# Rosette Trajectory MRI Reconstruction with Vision Transformers

## TL;DR

This paper introduces a new MRI reconstruction method using vision transformers to improve image quality from non-Cartesian data.

## Contribution

The novel approach combines inverse Fourier transform with a vision transformer network for efficient rosette trajectory MRI reconstruction.

## Key findings

- The method outperforms existing deep learning techniques in image quality metrics.
- It provides better runtime performance while maintaining competitive results across other metrics.

## Abstract

Introduction: 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 preprocessing. Materials and Methods: The inverse fast Fourier transform provides a robust initial approximation, which is refined by the ViT network to produce high-fidelity images. Results and Discussion: This approach outperforms established deep learning techniques for normalized root mean squared error, peak signal-to-noise ratio, and entropy-based image quality scores; offers better runtime performance; and remains competitive with respect to other metrics.