The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image...

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.

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).

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.

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 they had significantly improved the spatial resolution and 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, and DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.