Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment

被引:0
|
作者
Qi, Ke [1 ]
Xu, Chensi [2 ]
Yuan, Dian [1 ]
Zhang, Yicun [1 ]
Zhang, Mengyuan [1 ]
Zhang, Weiting [1 ]
Zhang, Jiong [1 ]
You, Bojun [1 ]
Gao, Jianbo [1 ]
Liu, Jie [1 ]
机构
[1] Zhengzhou Univ, Affiliated Hosp 1, Dept Radiol, 1 Eastern Jianshe Rd, Zhengzhou 450052, Henan Province, Peoples R China
[2] Neusoft Med Syst Co Ltd, CT Business Unit, 177-1 Innovat Rd, Shenyang, Liaoning Provin, Peoples R China
关键词
Computed tomography angiography; Aorta; Low tube voltage CT; Radiation dose; Contrast media; Deep learning; COMPUTED-TOMOGRAPHY ANGIOGRAPHY; ITERATIVE MODEL RECONSTRUCTION; LOW-TUBE-VOLTAGE; DOSE REDUCTION; CORONARY CT; OPTIMIZATION; REPAIR;
D O I
10.1016/j.acra.2024.10.042
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
Objective: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60 kVp) and a novel deep learning image reconstruction algorithm (ClearInfinity, DLIR-CI). Methods: Iodine attenuation curves obtained from a phantom study informed the administration of CM protocols. Non-obese participants undergoing aortic CTA were prospectively allocated into two groups and then obtained three reconstruction groups. The conventional group (100 kVp-CV group) underwent imaging at 100 kVp and received 210 mg iodine/kg in combination with a hybrid iterative reconstruction algorithm (ClearView, HIR-CV). The experimental group was imaged at 60 kVp with 105 mg iodine/kg, while images were reconstructed with HIR-CV (60 kVp-CV group) and with DLIR-CI (60 kVp-CI group). Student's t-test was used to compare differences in CM protocol and radiation dose. One-way ANOVA compared CT attenuation, image noise, SNR, and CNR among the three reconstruction groups, while the Kruskal-Wallis H test assessed subjective image quality scores. Post hoc analysis was performed with Bonferroni correction for multiple comparisons, and consistency analysis conducted in subjective image quality assessment was measured using Cohen's kappa. Results: The radiation dose (1.12 +/- 0.23 mSv vs. 2.03 +/- 0.82 mSv) and CM dosage (19.04 +/- 3.03 mL vs. 38.11 +/- 6.47 mL) provided the reduction of 45% and 50% in the experimental group compared to the conventional group. The CT attenuation, SNR, and CNR of 60 kVp-CI were superior to or equal to those of 100 kVp-CV. Compared to the 60 kVp-CV group, images in 60 kVp-CI showed higher SNR and CNR (all P < 0.001). There was no difference between the 60 kVp-CI and 100 kVp-CV group in terms of the subjective image quality of the aorta in various locations (all P > 0.05), with 60 kVp-CI images were deemed diagnostically sufficient across all vascular segments. Conclusion: For non-obese patients, the combined use of 60 kVp and DLIR-CI algorithm can be preserving image quality while enabling radiation dose and contrast medium savings for aortic CTA compared to 100 kVp using HIR-CV. (c) 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:1506 / 1516
页数:11
相关论文
共 50 条
  • [41] Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction
    Nam, Ju Gang
    Hong, Jung Hee
    Kim, Da Som
    Oh, Jiseon
    Goo, Jin Mo
    EUROPEAN RADIOLOGY, 2021, 31 (08) : 5533 - 5543
  • [42] Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction
    Ju Gang Nam
    Jung Hee Hong
    Da Som Kim
    Jiseon Oh
    Jin Mo Goo
    European Radiology, 2021, 31 : 5533 - 5543
  • [43] Feasibility of ultra-low radiation dose reduction for renal stone CT using model-based iterative reconstruction: prospective pilot study
    Kriegshauser, J. Scott
    Naidu, Sailen G.
    Paden, Robert G.
    He, Miao
    Wu, Qing
    Hara, Amy K.
    CLINICAL IMAGING, 2015, 39 (01) : 99 - 103
  • [44] Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography
    Yuan, Dian
    Wang, Luotong
    Lyu, Peijie
    Zhang, Yonggao
    Gao, Jianbo
    Liu, Jie
    INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING, 2024, 40 (06): : 1377 - 1388
  • [45] Low-dose CT angiography of the abdominal aorta and reduced contrast medium volume: Assessment of image quality and radiation dose
    Nijhof, W. H.
    Baltussen, E. J. M.
    Kant, I. M. J.
    Jager, G. J.
    Slump, C. H.
    Rutten, M. J. C. M.
    CLINICAL RADIOLOGY, 2016, 71 (01) : 64 - 73
  • [46] Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation
    Li, Mingqi
    Zeng, Dewen
    Fei, Hongwen
    Song, Hongning
    Chen, Jinling
    Cao, Sheng
    Hu, Bo
    Zhou, Yanxiang
    Guo, Yuxin
    Xu, Xiaowei
    Huang, Kui
    Zhang, Ji
    Zhou, Qing
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2023, 49 (10): : 2247 - 2255
  • [47] Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm
    Noda, Yoshifumi
    Takai, Yukiko
    Asano, Masashi
    Yamada, Nao
    Seko, Takuya
    Kawai, Nobuyuki
    Kaga, Tetsuro
    Miyoshi, Toshiharu
    Hyodo, Fuminori
    Kato, Hiroki
    Matsuo, Masayuki
    EUROPEAN JOURNAL OF RADIOLOGY, 2023, 159
  • [48] Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography
    Caruso, Damiano
    De Santis, Domenico
    Tremamunno, Giuseppe
    Santangeli, Curzio
    Polidori, Tiziano
    Bona, Giovanna G.
    Zerunian, Marta
    Del Gaudio, Antonella
    Pugliese, Luca
    Laghi, Andrea
    EUROPEAN RADIOLOGY, 2025, 35 (04) : 2213 - 2221
  • [49] Reducing the radiation dose for low-dose CT of the paranasal sinuses using iterative reconstruction: Feasibility and image quality
    Bulla, Stefan
    Blanke, Philipp
    Hassepass, Frederike
    Krauss, Tobias
    Winterer, Jan Thorsten
    Breunig, Christine
    Langer, Mathias
    Pache, Gregor
    EUROPEAN JOURNAL OF RADIOLOGY, 2012, 81 (09) : 2246 - 2250
  • [50] Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction
    Kang, Hyo-Jin
    Lee, Jeong Min
    Park, Sae Jin
    Lee, Sang Min
    Joo, Ijin
    Yoon, Jeong Hee
    CURRENT MEDICAL IMAGING, 2024, 20