Feasibility of Implicit Neural Representation Learned Motion Compensation for 3D Stack-of-Spirals Free-Breathing Cardiac Quantitative Susceptibility Mapping.

PURPOSE: Differential blood oxygenation between the right and left heart (ΔSO2) is an indicator of cardiovascular function currently assessed in clinical practice by invasive right heart catheterization. Cardiac MRI can non-invasively quantify ΔSO2 with quantitative susceptibility mapping (QSM) using a prospective navigator gated 3D cartesian acquisition. However, this method suffers from long acquisition time and reduced robustness. Here, a free-breathing cardiac QSM using spiral sampling and deep learning motion compensation is proposed.

METHODS

A retrospective self-gated stack-of-spirals multi-echo gradient echo sequence is combined with implicit neural representation (INR) learning for image reconstruction. The self-gating signals measure superior-inferior cardiac and respiratory motion thus allowing k-space binning. Using a physics-informed signal model and the spatiotemporal coordinate input, INR infers motion fields as well as motion-corrected water, fat, and field maps. Then, QSM and ΔSO2 are accordingly computed. Data were acquired in 10 healthy subjects. For comparison, a free-breathing prospective navigator ECG-triggered Cartesian acquisition (NAV) was performed.

RESULTS

INR reconstructed motion-corrected water, fat, R2* and field maps were successfully obtained in all subjects. INR-QSM showed superior image quality (p = 0.0067) and equivalent ΔSO2 measurement in the heart (r = 0.74, p < 0.001; 1.07% ± 3.52% bias/limits of agreement) compared to the reference NAV-QSM.

CONCLUSION

This study demonstrated the feasibility of INR for compensation of cardiac and respiratory motion in free-breathing 3D cardiac QSM.