Reconstruction of cardiovascular black-blood T2-weighted image by deep learning algorithm: A comparison with intensity filter

Abstract

BACKGROUND: Deep learning-based methods have been used to denoise magnetic resonance imaging. PURPOSE: The purpose of this study was to evaluate a deep learning reconstruction (DL Recon) in cardiovascular black-blood T2-weighted images and compare with intensity filtered images. MATERIAL AND METHODS: Forty-five DL Recon images were compared with intensity filtered and the original images. For quantitative image analysis, the signal to noise ratio (SNR) of the septum, contrast ratio (CR) of the septum to lumen, and sharpness of the endocardial border were calculated in each image. For qualitative image quality assessment, a 4-point subjective scale was assigned to each image (1 = poor, 2 = fair, 3 = good, 4 = excellent). RESULTS: The SNR and CR were significantly higher in the DL Recon images than in the intensity filtered and the original images (p < .05 in each). Sharpness of the endocardial border was significantly higher in the DL Recon and intensity filtered images than in the original images (p < .05 in each). The image quality of the DL Recon images was significantly better than that of intensity filtered and original images (p < .001 in each). CONCLUSIONS: DL Recon reduced image noise while improving image contrast and sharpness in the cardiovascular black-blood T2-weight sequence.