Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence

被引:4
|
作者
Yang, Chun [1 ,2 ,3 ]
Wang, Wenzhe [4 ]
Cui, Dingye [1 ,2 ]
Zhang, Jinliang [1 ,2 ,3 ]
Liu, Ling [5 ]
Wang, Yuxin [1 ,2 ,3 ]
Li, Wei [1 ,2 ]
机构
[1] Shandong First Med Univ, Dept Radiol, Affiliated Hosp 1, Jingshi Rd, Jinan 250014, Peoples R China
[2] Shandong Prov Qianfoshan Hosp, Jingshi Rd, Jinan 250014, Peoples R China
[3] Shandong First Med Univ & Shandong Acad Med Sci, Jinan, Peoples R China
[4] Fourth People Hosp Jinan, Dept Radiol, Jinan, Peoples R China
[5] GE Healthcare, CT Imaging Res Ctr, Shanghai, Peoples R China
关键词
Deep learning image reconstruction; computed tomography; low-dose radiation; abdomen; image quality; STATISTICAL ITERATIVE RECONSTRUCTION; NOISE POWER SPECTRUM; CT; REDUCTION;
D O I
10.21037/qims-22-1227
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: The image quality of computed tomography (CT) 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 (FBP); adaptive statistical iterative reconstruction-Veo (ASiR-V) at 30% (AV-30), 50% (AV-50), 80% (AV-80), and 100% (AV-100); and deep learning image reconstruction (DLIR) at low (DL-L), medium (DL-M), and high (DL-H) levels. The noise power spectrum (NPS) and task transfer function (TTF) 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 (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) 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 (P<0.05). For qualitative assessment, DL-M produced the highest qualitative image quality scores, with the exception of overall image noise (P<0.05). 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.
引用
收藏
页码:3161 / 3173
页数:13
相关论文
共 50 条
  • [1] Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose
    Zhang, Kun
    Shi, Xiang
    Xie, Shuang-Shuang
    Sun, Ji-Hang
    Liu, Zhuo-Heng
    Zhang, Shuai
    Song, Jia-Yang
    Shen, Wen
    [J]. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2022, 12 (06) : 3238 - +
  • [2] Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis
    Delabie, Aurelien
    Bouzerar, Roger
    Pichois, Raphael
    Desdoit, Xavier
    Vial, Jeremie
    Renard, Cedric
    [J]. ACTA RADIOLOGICA, 2022, 63 (09) : 1283 - 1292
  • [3] Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise
    Kim, Joo Hee
    Yoon, Hyun Jung
    Lee, Eunju
    Kim, Injoong
    Cha, Yoon Ki
    Bak, So Hyeon
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2021, 22 (01) : 131 - 138
  • [4] Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography
    Steuwe, Andrea
    Weber, Marie
    Bethge, Oliver Thomas
    Rademacher, Christin
    Boschheidgen, Matthias
    Sawicki, Lino Morris
    Antoch, Gerald
    Aissa, Joel
    [J]. BRITISH JOURNAL OF RADIOLOGY, 2021, 94 (1117):
  • [5] Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm
    Caruso, Damiano
    De Santis, Domenico
    Del Gaudio, Antonella
    Guido, Gisella
    Zerunian, Marta
    Polici, Michela
    Valanzuolo, Daniela
    Pugliese, Dominga
    Persechino, Raffaello
    Cremona, Antonio
    Barbato, Luca
    Caloisi, Andrea
    Iannicelli, Elsa
    Laghi, Andrea
    [J]. EUROPEAN RADIOLOGY, 2024, 34 (04) : 2384 - 2393
  • [6] Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm
    Damiano Caruso
    Domenico De Santis
    Antonella Del Gaudio
    Gisella Guido
    Marta Zerunian
    Michela Polici
    Daniela Valanzuolo
    Dominga Pugliese
    Raffaello Persechino
    Antonio Cremona
    Luca Barbato
    Andrea Caloisi
    Elsa Iannicelli
    Andrea Laghi
    [J]. European Radiology, 2024, 34 : 2384 - 2393
  • [7] Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction
    Noda, Yoshifumi
    Iritani, Yukako
    Kawai, Nobuyuki
    Miyoshi, Toshiharu
    Ishihara, Takuma
    Hyodo, Fuminori
    Matsuo, Masayuki
    [J]. ABDOMINAL RADIOLOGY, 2021, 46 (09) : 4238 - 4244
  • [8] Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction
    Yoshifumi Noda
    Yukako Iritani
    Nobuyuki Kawai
    Toshiharu Miyoshi
    Takuma Ishihara
    Fuminori Hyodo
    Masayuki Matsuo
    [J]. Abdominal Radiology, 2021, 46 : 4238 - 4244
  • [9] Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy
    Wang, Tonghe
    Lei, Yang
    Tian, Zhen
    Dong, Xue
    Liu, Yingzi
    Jiang, Xiaojun
    Curran, Walter J.
    Liu, Tian
    Shu, Hui-Kuo
    Yang, Xiaofeng
    [J]. JOURNAL OF MEDICAL IMAGING, 2019, 6 (04)
  • [10] The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
    Zhang, Minghan
    Gu, Sai
    Shi, Yuhui
    [J]. COMPLEX & INTELLIGENT SYSTEMS, 2022, 8 (06) : 5545 - 5561