Impact of noise index combined with deep learning image reconstruction on image quality and radiation dose
Objective To explore the application value of Deep learning image reconstruction(DLIR)in ultra-low-dose chest CT imaging.Methods A total of 66 patients with chest CT scans in the Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine from March to April 2024 were collected.All patients were used GE Revolution CT scans,the fixed tube voltage was 100 kVp,and the first with conventional radiation dose with noise index(NI)=15,and filtered back projection reconstructed images;the second was scanned with ultra-low-dose with NI=45,and medium and high intensity deep learning image reconstruction(DLIR-M、DLIR-H)were compared.The CT value and standard deviation(SD)of the left upper pulmonary hypovascular region were measured on three reconstructed images,SD represented noise,and the signal-to-noise ratio(SNR)was calculated.Subjective evaluation of 5-point method was used by two radiologists.The objective value and subjective score of three reconstructed images were compared.Results The NI=45 group reducted the radiation dose by 93.7%.The intensity of DLIR affected the objective value under ultra-low-dose condition,DLIR-H resulted in ower noise and higher SNR than DLIR-M(P＜0.05).Two physicians evaluated the image quality consistency of the three reconstructed images(Kappa=0.952,0.846,0.903).The image quality scores,pass rates and satisfaction rates had no significant differences between three groups(P＞0.05).Conclusion Under the condition of reducing the radiation dose by 93.7%,DLIR can obtain images of the lung that are close to the conventional radiation dose,and the radiation dose for lung disease screening has been further reduced.
万方数据
维普
