Multiparametric quantitative MRI (MP-qMRI) provides comprehensive 3D tissue characterization in neuroimaging but remains highly susceptible to involuntary head motion. Motion correction in 3D MP-qMRI is particularly challenging, as even subtle head movements corrupt volumetric encoding, disrupt inter-contrast consistency, and bias quantitative parameter estimation. In this work, we propose a motion-corrected MP-qMRI framework that integrates rapid navigator-based motion tracking with motion-compensated implicit neural modeling. A spatiotemporal controlled aliasing (k-t CAIPI) navigator acquisition enables efficient estimation of time-resolved rigid-body motion. The implicit neural formulation embeds these estimates into the signal model to recover motion-corrected quantitative maps. The framework is evaluated through retrospective, simulation, and in vivo experiments, demonstrating substantial reductions in motion-induced artifacts and improved quantitative accuracy across T1, T2, and T2* maps. Evaluation in a motion-prone patient with spinocerebellar ataxia type 3 further highlights the robustness of the approach under clinically challenging conditions. Overall, this work establishes a principled and flexible approach for addressing motion in 3D MP-qMRI, providing a generalizable strategy for motion-resilient quantitative neuroimaging.

Diffusion magnetic resonance imaging (dMRI) enables noninvasive mapping of tissue microstructure by probing water molecule diffusivity. While advanced multi-shell diffusion models offer improved sensitivity to cellular properties, their requirement for densely sampled q-space data leads to prohibitively long acquisition times. Current deep learning approaches for parameter estimation face three key limitations: (1) dependency on fixed acquisition protocols, (2) model-specific assumptions that constrain applicability, and (3) reliance on supervised learning paradigms that demand large labeled datasets and exhibit poor generalization to out-of-distribution cases. To address these challenges, we propose MINeR, a novel unsupervised subject-specific framework for reconstructing dense q-space data from highly undersampled acquisitions. Our method leverages direction-modulated implicit neural representation to flexibly sample diffusion signals across q-space, supporting the estimation of parameters for diverse diffusion models. Comprehensive evaluations demonstrate that MINeR maintains high fidelity in microstructural parameter estimation, particularly for advanced multi-shell diffusion models. The framework shows remarkable generalization capability, as evidenced by its robust performance on tumor data. Notably, MINeR effectively reconstructs high-quality diffusion signals by interpolating from 6 directions, significantly reducing acquisition time, while maintaining robust parameter estimation. This work presents a practical approach for enabling microstructural modeling from sparsely sampled q-space data, thereby improving the clinical applicability of diffusion MRI. The code is available at: https://github.com/AMRI-Lab/MINeR.

Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengthy scan times for 3D multiparametric qMRI acquisition limit its clinical utility. Here, we propose SUMMIT, an innovative imaging methodology that includes data acquisition and an unsupervised reconstruction for simultaneous multiparametric qMRI. SUMMIT first encodes multiple important quantitative properties into highly undersampled k-space. It further leverages implicit neural representation incorporated with a dedicated physics model to reconstruct the desired multiparametric maps without needing external training datasets. SUMMIT delivers co-registered T 1 , T 2 , T 2 ∗ , and subvoxel quantitative susceptibility mapping. Extensive simulations, phantom, and in vivo brain imaging demonstrate SUMMIT’s high accuracy. Notably, SUMMIT uniquely unravels microstructural alternations in patients with white matter hyperintense lesions with high sensitivity and specificity. Additionally, the proposed unsupervised approach for qMRI reconstruction also introduces a novel zero-shot learning paradigm for multiparametric imaging applicable to various medical imaging modalities .