FDoSR-Net: Frequency-Domain Informed Auto-Encoder Network for Arbitrary-Scale 3D Whole-Heart MRI Super-Resolution

This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH MR volumes, acquired using four different sequences, are used in this study for training, validation, and testing. The proposed method utilizes a frequency-domain regularization in training to maintain fine image detail along with a 3D autoencoder framework. It is also trained in manner to enable it to perform arbitrary factor SR. The proposed method is compared against multiple super-resolution algorithms including two state-of-the-art deep learning methods referred to here as ACNS and TFC as well as nearest neighbor interpolation. The proposed method was evaluated quantitatively and compared against the competing methods with the mean result of the proposed method and the improvements provided by the proposed method (reported by mean percentage between the proposed method and all other competing methods) were recorded. The metrics of interest used for the quantitative comparison are peak signal-to-noise ratio (PSNR, mean = 34.10, mean percentage of improvement = 4.5%), structural similarity index measure (SSIM, mean = 0.94, mean percentage of improvement = 2.2%), mean squared error (MSE, mean = 0.00094, mean percentage of improvement = 48.2%), and root mean squared error (RMSE, mean = 0.024, mean percentage of improvement = 31.0%). Moreover, qualitative comparison was performed using multiple visual comparisons. The quantitative results achieved demonstrate that the proposed method regularly outperforms all other comparison methods. The visual comparisons demonstrate that the proposed method outperforms current state-of-the-art methods in preserving fine image details, as well as its ability to do so for multiple SR factors.