Predicting before Reconstruction: A generative prior framework for MRI acceleration

TL;DR

This paper introduces a novel generative prior framework for MRI acceleration that predicts target images to serve as priors, significantly reducing scan times and outperforming existing methods across multiple datasets and acceleration factors.

Contribution

The study presents a new paradigm shift from traditional image reconstruction to predictive imaging using generative models as priors for highly accelerated MRI scans.

Findings

Outperforms existing MRI reconstruction methods with various acceleration factors.

Demonstrates effectiveness on large, diverse datasets including multi-channel k-space data.

Achieves significant improvements in image quality and reconstruction accuracy.

Abstract

Recent advancements in artificial intelligence have created transformative capabilities in image synthesis and generation, enabling diverse research fields to innovate at revolutionary speed and spectrum. In this study, we leverage this generative power to introduce a new paradigm for accelerating Magnetic Resonance Imaging (MRI), introducing a shift from image reconstruction to proactive predictive imaging. Despite being a cornerstone of modern patient care, MRI's lengthy acquisition times limit clinical throughput. Our novel framework addresses this challenge by first predicting a target contrast image, which then serves as a data-driven prior for reconstructing highly under-sampled data. This informative prior is predicted by a generative model conditioned on diverse data sources, such as other contrast images, previously scanned images, acquisition parameters, patient information. We demonstrate this approach with two key applications: (1) reconstructing FLAIR images using predictions from T1w and/or T2w scans, and (2) reconstructing T1w images using predictions from previously acquired T1w scans. The framework was evaluated on internal and multiple public datasets (total 14,921 scans; 1,051,904 slices), including multi-channel k-space data, for a range of high acceleration factors (x4, x8 and x12). The results demonstrate that our prediction-prior reconstruction method significantly outperforms other approaches, including those with alternative or no prior information. Through this framework we introduce a fundamental shift from image reconstruction towards a new paradigm of predictive imaging.