Voxelwise comparison of perfusion parameters estimated using dynamic contrast enhanced (DCE) computed tomography and DCE-magnetic resonance imaging in locally advanced cervical cancer

Abstract

Purpose: Dynamic contrast enhanced (DCE) imaging has gained interest as an imaging modality for assessment of tumor characteristics and response to cancer treatment. However, for DCE-magnetic resonance imaging (MRI) tissue contrast enhancement may vary depending on imaging sequence and temporal resolution. The aim of this study is to compare DCE-MRI to DCE-computed tomography (DCE-CT) as the gold standard.

Material and methods: Thirteen patients with advanced cervical cancer were scanned once prior to chemo-radiation and during chemo-radiation with DCE-CT and -MRI in immediate succession. A total of 22 paired DCE-CT and -MRI scans were acquired for comparison. Kinetic modeling using the extended Tofts model was applied to both image series. Furthermore the similarity of the spatial distribution was evaluated using a Γ analysis. The correlation between the two imaging techniques was evaluated using Pearson's correlation and the parameter means were compared using a Student's t-test (p < 0.05).

Results: A significant positive correlation between DCE-CT and -MRI was found for all kinetic parameters. The results showing the best correlation with the DCE-CT-derived parameters were obtained using a population-based input function for MRI. The median Pearson's correlations were: volume transfer constant K(trans) (r = 0.9), flux rate constant kep (r = 0.77), extracellular volume fraction ve (r = 0.58) and blood plasma volume fraction vp (r = 0.83). All quantitative parameters were found to be significantly different as estimated by DCE-CT and -MRI. The Γ analysis in normalized maps revealed that 45% of the voxels failed to find a voxel with the corresponding value allowing for an uncertainty of 3 mm in position and 3% in value (Γ3,3). By reducing the criteria, the Γ-failure rates were: Γ3,5 (37% failure), Γ3,10 (26% failure) and at Γ3,15 (19% failure).

Conclusion: Good to excellent correlations but significant bias was found between DCE-CT and -MRI. Both the Pearson's correlation and the Γ analysis proved that the spatial information was similar when analyzing the two sets of DCE data using the extended Tofts model. Improvement of input function sampling is needed to improve kinetic quantification using DCE-MRI.