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

PURPOSE This 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. METHODS A 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 AIR TM 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. RESULTS The 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 < 0.001). However, the DLR images showed a significantly more synthetic appearance than the other two images ( P < 0.05). There was no statistically significant difference in all scores between the original and filtered 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). CONCLUSION Using DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR.

Deep Learning in Magnetic Resonance Enterography for Crohn’s Disease Assessment: A Systematic Review

“…The use of deep learning models presents significant potential advancements. It offers enhanced accuracy with these algorithms applied to MRE image analysis for CD 12,13,24 . Dice coefficient values between studies ranged from 0.75 to 0.97, indicating similarity between the results of the deep learning models and those of conventional methods 25,26 .…”

“…Despite the promise shown by these studies, they are not without limitations. All, but one, of the studies are retrospective and none of them have had external validation [24][25][26][27][28][29] .…”

Impact of deep learning-based reconstruction and anti-peristaltic agent on the image quality and diagnostic performance of magnetic resonance enterography comparing single breath-hold single-shot fast spin echo with and without anti-peristaltic agent

“…The mean signal of the image slice was calculated, and it was divided by estimated standard deviation of noise level. The noise level was estimated by a hybrid discrete wavelet transform (DWT) and edge information removal-based algorithm ( 18 , 19 ).…”