PrIINeR: Towards Prior-Informed Implicit Neural Representations for Accelerated MRI
This work addresses the need for high-quality, accelerated MRI reconstruction, which is crucial for medical imaging applications, by combining deep learning and INR-based techniques in an incremental way.
The paper tackled the problem of degraded image quality in accelerated MRI by proposing PrIINeR, a method that integrates prior knowledge from pre-trained models into implicit neural representations, resulting in improved structural preservation and fidelity while removing aliasing artefacts on the NYU fastMRI dataset.
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.