Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels

Abstract

To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength levels. This prospective study included 59 patients with 373 hepatic lesions who underwent dynamic contrast-enhanced CT of the abdomen. All images were reconstructed using four reconstruction algorithms, including 40% adaptive statistical iterative reconstruction-Veo (ASiR-V) and DLIR at low, medium, and high-strength levels (DLIR-L, DLIR-M, and DLIR-H, respectively). The signal-to-noise ratio (SNR) of the abdominal aorta, portal vein, liver, pancreas, and spleen and the lesion-to-liver contrast-to-noise ratio (CNR) were calculated and compared among the four reconstruction algorithms. The diagnostic acceptability was qualitatively assessed and compared among the four reconstruction algorithms and the conspicuity of hepatic lesions was compared between <5 and≥5 mm lesions. The SNR of each anatomical structure (p<0.0001) and CNR (p<0.0001) were significantly higher in DLIR-H than the other reconstruction algorithms. Diagnostic acceptability was significantly better in DLIR-M than the other reconstruction algorithms (p<0.0001). The conspicuity of hepatic lesions was highest when using 40% ASiR-V and tended to lessen as the reconstruction strength level was getting higher in DLIR, especially in <5 mm lesions; however, all hepatic lesions could be detected. DLIR improved the SNR, CNR, and image quality compared with 40% ASiR-V, while making it possible to decrease lesion conspicuity using higher reconstruction strength.