Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique – initial clinical experience

Abstract

Background A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. Purpose To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. Material and methods Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625\u2009mm and 2.5\u2009mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses. Results For 2.5\u2009mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p\u2009<\u20090.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p\u2009<\u20090.001). This improved image quality was also observed for 0.625\u2009mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5\u2009mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p\u2009<\u20090.01). Conclusions Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria.