Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients.

OBJECTIVES: To assess observer variability of different reference tissues used for relative CBV (rCBV) measurements in DSC-MRI of glioma patients. METHODS: In this retrospective study, three observers measured rCBV in DSC-MR images of 44 glioma patients on two occasions. rCBV is calculated by the CBV in the tumour hotspot/the CBV of a reference tissue at the contralateral side for normalization. One observer annotated the tumour hotspot that was kept constant for all measurements. All observers annotated eight reference tissues of normal white and grey matter. Observer variability was evaluated using the intraclass correlation coefficient (ICC), coefficient of variation (CV) and Bland-Altman analyses. RESULTS: For intra-observer, the ICC ranged from 0.50-0.97 (fair-excellent) for all reference tissues. The CV ranged from 5.1-22.1 % for all reference tissues and observers. For inter-observer, the ICC for all pairwise observer combinations ranged from 0.44-0.92 (poor-excellent). The CV ranged from 8.1-31.1 %. Centrum semiovale was the only reference tissue that showed excellent intra- and inter-observer agreement (ICC>0.85) and lowest CVs (<12.5 %). Bland-Altman analyses showed that mean differences for centrum semiovale were close to zero. CONCLUSION: Selecting contralateral centrum semiovale as reference tissue for rCBV provides the lowest observer variability. KEY POINTS: • Reference tissue selection for rCBV measurements adds variability to rCBV measurements. • rCBV measurements vary depending on the choice of reference tissue. • Observer variability of reference tissue selection varies between poor and excellent. • Centrum semiovale as reference tissue for rCBV provides the lowest observer variability.

A 4D digital phantom for patient-specific simulation of brain CT perfusion protocols.

PURPOSE: 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.