Improving data fitting accuracy and precision for MRI cytometry

Motivation: A generalized protocol for efficient and accurate data fitting is important. Goal(s): To optimize an accurate and precise fitting approach for MRI cytometry. Approach: A Monte Carlo simulation was performed to optimize parameters for a dictionary-based fitting method. Additionally, a selection of data fitting methods were compared for accuracy and speed. Results: A dictionary step size of d 0.5 m and prior Gaussian distribution with dmean = 14 m and d = 100 m provide accurate and precise data fitting for a broad range of ground truth cell size values. Dictionary-matching with a prior distribution proved to be the most robust. Impact: To improve and achieve robust data fitting accuracy and precision for clinical applications.