Dynamic cell tracking using time-lapse MRI with variable temporal resolution Cartesian sampling

Abstract

Purpose: Temporal resolution of time-lapse MRI to track individual iron-labeled cells is limited by the required data-acquisition time to fill k-space and to reach sufficient SNR. Although motion of slowly patrolling monocytes can be resolved, detection of fast-moving immune cells requires improved acquisition and reconstruction strategies.

Theory and methods: For accelerated MRI cell tracking, a Cartesian sampling scheme was designed, in which the fully sampled and undersampled k-space data for different acceleration factors were acquired simultaneously, and multiple undersampling ratios could be chosen retrospectively. Compressed-sensing reconstruction was applied using dictionary learning and low-rank constraints. Detection of iron-labeled monocytes was evaluated with simulations, rotating phantom experiments and in vivo mouse brain measurements at 9.4 T.

Results: Fully sampled and 2.4-times and 4.8-times accelerated images were reconstructed and had sufficient contrast-to-noise ratio (CNR) for single cells to be resolved and followed dynamically. The phantom experiments showed an improvement in CNR of 6.1% per μm/s in the 4.8-times undersampled images. Geometric distortion of cells caused by motion was visibly reduced in the accelerated images, which enabled detection of moving cells with velocities of up to 7.0 μm/s. In vivo, additional cells were resolved in the accelerated images due to the improved temporal resolution.

Conclusion: The easy-to-implement flexible Cartesian sampling scheme with compressed-sensing reconstruction permits simultaneous acquisition of both fully sampled and high temporal resolution images. The CNR of moving cells is effectively improved, enabling the recovery of high velocity cells with sufficient contrast at virtually no cost.

Keywords: cell tracking; compressed sensing; dictionary learning; low rank; time lapse MRI; variable temporal resolution.