Input function and modeling for determining bone metabolic flux using [ F] sodium fluoride PET imaging: A step-by-step guide.

Studies of skeletal metabolism using measurements of bone metabolic flux (K ) obtained with [ F] sodium fluoride ([ F]NaF) positron emission tomography (PET) scans have been used in clinical research for the last 30 years. The technique has proven useful as an imaging biomarker in trials of novel drug treatments for osteoporosis and investigating other metabolic bone diseases, including chronic kidney disease mineral and bone disorder. It has also been shown to be valuable in metastatic bone disease in breast cancer patients and may have potential in other cancer types, such as prostate cancer, to assess early bone fracture risk. However, these studies have usually required a 60-min dynamic PET scan and measurement of the arterial input function (AIF), making them difficult to translate into the clinic for diagnostic purposes. We have previously proposed a simplified method that estimates the K value at an imaging site from a short (4-min) static scan and venous blood samples. A key advantage of this method is that, by acquiring a series of static scans, values of K can be quickly measured at multiple sites using a single injection of the tracer. To date, the widespread use of [ F]NaF PET has been limited by the need to measure the AIF required for the mathematical modeling of tracer kinetics to derive K and other kinetic parameters. In this report, we review different methods of measuring the AIF, including direct arterial sampling, the use of a semi-population input function (SP-AIF), and image-derived input function, the latter two requiring only two or three venous blood samples obtained between 30 and 60 min after injection. We provide an SP-AIF model and a spreadsheet for calculating K values using the static scan method that others can use to study bone metabolism in metabolic and metastatic bone diseases without requiring invasive arterial blood sampling. The method shortens scan times, simplifies procedures, and reduces the cost of multicenter trials without losing accuracy or precision.