Study of Deep Learning-Based Image Reconstruction Algorithms in the Diagnosis of Lower Limb Arterial Lesions Using CTA
Objective To explore the diagnostic value of deep learning based image reconstruction algorithms for computed tomography angiography(CTA)of lower limb arterial lesions.Methods CTA examination data from 51 patients(65 lower extremity arteries)with lower extremity arterial stenosis or occlusion who were treated in our hospital from June 2021 to February 2022 was retrospective collected.Based on the deep learning image reconstruction(DLIR)algorithm and hybrid iterative reconstruction(HIR)algorithm,CTA images were reconstructed separately,and the quality was evaluated using HIR as a reference.Two physicians assessed the location and degree of vascular stenosis under different reconstruction algorithms and observed interobserver consistency using Kappa test.Digital subtraction angiography was used as the"gold standard"to compare the performance of HIR and DLIR in diagnosing moderate and severe stenosis of lower extremity arteries.Results Compared with the HIR algorithm,the noise of image quality in DLIR algorithm was significantly reduced(ZSuperior knee artery=8.36,ZInfrapopliteal artery=9.46,ZDorsalis pedis artery =7.19,P<0.001),and signal to noise ratio(ZSuperior knee artery=-7.32,ZInfrapopliteal artery=-7.91,ZDorsalis pedis artery=-8.45,P<0.001)and contrast to noise ratio was significantly improved(ZSuperior knee artery=-8.66,ZInfrapopliteal artery=-9.21,ZDorsalis pedis artery=-8.52,P<0.001).Compared with the HIR method,images reconstructed based on DLIR showed significantly improved sensitivity(72.2%vs.94.4%)and specificity(78.7%vs.95.7%)for severe stenosis of the inferior knee artery,specificity for moderate stenosis in the dorsal foot artery(86.0%vs.97.7%)and the sensitivity for severe stenosis(50.0%vs.87.5%)(P<0.05).Conclusion DLIR algorithm can effectively enhance the quality of CTA images of lower extremity arteries,leading to improved diagnostic efficiency.
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