Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI

Abstract

Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOFSSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test–retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOFSSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOFSSS derived semi-quantitative parameters was good to excellent (ICC = 0.717–0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOFSSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.

Low-dose GBCA administration for brain tumour dynamic contrast enhanced MRI: a feasibility study

“…For the LEGATOS reconstruction, a combined VIF extracted from the superior sagittal sinus (SSS) is used 19 , 26 . A previously described semiautomatic extraction method is used to select voxels within the SSS that during the first pass of the GBCA bolus display maximum enhancement 19 , 20 , 26 .…”

“…For the LEGATOS reconstruction, a combined VIF extracted from the superior sagittal sinus (SSS) is used 19 , 26 . A previously described semiautomatic extraction method is used to select voxels within the SSS that during the first pass of the GBCA bolus display maximum enhancement 19 , 20 , 26 . The VIF was constructed by concatenating all LDHT series segments according to acquisition time (i.e., the initial fifty dynamic HT frames plus the 12 LDHT series segments in the HS-HT interleaved parenchymal phase).…”

“…For each tissue voxel the GBCA bolus arrival time (BAT) is calculated as part of the fitting and the C p (t) measured from within the VIF time-shifted to align with the BAT of each tissue voxel contrast agent concentration–time curve 19 , 26 . A map of scaled fitting error (SFE) was also generated to assess the discrepancy between the original data and the derived curve 19 , using the equation: where A and T are experimental and theoretical data, respectively.…”

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

“…Quantifying DCE-MRI-derived kinetic parameters through modelling requires a suitable vascular input function (VIF), and previous studies have demonstrated that the usage of the superior sagittal sinus (SSS) as a surrogate input function provides a good approximation [ 63 , 64 ]. This VIF measurement method uses a semi-automatic extraction method to identify voxels within the SSS that display maximum enhancement during the first pass of the GBCA bolus, as has been previously described [ 65 ]. For the LEGATOS reconstruction, a combined vascular input function is constructed through concatenation of the GBCA concentration-time curve, C p ( t ), from the LDHT series with the later parenchymal phase of the dose-calibrated GBCA concentration-time curve from the FDHS-DCE series [ 4 , 6 ].…”

Inverse radon transform with deep learning: an application in cardiac motion correction

“…It has recently been gaining popularity within the medical imaging community. (Yan and Zhao 2021, Lewis et al 2022) VIF assumes that the shared information is highly related to the fidelity of the visual quality. In simple terms, VIF is the ratio of I(C; F) and I(C; E), where I(x; y) is the mutual information between image x and image y.…”