Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose
ObjectiveTo evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD).MethodsA total of 72 pediatric CHD patients were divided into the low-contrast-dose group (CE-Boost group, injection of 0.5 mL per kilogram of body weight, n = 36) or the standard-scan group (CE-CT group, 1.5 mL per kilogram of body weight, n = 36), both groups undergoing imaging with SR-DLR. The two imaging protocols were compared based on radiation dose, objective image quality, subjective evaluation, and diagnostic accuracy. To quantitatively evaluate image quality, CT attenuation (HU) and standard deviation (SD) values were measured within ROIs at the four-chamber plane for cardiac chambers; an anonymized dataset was assessed using a double-blind methodology by two independent readers blinded to the clinical information and prior diagnoses of the pediatric patients.ResultsCE-Boost combined with SR-DLR significantly reduced contrast agent usage (62.3% reduction compared to CE-CT, P < 0.001) while maintaining image quality comparable to the conventional contrast protocol (P > 0.05). There was no significant difference in radiation dose parameters, including dose-length product (DLP, mGy<middle dot>cm) and volume-weighted CT dose index (CTDIvol, mGy) (all P > 0.1), while the effective dose (ED) in the CE-Boost group was slightly lower but not significant (0.36 vs. 0.43 mSv, P = 0.078). Additionally, the CE-Boost group's image quality metrics (CT values, SNR, CNR) remained stable, with no significant difference in subjective scores (P = 0.660).ConclusionCE-Boost combined with SR-DLR enables a significant reduction in contrast agent usage in pediatric CHD imaging while maintaining comparable image quality to conventional contrast protocols and optimizing SNR and CNR. This approach ensures diagnostic readability while minimizing contrast exposure, highlighting its feasibility and clinical value in pediatric CHD imaging.
基金:
Yunnan Talents Support Program [XDYC-MY-2022-0064]; Yunnan Provincial Department of Education Scientific Research Fund Project [2024J0261]; Key Laboratory of Cardiovascular Disease of Yunnan Province Project [2018DG008]
第一作者机构:[1]Kunming Med Univ, Kunming Yanan Hosp, Dept Radiol, Yanan Hosp, Kunming, Peoples R China[2]Key Lab Cardiovasc Dis Yunnan Prov, Kunming, Peoples R China
共同第一作者:
通讯作者:
通讯机构:[1]Kunming Med Univ, Kunming Yanan Hosp, Dept Radiol, Yanan Hosp, Kunming, Peoples R China[2]Key Lab Cardiovasc Dis Yunnan Prov, Kunming, Peoples R China
推荐引用方式(GB/T 7714):
Zhou Xinyan,Li Junyi,Ke Tengfei,et al.Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose[J].BMC MEDICAL IMAGING.2025,25(1):doi:10.1186/s12880-025-02015-2.
APA:
Zhou, Xinyan,Li, Junyi,Ke, Tengfei,Xiong, Daoqiang,Liu, Fei...&Liao, Chengde.(2025).Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose.BMC MEDICAL IMAGING,25,(1)
MLA:
Zhou, Xinyan,et al."Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose".BMC MEDICAL IMAGING 25..1(2025)