Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density

PAN Zhijie; LIU Ling; LI Qingyao; QU Tingting; ZHANG Shuai; XIE Xueqian

doi:10.15953/j.ctta.2024.056

Abstract：Objective:This study aimed to compare the quality of reconstructed images by deep learning image reconstruction(DLIR)and adaptive statistical iterative reconstruction-V(ASIR-V)techniques at different scan doses using a phantom with liver density.Methods:The Gammex computed tomography(CT)phantom with a standard liver-density insert(ρew=1.06)was scanned at six different radiation doses(CTDIvol):30,20,15,10,7.5,and 4.5mGy).Images obtained at each dose were reconstructed using DLIR and ASIR-V.Image quality was analyzed through the imQuest software.The quality of reconstructed images by DLIR at 4.5mGy(lowest radiation dose)and ASIR-V at 15mGy(recommended scan dose)were compared using the Bland-Altman method.Results:Across the six doses,DLIR significantly outperformed ASIR-V in key metrics,such as noise,signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and detectability index(d').Bland-Altman analysis indicated that the quality of reconstructed images by DLIR at 4.5mGy was significantly better to those by ASIR-V at 15mGy.The noise level of DLIR images at 4.5mGy was(17.41±0.32),which is significantly lower than that of ASIR-V at 15mGy(21.17±0.67).At 4.5mGy,DLIR SNR,CNR,and d'were(3.21±0.24),(3.42±0.35),and(8.81±0.63),respectively,which are significantly higher than that of ASIR-V at 15mGy(2.69±0.14),(2.87±0.11),and(5.61±1.28),respectively.Conclusion:In CT scan of focal liver-density lesions using a phantom,DLIR significantly improved the SNR,CNR,and d'values and reduced image noise compared to ASIR-V.DLIR was able to achieve better quality image reconstruction at 4.5mGy than the conventional ASIR-V reconstruction at 15mGy.