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

Area of Science:

• Biomedical Imaging
• Immunology
• Cancer Research

Background:

• Tracking immune cell migration in vivo is crucial for understanding cancer immunogenicity and therapy response.
• Quantitative cell tracking using Turbo-Spiral Imaging (TurboSPI)-based R2* mapping shows promise for immune recruitment studies.
• Fat signal interference in R2* mapping can lead to inaccurate estimations of labeled cell density.

Purpose of the Study:

• To develop and validate a novel dual-decay Dixon-based signal model for accurate R2* estimation in the presence of fat.
• To improve quantitative cell tracking of superparamagnetic iron oxide (SPIO)-labeled immune cells.

Main Methods:

• A dual-decay (separate R2f* for fat and R2w* for water) Dixon model was proposed to account for fat in voxels.
• The model was tested using in silico simulations, phantoms with varying fat and SPIO-labeled cell concentrations, and in vivo studies with SPIO-labeled CD8+ T cells in mice.

Main Results:

• In silico simulations demonstrated that the dual-decay model provides stable R2w* estimates, independent of fat content.
• The proposed model outperformed existing methods in vitro with oil content ≥15%.
• Preliminary in vivo results showed improved R2* mapping balance in fat-dense regions, promising more reliable cell tracking.

Conclusions:

• The dual-decay model is a valuable tool for quantitative TurboSPI R2* cell tracking.
• Further refinements could enhance the specificity and sensitivity of SPIO-labeled cell tracking in complex biological environments.