.17±0.67). At 4.5 mGy, DLIR SNR, CNR, and d’ were (3.21±0.24), (3.42±0.35), and (8.81±0.63), respectively, which are significantly higher than that of ASIR-V at 15 mGy (2.69±0.14), (2.87±0.11), and (5.61±1.28), respectively. Conclusion: In CT scan of focal liver-density lesions using a phantom, DLIR significantly improved the SNR, CNR, and d’ values and reduced image noise compared to ASIR-V. DLIR was able to achieve better quality image reconstruction at 4.5 mGy than the conventional ASIR-V reconstruction at 15 mGy.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.056deep learningphantomctradiation dose |
| spellingShingle | Zhijie PAN Ling LIU Qingyao LI Tingting QU Shuai ZHANG Xueqian XIE Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density CT Lilun yu yingyong yanjiu deep learning phantom ct radiation dose |
| title | Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density |
| title_full | Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density |
| title_fullStr | Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density |
| title_full_unstemmed | Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density |
| title_short | Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density |
| title_sort | deep learning image reconstruction to improve computed tomography image quality of the phantom with standard liver density |
| topic | deep learning phantom ct radiation dose |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.056 |
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