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The accuracy of quantitative measurements of perfusion parameters by DCE-MRI is crucial as it can significantly impact the clinical care of cancer patients. In this study, we provide a method to identify reliable DCE-MRI brain data for perfusion quantification with different pharmacokinetic models. We analysed DCE-MRI data of 14 patients with primary brain tumours using the Tofts model (TM), the extended-Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). Due to the presence of the blood brain barrier, which can block the leakage of the CA into the interstitium, we also implemented the no-exchange model (NEM). For each lesion, we produced a 3D model selection map with the evaluation of the Akaike Information Criteria. The variability of each pharmacokinetic parameter extracted from the fitting of the model of choice was assessed with a noise propagation procedure, resulting in distributions of the coefficient of variation (CV). Results showed the NEM to be the most frequent model of choice (35.5%), followed by the ETM (32%), the TM (28.2%), the SSM (4.3%) and the ESSM (&lt;0.1%). In analysing the reliability of Ktrans, when considering regions with a CV&lt;20%, ≈25% of voxels were found to be stable. The remaining 75% of voxels were considered to be unreliable. In conclusion, an appropriate model selection, considering tissue biology and its effects on BBB permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in the identification of reliable brain DCE-MRI data.

Reliability of DCE MRI data in primary brain tumours: a comparison of Tofts and Shutter Speed Models