PrIINeR: Towards Prior-Informed Implicit Neural Representations for Accelerated MRI

TL;DR

PrIINeR introduces a novel MRI reconstruction method that integrates prior knowledge from pre-trained models into implicit neural representations, significantly enhancing image quality at high acceleration factors.

Contribution

It is the first to combine prior-informed deep learning with INR frameworks for accelerated MRI, improving structural preservation and artifact removal.

Findings

Outperforms state-of-the-art INR-based methods

Improves structural preservation and fidelity

Effectively removes aliasing artifacts

Abstract

Accelerating Magnetic Resonance Imaging (MRI) reduces scan time but often degrades image quality. While Implicit Neural Representations (INRs) show promise for MRI reconstruction, they struggle at high acceleration factors due to weak prior constraints, leading to structural loss and aliasing artefacts. To address this, we propose PrIINeR, an INR-based MRI reconstruction method that integrates prior knowledge from pre-trained deep learning models into the INR framework. By combining population-level knowledge with instance-based optimization and enforcing dual data consistency, PrIINeR aligns both with the acquired k-space data and the prior-informed reconstruction. Evaluated on the NYU fastMRI dataset, our method not only outperforms state-of-the-art INR-based approaches but also improves upon several learning-based state-of-the-art methods, significantly improving structural preservation and fidelity while effectively removing aliasing artefacts.PrIINeR bridges deep learning and INR-based techniques, offering a more reliable solution for high-quality, accelerated MRI reconstruction. The code is publicly available on https://github.com/multimodallearning/PrIINeR.