Deep learning reconstruction algorithm in improving portal vein CT image quality / 西安交通大学学报(医学版)

ABSTRACT

【Objective】 To explore the value of deep learning reconstruction algorithm (DLIR) in improving image quality of portal vein. 【Methods】 We retrospectively enrolled 32 patients who underwent double-phasic enhanced abdominal CT scanning. Images at the portal venous phase were reconstructed using the 50% adaptive statistical iterative reconstruction (ASIR-V), DLIR at medium (DLIR-M) and high strength (DLIR-H). The CT value and image noise (standard deviation) of the main portal vein, the right portal vein branch, the left portal vein branch, and the paravertebral muscle were measured, and the contrast-noise-ratio (CNR) for vessels were calculated. Moreover, the edge-rising-slope (ERS) of the main portal vein edge was measured to evaluate image spatial resolution. The overall image noise, image contrast, and portal vein branch display were evaluated using a 5-point grading scale and image artifacts using a 4-point grading scare by two experienced radiologists. In addition, we calculated the display rate of small branches of the portal vein in the three reconstruction algorithms. 【Results】 Image noise of the DLIR images in the main portal vein, right branch and left branch was significantly lower than that of ASIR-V 50% images, of which the DLIR-H images had the lowest noise and highest CNR. The ERS of the DLIR images in the main portal vein was significantly higher than that of the ASIR-V 50% images. For qualitative analyses, the DLIR images were significantly better than the ASIR-V 50% ones (P<0.01). In addition, the display rates of small branches of the portal vein in DLIR images were (DLIR-M: 93.75%; DLIR-H: 100%), significantly higher than that of ASIR-V 50% (68.75%). 【Conclusion】 Compared with ASIR-V 50% images, DLIR images can significantly reduce the image noise and improve the spatial resolution of the portal vein and the display rate of small branches of the portal vein.