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Tracking the migration of superparamagnetic iron oxide (SPIO) labeled immunecells in vivo is valuable for understanding the immunogenic response to cancerand therapies. Quantitative cell tracking using compressed sensingTurboSPI-based R2* mapping is a promising development to improve accuracy inlongitudinal studies on immune recruitment. The phase-encoded TurboSPI sequenceprovides high fidelity relaxation data in the form of signal time-courses withhigh temporal resolution. However, early in vivo applications of this methodrevealed that simple mono-exponential R2* fitting performs poorly due to thecontaminant fat signal in voxels surrounding regions of interest, such as flanktumors and lymph nodes adjacent to adipose tissue. This is especiallyproblematic if there is poor infiltration to the tumor such that immune cellsremain near the periphery. The presence of an off-resonance fat isochromatresults in modulations in the signal time-course can be erroneously fit as R2*signal decay, thereby overestimating the density of SPIO labeled cells. Simplyexcluding any voxel with fat-typical modulations results in underestimates invoxels that have mixed content. We propose using a more comprehensivedual-decay (R2f* and R2w*) Dixon-based signal model that accounts for thepotential presence of fat in a voxel to better estimate SPIO inducedde-phasing. In silico single voxel simulations illustrate how the proposedsignal model provides stable R2w* estimates that are invariant to fat content.The proposed dual-decay model outperforms previous methods when applied to invitro samples of SPIO labeled cells and oil prepared with oil content >15%.Preliminary in vivo results show that, compared to previous methods, thedual-decay Dixon model improves the balance of R2* specificity versussensitivity, which in turn will result in more reliable analysis in future celltracking studies.

Cell Density Quantification with TurboSPI: R2* Mapping with Compensation for Off-Resonance Fat Modulation