Rosette Trajectory MRI Reconstruction With Vision Transformers

An efficient pipeline for Rosette trajectory Magnetic Resonance Imaging reconstruction is proposed, combining the inverse Fourier transform with a Vision Transformer (ViT) network enhanced with a convolutional layer. This method addresses the challenges of reconstructing high-quality images from non-Cartesian data by leveraging the ViT’s ability to handle complex spatial dependencies without extensive pre-processing. The Inverse Fast Fourier Transform provides a robust initial approximation, which is refined by the ViT network to produce high-fidelity images. This approach outperforms established deep learning techniques for Normalized Root Mean Squared Error and Peak Signal-to-Noise Ratio scores, offers better runtime performance, and remains competitive with respect to Structural Similarity Index Measure scores and relative contrast.