MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors

TL;DR

MRI2Qmap introduces a novel framework that leverages deep learning priors from routine MRI images to improve quantitative MRI reconstruction, reducing reliance on ground-truth data and enhancing image quality.

Contribution

The paper presents a plug-and-play reconstruction method that integrates physical models with pretrained denoising autoencoders trained on large clinical MRI datasets, enabling effective quantitative mapping without ground-truth data.

Findings

Achieves competitive or superior performance to existing methods.

Effectively utilizes priors from routine MRI datasets.

Validates on in-vivo and simulated 3D brain data.

Abstract

Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties, but often suffer from aliasing artifacts due to compressed sampling. Incorporating spatial image priors can mitigate these artifacts, and deep learning has shown strong potential when large training datasets are available. However, extending this paradigm to MRF-type sequences remains challenging due to the scarcity of quantitative imaging data for training. Can this limitation be overcome by leveraging sources of training data from clinically-routine weighted MRI images? To this end, we introduce MRI2Qmap, a plug-and-play quantitative reconstruction framework that integrates the physical acquisition model with priors learned from deep denoising autoencoders pretrained on large multimodal weighted-MRI datasets. MRI2Qmap demonstrates that spatial-domain structural priors learned from independently acquired datasets of routine weighted-MRI images can be effectively used for quantitative MRI reconstruction. The proposed method is validated on highly accelerated 3D whole-brain MRF data from both in-vivo and simulated acquisitions, achieving competitive or superior performance relative to existing baselines without requiring ground-truth quantitative imaging data for training. By decoupling quantitative reconstruction from the need for ground-truth MRF training data, this framework points toward a scalable paradigm for quantitative MRI that can capitalize on the large and growing repositories of routine clinical MRI.