 imaging and Time-Activity Curve (TAC) analyses are essential for understanding and quantifying the biodistribution of radiopharmaceuticals over time and space. Traditional compartmental modeling%2C while foundational%2C commonly struggles to fully capture the complexities of biological systems%2C including non-linear dynamics and variability. This study introduces an innovative data-driven neural network-based framework%2C inspired by Reaction Diffusion systems%2C designed to address these limitations. Our approach%2C which adaptively fits TACs from dPET%2C enables the direct calibration of diffusion coefficients and reaction terms from observed data%2C offering significant improvements in predictive accuracy and robustness over traditional methods%2C especially in complex biological scenarios. By more accurately modeling the spatio-temporal dynamics of radiopharmaceuticals%2C our method advances modeling of pharmacokinetic and pharmacodynamic processes%2C enabling new possibilities in quantitative nuclear medicine.?quality=80&w=1200)
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