Structure-preserving Image-quality Enhancement for 3D Synthetic FLAIR Using a 3D U-Net with Content and Style Losses

Abstract

Purpose: Synthetic MRI can generate multiple contrasts from a single acquisition, yet synthetic fluid-attenuated inversion recovery (FLAIR) generally shows lower quality than conventional FLAIR. We aimed to improve 3D synthetic FLAIR image quality using deep learning, without losing the scan-time advantage of synthetic MRI.

Methods: We studied 55 adults with inflammatory demyelinating diseases who underwent 3T MRI. For each participant, five 3D quantification using an interleaved Look–Locker acquisition sequence with T2 preparation (3D-QALAS) source images and a conventional 3D-FLAIR image were acquired, and a synthetic FLAIR image was generated from the 3D-QALAS data. We trained a deep learning model in which a 3D U-shaped convolutional network (U-Net)–based attention network predicted voxel-wise weights for generating FLAIR images from the five 3D-QALAS source images, using conventional 3D-FLAIR images as the reference. The model was trained with a combined loss function of mean squared error, content loss, and style loss. Agreement with the reference was assessed using image similarity and error metrics, lesion overlap using the Dice similarity coefficient, and overall image quality and focal lesion visibility using blinded radiologist ratings. Synthetic FLAIR and the deep learning–generated FLAIR images were compared using 2-sided Wilcoxon signed-rank tests; P < 0.05 was considered statistically significant.

Results: The deep learning–generated FLAIR images showed significantly higher agreement with the reference image than synthetic FLAIR images (all P < 0.001). Lesion overlap was higher with the deep learning–generated FLAIR images (median [interquartile range]: 0.642 [0.528–0.711] versus 0.487 [0.344–0.641]). Qualitatively, the deep learning–generated FLAIR images improved overall image quality and focal lesion visibility, but reader scores remained lower than those for the reference conventional 3D-FLAIR images (all P < 0.001).

Conclusion: A deep learning–based approach applied to five 3D-QALAS source images improved the image quality of 3D synthetic FLAIR. These improvements may increase the clinical utility of 3D synthetic MRI for neuroradiologic assessment.