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Feasibility of deep learning reconstruction algorithm combined with the "ultra triple-low" protocol at 60 kVp in
Zhichao Liu1, Qin Wang1, Xianying Ning1
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Ave, Wuhan 430022, Hubei China; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan 430022, Hubei, China; Hubei Key Laboratory of Molecular Imaging,Wuhan430022, Hubei, China.
Purpose:
To evaluate the feasibility of a deep learning-based reconstruction algorithm (Clear Infinity, CI) combined with an "ultra triple-low" protocol (60 kVp, 10 ml iodine contrast, 2.5 ml/s) for pulmonary CT angiography (CTPA).
Method:
Seventy patients (body mass index (BMI) < 28 kg/m2) were randomised to Group A (100 kVp, 30 ml, 4 ml/s, n = 35) and Group B (60 kVp, 10 ml, 2.5 ml/s, n = 35). Group B was subdivided into B1 (filtered back-projection (FBP)), B2 (hybrid iterative reconstruction (HIR), 50 % strength), and B3 (CI algorithm, 50 % strength). CT values, standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact index, and subjective scores (5-point Likert) were compared. Inter-observer agreement was assessed using Kappa analysis.
Results:
Group B reduced effective dose by 84.93 % (0.71 ± 0.09 vs. 4.71 ± 1.13 mSv, P < 0.001) and contrast volume by 66.7 % (10 ml vs. 30 ml). In Group B3, all pulmonary artery CT values exceeded 250 Hounsfield units (HU), and SNR, CNR, and subjective scores were superior (P < 0.001). Subjective artifact scores were also higher in Group B3 (P < 0.001). Inter-observer agreement was strong (κ = 0.87-0.92).
Conclusion:
In patients with BMI < 28 kg/m2, the CI algorithm (50 % strength) with the "ultra triple-low" protocol provides diagnostic image quality while reducing radiation and contrast doses, showing potential as a safer approach for high-risk populations, warranting further validation.
