Optimizing CT brain perfusion protocols is a challenge because of the complex interaction between image acquisition, calculation of perfusion data, and patient hemodynamics. Several digital phantoms have been developed to avoid unnecessary patient exposure or suboptimum choice of parameters. The authors expand this idea by using realistic noise patterns and measured tissue attenuation curves representing patient-specific hemodynamics. The purpose of this work is to validate that this approach can realistically simulate mean perfusion values and noise on perfusion data for individual patients.Methods:
The proposed 4D digital phantom consists of three major components: (1) a definition of the spatial structure of various brain tissues within the phantom, (2) measured tissue attenuation curves, and (3) measured noise patterns. Tissue attenuation curves were measured in patient data using regions of interest in gray matter and white matter. By assigning the tissue attenuation curves to the corresponding tissue curves within the phantom, patient-specific CTP acquisitions were retrospectively simulated. Noise patterns were acquired by repeatedly scanning an anthropomorphic skull phantom at various exposure settings. The authors selected 20 consecutive patients that were scanned for suspected ischemic stroke and constructed patient-specific 4D digital phantoms using the individual patients' hemodynamics. The perfusion maps of the patient data were compared with the digital phantom data. Agreement between phantom- and patient-derived data was determined for mean perfusion values and for standard deviation in de perfusion data using intraclass correlation coefficients (ICCs) and a linear fit.Results:
ICCs ranged between 0.92 and 0.99 for mean perfusion values. ICCs for the standard deviation in perfusion maps were between 0.86 and 0.93. Linear fitting yielded slope values between 0.90 and 1.06.Conclusions:
A patient-specific 4D digital phantom allows for realistic simulation of mean values and standard deviation in perfusion data and makes it possible to retrospectively study how the interaction of patient hemodynamics and scan parameters affects CT perfusion values.