Cell density quantification with TurboSPI: R₂* mapping with compensation for off-resonance fat modulation

Tracking the migration of superparamagnetic iron oxide (SPIO)-labeled immune cells in vivo is valuable for understanding the immunogenic response to cancer and therapies. Quantitative cell tracking using TurboSPI-based R₂* mapping is a promising development to improve accuracy in longitudinal studies on immune recruitment. However, off-resonance fat signal isochromats lead to modulations in the signal time-course that can be erroneously fit as R₂* signal decay, overestimating the density of labeled cells, while excluding voxels with fat-typical modulations results in underestimation of cell density in voxels with mixed content. Approaches capable of accurate R₂* estimation in the presence of fat are needed.

We propose a dual-decay (separate R_2f* and R_2w* for fat and water) Dixon-based signal model that accounts for the presence of fat in a voxel to provide better estimates of SPIO-induced dephasing. This model was tested in silico, in phantoms with varying quantities of fat and SPIO-labeled cells, and in 5 mice injected with SPIO-labeled CD8+ T cells.

In silico single voxel simulations illustrate how the proposed dual-decay model provides stable R_2w* estimates that are invariant to fat content. The proposed model outperforms previous methods when applied to in vitro samples of SPIO-labeled cells and oil prepared with oil content ≥ 15%. Preliminary in vivo results show that, compared to previous methods, the dual-decay model improves the balance of R₂* mapping in fat-dense areas, which will yield more reliable analysis in future cell tracking studies.

The proposed model is a promising tool for quantitative TurboSPI R₂* cell tracking, with further refinements offering the possibility of better specificity and sensitivity.