The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT

Background: Traditional reconstruction techniques have certain limitations in balancing image quality and reducing radiation dose. The deep learning image reconstruction (DLIR) algorithm opens the door to a new era of medical image reconstruction. The purpose of the study was to evaluate the DLIR images at 1.25 mm thickness in balancing image noise and spatial resolution in low-dose abdominal computed tomography (CT) in comparison with the conventional adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V40%) at 5 and 1.25 mm. Methods: This retrospective study included 89 patients who underwent low-dose abdominal CT. Five sets of images were generated using ASIR-V40% at a 5 mm slice thickness and 1.25 mm (high-resolution) with DLIR at 1.25 mm using 3 strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Qualitative evaluation was performed for image noise, artifacts, and visualization of small structures, while quantitative evaluation was performed for standard deviation (SD), signal-to-noise ratio (SNR), and spatial resolution (defined as the edge rising slope). Results: At 1.25 mm, DLIR-M and DLIR-H images had significantly lower noise (SD in fat: 14.29 +/- 3.37 and 9.65 +/- 3.44 HU, respectively), higher SNR for liver (3.70 +/- 0.78 and 5.64 +/- 1.20, respectively), and higher overall image quality (4.30 +/- 0.44 and 4.67 +/- 0.40, respectively) than did the respective values in ASIR-V40% images (20.60 +/- 4.04 HU, 2.60 +/- 0.63, and 3.77 +/- 0.43; all P values <0.05). Compared with the 5 mm ASIR-V40% images, the 1.25 mm DLIR-H images had lower noise (SD: 9.65 +/- 3.44 vs. 13.63 +/- 10.03 HU), higher SNR (5.64 +/- 1.20 vs. 4.69 +/- 1.28), and higher overall image quality scores (4.67 +/- 0.40 vs. 3.94 +/- 0.46) (all P values <0.001). In addition, DLIR-L, DLIR-M, and DLIR-H images had a significantly higher spatial resolution in terms of edge rising slope (59.66 +/- 21.46, 58.52 +/- 17.48, and 59.26 +/- 13.33, respectively, vs. 33.79 +/- 9.23) and significantly higher image quality scores in the visualization of fine structures (4.43 +/- 0.50, 4.41 +/- 0.49, and 4.38 +/- 0.49, respectively vs. 2.62 +/- 0.49) than did the 5 mm ASIR-V40 images. Conclusions: The 1.25 mm DLIR-M and DLIR-H images had significantly reduced image noise and improved SNR and overall image quality compared to the 1.25 mm ASIR-V40% images, and theyhad significantly improved the spatial resolution , visualization of fine structures compared to the 5 mm ASIR-V40% images. DLIR-H images had further reduced image noise compared with the 5 mm ASIR-V40% images , DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.