In the medical sector, various imaging methodologies or modalities (e.g. MRI, PET, CT) are used to assess the health of various parts of the bodies of patients. One such investigation is the blood flow or perfusion of the heart muscle, expressed as the (blood) flow rate normalized by the mass of the volume of interest. Currently there is no physical flow standard for the validation of quantitative perfusion measurements. This need has been addressed in the EMPIR 15HLT05 PerfusImaging project. A phantom simulating the heart muscle has been developed with the capability that it can reproducibly generate a flow profile with individual flow rates known with a relative uncertainty of about 10% (k = 2) and total flow rate known with an uncertainty of 1% (k = 2). An overview of the phantom and its validation is given. Next, a new analysis method is presented to analyse the sequence of images which are acquired when using a standard dynamic imaging protocol. It is concluded that the new, alternative approach gives results comparable to the standard analysis method.

This paper presents an approach for updating the epistemic uncertainty of ultrasonic flow meter measurements under non-ideal flow conditions. Instead of re-calibrating the instrument to correct its behavior in these difficult working conditions, a Bayesian calibration of a computer model of the real process is used. The numerical model is based on computational fluid dynamics (CFD) and a surrogate model is constructed from a limited number of CFD calculations using kriging. The computer model predicts the flow rate dependent on certain parameters including the bulk Reynolds number - which carries information about the true speed of the flow, and is measured only approximately by an ultrasonic flow meter. Bayesian calibration is applied, and the posterior of the true speed can be derived from the marginal posterior of the Reynolds number. This pdf has a smaller uncertainty with respect to the observed data used to fit the model on the condition that sufficiently informative data are available. If this is the case, the proposed approach is capable of reducing not only the uncertainty but also the error associated with the flow meter measurements in non-ideal conditions.