Background Conventional MRI fails to detect regions of glioblastoma cell infiltration beyond the contrast‐enhanced T1 solid tumor region, with infiltrating tumor cells often migrating along host blood vessels. Purpose To quantitatively and qualitatively analyze the correlation between perfusion MRI signal and tumor cell density in order to assess whether local perfusion perturbation could provide a useful biomarker of glioblastoma cell infiltration. Study Type Animal model. Subjects Mice bearing orthotopic glioblastoma xenografts generated from a patient‐derived glioblastoma cell line. Field Strength/Sequences 7T perfusion images acquired using a high signal‐to‐noise ratio (SNR) multiple boli arterial spin labeling sequence were compared with conventional MRI (T1/T2 weighted, contrast‐enhanced T1, diffusion‐weighted, and apparent diffusion coefficient). Assessment Immunohistochemistry sections were stained for human leukocyte antigen (probing human‐derived tumor cells). To achieve quantitative MRI‐tissue comparison, multiple histological slices cut in the MRI plane were stacked to produce tumor cell density maps acting as a “ground truth.” Statistical Tests Sensitivity, specificity, accuracy, and Dice similarity indices were calculated and a two‐tailed, paired t‐test used for statistical analysis. Results High comparison test results (Dice 0.62–0.72, Accuracy 0.86–0.88, Sensitivity 0.51–0.7, and Specificity 0.92–0.97) indicate a good segmentation for all imaging modalities and highlight the quality of the MRI tissue assessment protocol. Perfusion imaging exhibits higher sensitivity (0.7) than conventional MRI (0.51–0.61). MRI/histology voxel‐to‐voxel comparison revealed a negative correlation between tumor cell infiltration and perfusion at the tumor margins (P = 0.0004). Data Conclusion These results demonstrate the ability of perfusion imaging to probe regions of low tumor cell infiltration while confirming the sensitivity limitations of conventional imaging modalities. The quantitative relationship between tumor cell density and perfusion identified in and beyond the edematous T2 hyperintensity region surrounding macroscopic tumor could be used to detect marginal tumor cell infiltration with greater accuracy. Level of Evidence: 1 Technical stage: 2 J. Magn. Reson. Imaging 2019;50:529–540.
Stacked in-plane histology for quantitative validation of non-invasive imaging biomarkers: application to an infiltrative brain tumour model.
Phase contrast velocimetry (PCV) has been widely used to investigate flow properties in numerous systems. Several authors have reported errors in velocity measurements and have speculated on the sources, which have ranged from eddy current effects to acceleration artefacts. An often overlooked assumption in the theory of PCV, which may not be met in complex or unsteady flows, is that the intravoxel displacement distributions (propagators) are symmetric. Here, the effect of the higher moments of the displacement distribution (variance, skewness and kurtosis) on the accuracy of PCV is investigated experimentally and theoretically. Phase and propagator measurements are performed on tailored intravoxel distributions, achieved using a simple phantom combined with a single large voxel. Asymmetric distributions (Skewness ≠ 0) are shown to generate important phase measurement errors that lead to significant velocimetry errors. Simulations of the phase of the spin vector sum, based on experimentally measured propagators, are shown to quantitatively reproduce the relationship between measured phase and experimental parameters. These allow relating the observed velocimetry errors to a discrepancy between the average phase of intravoxel spins considered in PCV theory and the vector phase actually measured by a PFG experiment. A theoretical expression is derived for PCV velocimetry errors as a function of the moments of the displacement distribution. Positively skewed distributions result in an underestimation of the true mean velocity, while negatively skewed distributions result in an overestimation. The magnitude of these errors is shown to increase with the variance and decrease with the kurtosis of the intravoxel displacement distribution.
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