Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to other reconstruction algorithms. The authors showed that the DLIR algorithm reduced image noise and variability of iodine concentration values when compared with other reconstruction algorithms.

Key points

• In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms.
• In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.

Article: Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study

Authors: Akiyo Fukutomi, Keitaro Sofue, Eisuke Ueshima, Noriyuki Negi, Yoshiko Ueno, Yushi Tsujita, Shinji Yabe, Takeru Yamaguchi, Ryuji Shimada, Akiko Kusaka, Masatoshi Hori & Takamichi Murakami