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 compressed sensing TurboSPI-based R2* mapping is a promising development to improve accuracy in longitudinal studies on immune recruitment. The phase-encoded TurboSPI sequence provides high fidelity relaxation data in the form of signal time-courses with high temporal resolution. However, early in vivo applications of this method revealed that simple mono-exponential R2* fitting performs poorly due to the contaminant fat signal in voxels surrounding regions of interest, such as flank tumors and lymph nodes adjacent to adipose tissue. This is especially problematic if there is poor infiltration to the tumor such that immune cells remain near the periphery. The presence of an off-resonance fat isochromat results in modulations in the signal time-course can be erroneously fit as R2* signal decay, thereby overestimating the density of SPIO labeled cells. Simply excluding any voxel with fat-typical modulations results in underestimates in voxels that have mixed content. We propose using a more comprehensive dual-decay (R2f* and R2w*) Dixon-based signal model that accounts for the potential presence of fat in a voxel to better estimate SPIO induced de-phasing. In silico single voxel simulations illustrate how the proposed signal model provides stable R2w* estimates that are invariant to fat content. The proposed dual-decay 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 Dixon model improves the balance of R2* specificity versus sensitivity, which in turn will result in more reliable analysis in future cell tracking studies.