152 Chapter 6 modified whole-brain mask was generated using the ‘remLV.m’ function of the seeVR toolbox.28 For this,a temporal noise-to-signal (tNSR) map was calculated by taking the inverse of the temporal signal to noise (tSNR) map. Next, voxels showing values higher than the 98th percentile tNSR value were removed from the original whole brain-mask. This modified mask was then used in subsequent analyses.28 Next, a manual bulk alignment was performed between the PetCO2 and average GM time-series to minimize alignment errors that can occur when using automated correlation methods. Thereby, any bulk delays between end-tidal gas measurements at the lungs and BOLD signal responses in the brain were accounted for. Residual motion signals with a correlation higher than 0.3 with the GM time-series, along with a linear drift term were regressed out using a general linear model. BOLD data was then temporally de-noised using a wavelet-based approach29 and was spatially smoothed using a 3D Gaussian kernel (FWHM: 4 x 4 x 7 mm3). This kernel was chosen in order to best match the effective spatial resolution of the ASL data. BOLD data and corresponding PetCO2 traces were interpolated by a factor 4 (effective TR: 262.5 ms) to identify sub-TR signal displacements in subsequent hemodynamic lag analysis. A linear regression was performed between the bulk-aligned PetCO2 trace with each processed BOLD voxel time-series. The slope of this linear regression was taken as the CVR (ΔBOLD/mmHg PetCO2). Hemodynamic lag maps were generated using the Rapidtide10,30 approach as implemented in the seeVR toolbox28 and described previously.31 The multi-delay ASL was processed using the open-source ClinicalASL toolbox (available at https://github.com/JSIERO/ClinicalASL) and FSL BASIL for quantitative CBF maps.32 A T1-weighted image was reconstructed based on the M0 images using the ‘ASLT1fromM0Compute.m’ function. In short, as we used a Look-Locker read-out, the signal evolution over the multiple PLDs will show a T1-weighted signal response that was used to generate a surrogate T1 weighted image. This image had sufficient T1 contrast to be used for image registration (i.e. improved contrast compared to any single M0 or label/control image). Outlier removal was performed based on the standard deviation and tissue variance. Quantitative CBF and AAT maps were generated using the BASIL tool (Figure 2.2).33 Statistical analysis The CSF mask and non-brain metastases mask (containing areas of previously resected or irradiated brain metastases) were excluded from the hemodynamic parameter maps (Figure 2.3). Thereby, all analyzed tissue consists of either brain matter (GM and WM), edema or non-treated brain metastases. Next, the CVR values
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