From Perception to Precision: Navigating Perceptual Loss in MRI Super-Resolution

In the field of MRI super-resolution, training an image upscaling network under a pixel-oriented cost function (e.g., Mean-Intensity-Error) has proven to boost the signal-to-noise ratio. However, these types of cost functions tend to miss high-frequency details and fail to achieve an ideal sharpness, which is a pivotal image property for clinical applications to make diagnoses. To address this issue, the cost function of these upscaling networks typically includes a perceptual loss function, which is well recognized for the reconstruction of textures and enhancing sharpness, in addition to a pixel-oriented one. In this paper, we investigate the effect of perceptual loss on several MRI super-resolution metrics. We train UNet architecture under two loss function scenarios: One only including a pixel-oriented loss function, and the other a fusion of pixel-oriented and perceptual losses. We then employ an ablation study using a mixed effect model on a comprehensive set of evaluation criteria to measure the significance of change upon the inclusion of perceptual loss. Our results show that even though perceptual loss substantially shifts the networks towards outputting sharper images, it only causes negligible performance degradation in the accuracy of the reconstructed regions of interest, which can be alleviated using proper hyperparameter tuning.