Combining brain imaging data with electronic health records to non-invasively quantify [11C]DASB binding

Quantitative analysis of PET data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but one arterial blood sample is necessary to be used as an anchor value to ensure identifiability of each individuals AIF. For [¹¹C]DASB, a widely used serotonin transporter PET tracer, this blood sample is optimally taken 50 minutes after injection. We present here an approach for replacing such a single time-point anchor with a predicted value using brain imaging and electronic health record (EHR) data. Average bootstrap R² > 0.8 in training data suggest that up to 80% of the variance in [¹¹C]DASB SIME anchor may be explained by a model including heart rate, blood pressure, tracer dose, body size and cerebellar gray matter uptake. Preliminary results show that these models generalize well to a small test dataset. This may allow for quantitative analysis with no blood sampling.