The super-resolution reconstruction in diffusion-weighted imaging of preoperative rectal MR using generative adversarial network (GAN): Image quality and T-stage assessment

Abstract

Aims

To assess the feasibility of using a generative adversarial network (GAN) to improve diffusion-weighted imaging (DWI) resolution in rectal MR scans for rectal carcinoma (RC), and to evaluate both the image quality and the diagnostic utility of super-resolution DWI (SR-DWI) in T stage assessment.

Materials and methods

In this retrospective investigation, a total of 291 patients diagnosed with RC during the period spanning May 2018 to December 2021 were included. The generated SR-DWI was evaluated against the original DWI using multi-scale structural similarity and peak signal-to-noise ratio. Two radiologists scored the SR-DWI and original DWI using a 4-point Likert scale in image quality. Moreover, both radiologists independently evaluated the T category staging based on T2WI and SR-DWI. Interobserver agreement was assessed using Cohen's kappa.

Results

The PSRN and MS-SSIM values of SR-DWI (4 ×) were significantly higher compared to those of SR-DWI (16 ×). Regarding the details of anatomic structures and overall image quality parameters, both radiologists exhibited a preference for SR DWI with 16 × enlargement over SR DWI with 4 × enlargement, yielding significantly superior ratings (both p < 0.001). The T-staging accuracy rates of SR-DWI (16 ×) performed by radiologist 1 and radiologist 2 were significantly superior to those achieved with T2WI (0.621 vs. 0.768, p = 0.027; 0.653 vs 0.810, p = 0.014).

Conclusions

Our study demonstrates that the adapted super-resolution approach can significantly improve the overall image quality and details of anatomic structure of DWI in rectal MR. And SR-DWI offer better diagnostic accuracy in RC T staging when compared with T2WI.