Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction

Abstract

Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. Methods One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 +/- 1.53 mGy; abdominal CT, 7.10 +/- 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. Results The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 +/- 2.81, 14.8 +/- 2.56, and 12.3 +/- 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 +/- 0.23 vs. 2.87 +/- 0.26; p < .001), while DLIR showed better spatial resolution (2.60 +/- 0.34 vs. 2.44 +/- 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. Conclusion With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers.