The potential for reduced radiation dose from deep learning-based CT image reconstruction

Abstract

Abstract The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms. A total of 15 CT scans of whole body phantoms were taken with 3 different tube voltages and 5 different tube currents. The images were reconstructed with FBP, 30% and 50% hybrid IR adaptive statistical iterative reconstruction (ASIR-V), and low, medium and high strength DLIR algorithms. The image scanned with tube voltage/tube current of 120\u200akV/ 200\u200amA and reconstructed with FBP algorithm was chosen as the reference image. Five radiologists independently analyzed the images individually and also compared it with the reference image in overall, using the visual grading analysis. The mean score of each image was calculated and compared. Using DLIR algorithms, the radiation dose was reduced by 65.5% to 68.1% compared with the dose used in the reference image, while maintaining comparable image quality. Using the DLIR algorithm of medium strength, the image quality was even better than the reference image with a reduced radiation dose up to 36.2% to 50.0%. The DLIR algorithms generated better quality images than ASIR-V algorithms in all the data sets. In addition, among the data sets reconstructed with DLIR algorithms, image quality was the best at the medium strength level, followed by low and high. This phantom study suggests that DLIR algorithms may be considered as a new reconstruction technique by reducing radiation dose while maintaining the image quality of abdominal CTs.