LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn’s disease: utility in noise reduction and image quality improvement

Abstract

PURPOSEThis study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms of image quality.METHODSA total of 35 patients who underwent MRE for Crohn’s disease between August 2021 and February 2022 were included in this retrospective study. The enteric phase CE-T1W MRE images of each patient were reconstructed with conventional reconstruction and no image filter (original), with conventional reconstruction and image filter (filtered), and with a prototype version of AIRTM Recon DL 3D (DLR), which were then reformatted into the axial plane to generate six image sets per patient. Two radiologists independently assessed the images for overall image quality, contrast, sharpness, presence of motion artifacts, blurring, and synthetic appearance for qualitative analysis, and the signal-to-noise ratio (SNR) was measured for quantitative analysis.RESULTSThe mean scores of the DLR image set with respect to overall image quality, contrast, sharpness, motion artifacts, and blurring in the coronal and axial images were significantly superior to those of both the filtered and original images (P 0.05). In the quantitative analysis, the SNR was significantly increased in the order of original, filtered, and DLR images (P < 0.001).CONCLUSIONUsing DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR.

Keywords

• crohn’s disease
• mr enterography
• image quality
• deep learning
• noise reduction