To mitigate low sensitivity, several MRI data acquisition methods including fast low angle shot (FLASH), balance steady-state free precession (bSSFP), and fast spin echo (FSE) pulse sequences have been commonly used in ¹⁹F MRI cell tracking and optimized to maximize the image signal-to-noise ratio (SNR). A published cell quantification algorithm relies on the ratio of the detected in vivo ¹⁹F signal arising from ¹⁹F-labeled cells compared to a ¹⁹F reference phantom with a known ¹⁹F-spin density to estimate the apparent in vivo cell number.¹ However, spin-lattice (T₁) and spin-spin (T₂) relaxation properties can vary due to temperature, magnetic field strength, and chemical environment._² Choice in the data acquisition strategy may result in ¹⁹F signal contrast that is not dominantly spin-density weighted, thereby biasing any quantification with the relative T₁ and T₂ of the in vivo and phantom ¹⁹F spins. This work investigates MR parameter optimization and the resulting differential image weighting which may compromise accuracy of the estimated cell count for three different ¹⁹F pulse sequences.Relaxation parameters were measured on a ¹⁹F perfluoropolyether (PFPE) reference phantom (linearized PFPE suspended in agar; CelSense) and free PFPE cellular label (CS-1000-ATM, linearized PFPE in solution; CelSense). ¹⁹F spectra from an inversion recovery experiment (six TIs=0.063-2.0s) were fit to a mono-exponential signal recovery model for T₁ estimates. A CPMG pulse sequence measured T₂ from ¹⁹F spectra (six TEs=13.8-440ms). Global FIDs at ten readout delay times (0.5-120.5ms) were acquired for T₂* measurements. Both T₂ and T₂* data were fit to a mono-exponential decay model. All ¹⁹F relaxation measurements used a home-built ¹⁹F quadrature volumetric coil, 90° global RF excitation, 10kHz receiver bandwidth (rBW), 5.0s TR, and a hot air blower/temperature probe on a 4.7T Agilent MRI system (Agilent Technologies). Bloch simulations³ estimated transverse magnetization for FLASH, bSSFP, and FSE to optimize sequence parameter for SNR. The optimization utilized the relaxation measurements from the PFPE phantom at room temperature. Bloch simulations and theoretical SNR calculations, performed as previously reported,⁴ were then simulated for the free PFPE at 37°C to demonstrate variance in signal-weighting. Matrix size, spatial resolution and total acquisition time for all simulations were held constant. All simulations and post-processing were performed in MATLAB (MathWorks).The estimated ¹⁹F T₁ increased with temperature, as expected, while T₂ and T₂* did not vary greatly (Table 1). A large 201-262ms difference in T₁ and 205-211ms difference in T₂ were observed between the PFPE phantom and free PFPE as shown in Figure 1A-B. The workflow for optimizing the pulse sequences for SNR with Bloch simulations is shown in Figure 2 with the resulting optimized parameters listed in Table 2 for each pulse sequence. The simulated signal exhibited a substantial dependence on TR and ETL for FSE, flip angle for bSSFP and FLASH, and rBW for all sequences. The bSSFP led to the greatest simulated SNR but resulted in the largest percent error. The FSE led to the smallest percent error and showed an advantageous boost in SNR for the free PFPE instead of hindering the potential to detect ‘in vivo’ signal in the free PFPE. Using the optimized parameters resulted in considerable differences in the simulated ¹⁹F signal between the PFPE phantom (24°C) and the free PFPE (37°C) for FSE, FLASH, and bSSFP.The T₁ measurements of free PFPE corroborate with trends previously reported¹ at higher field strengths. Here, the free PFPE is a surrogate for the relaxation parameters that may arise from ¹⁹F labeled cells in vivo, which would reasonably be at body temperature (37°C). Off-resonance and regional variation in flip angle are not addressed in this work but would be expected to affect bSSFP and FLASH sequences more so than FSE. Despite potentially high SAR deposition, the FSE’s long optimum TR would enable multiple slices to be acquired with no additional time penalty. The simulated relative errors in ¹⁹F signal presented here suggest SNR optimization can compromise signal quantification and thus cell quantification.An optimization workflow is presented for three common ¹⁹F MRI pulse sequences. The simulated signal differences observed between a ¹⁹F reference phantom and pure ¹⁹F cellular label provide insight on the effects of pulse sequence SNR optimization for ¹⁹F cell tracking on the accuracy of in vivo quantification of cell number. Ongoing work will focus on validating simulations with acquired images and investigating the tradeoffs between TR, SNR, and quantification accuracy. These insights could lead to improved methods to balance the degree of spin-density weighting and SNR.