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作者

Chun Yang Wenzhe Wang Dingye Cui Jinliang Zhang Ling Liu Yuxin Wang Wei Li

作者单位

Department of Radiology，The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Jinan，China；Shandong First Medical University and Shandong Academy of Medical Sciences，Jinan，China；Department of Radiology，The Fourth People’ Hospital of Jinan，Jinan，China；Department of Radiology，The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Jinan，China；Department of Radiology，The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Jinan，China；Shandong First Medical University and Shandong Academy of Medical Sciences，Jinan，China；CT Imaging Research Center，GE Healthcare，Shanghai，China；Department of Radiology，The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Jinan，China；Shandong First Medical University and Shandong Academy of Medical Sciences，Jinan，China；Department of Radiology，The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital，Jinan，China

刊名

定量影像学

年份

2023

卷号

第13卷

期号

第5期

页码

3161-3173

关键词

Deep learning image reconstruction computed tomography low-dose radiation abdomen image quality

摘要

Background The image quality of computed tomography can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. Methods Eight sets of CT images of a phantom were reconstructed with filtered back projection ; adaptive statistical iterative reconstruction-Veo at 30% , 50% , 80% , and 100% ; and deep learning image reconstruction at low , medium , and high levels. The noise power spectrum and task transfer func...更多

Background The image quality of computed tomography can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. Methods Eight sets of CT images of a phantom were reconstructed with filtered back projection ; adaptive statistical iterative reconstruction-Veo at 30% , 50% , 80% , and 100% ; and deep learning image reconstruction at low , medium , and high levels. The noise power spectrum and task transfer function were measured. Thirty consecutive patients underwent low-dose radiation contrast-enhanced abdominal CT scans that were reconstructed using FBP, AV-30, AV-50, AV-80, and AV-100, and three levels of DLIR. The standard deviation , signal-to-noise ratio , and contrast-to-noise ratio of the hepatic parenchyma and paraspinal muscle were evaluated. Two radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. Results In the phantom study, both a higher DLIR and ASiR-V strength and a higher radiation dose led less noise. The NPS peak and average spatial frequency of the DLIR algorithms were closer to those of FBP, as the tube current increased and declined as the level of ASiR-V and DLIR strengthened. The NPS average spatial frequency of DL-L were higher than those of AISR-V. In clinical studies, AV-30 demonstrated a higher SD and lower SNR and CNR compared to DL-M and DL-H . For qualitative assessment, DL-M produced the highest qualitative image quality scores, with the exception of overall image noise . The NPS peak, average spatial frequency, and SD were the highest and the SNR, CNR, and subjective scores were the lowest with FBP. Conclusions Compared with FBP and ASiR-V, DLIR provided better image quality and noise texture both in the phantom and clinical studies, and DL-M maintained the best image quality and lesion diagnostic confidence in low-dose radiation abdominal CT.收起

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