A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions

Objective: To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT. Methods: This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0. Results: Compared to AP, DP significantly reduced radiation dose by 76% (0.76 +/- 0.09 mSv vs 3.18 +/- 0.48 mSv), DLIR-H DP images had lower image noise (14.08 +/- 2.89 HU vs 16.67 +/- 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 0.34 vs 4.05 +/- 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 +/- 2.61 HU, 14.26 +/- 2.67 HU, 14.08 +/- 2.89 HU and 16.25 +/- 4.42 HU) from (24.95 +/- 4.32 HU, 25.42 +/- 4.99 HU, 23.99 +/- 5.26 HU and 27.01 +/- 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 +/- 0.34 vs 2.87 +/- 0.53; p < 0.05). Conclusion: DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction. Advances In knowledge: (1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images.

Automated summary

The study demonstrates that deep learning image reconstruction can significantly reduce radiation dose and improve image quality in abdominal CT. Particularly in low-dose conditions, DLIR-H generates images with lower noise and higher overall image quality.