150 Chapter 6 scanned on a 1.5 Tesla MRI scanner (Inginia, Philips Medical Systems, Best, The Netherlands) using a 15 channel receive coil. A 3D SPGR sequence after injection of 0.1 ml gadovist/kg was performed (TR = 7.6 ms, TE = 3.4 ms, flip angle 8°, isotropic resolution: 1 mm, acquisition matrix 232 x 232 x 170). The clinician uses this clinically acquired MRI registered to the CT, to plan the radiotherapy and delineate the socalled gross tumor volume (i.e. brain metastases). The CT and corresponding gross tumor volume were extracted for each patient. For the current analysis, a distinction was made between newly treated brain metastases, resection cavities of brain metastases and previously irradiated brain metastases. Breathing protocol Hypercapnic stimuli were delivered using a computer-controlled gas blender and sequential delivery system (RespirActTM, Thornhill Research Institute, Toronto, Canada). The breathing mask was sealed to the patients’ face using transparent dressings (Tegaderm, 3M, St. Paul, MN, US) to acquire an air tight seal. Before starting the breathing challenges inside the MRI scanner, patients performed a test round with a CO2 challenge which is similar to the CO2 block given during the protocol. Only after successful completion of the test round, the breathing challenges inside the scanner were performed (Figure 1). The breathing challenges started with a 5-minute baseline period, followed by a block-shaped increase of end-tidal CO2 (PetCO2) 10 mmHg relative to a patients’ baseline for 90 seconds. After this so-called CO2-block, PetCO2 values returned to baseline values for 120 seconds, followed by a PetCO2 ramp increase of 12 mmHg relative to patients’ baseline for 180 seconds after which patients returned to baseline for 90 seconds. Pre-processing Pre-processing steps were performed using the Oxford Centre for Functional MRI of the BRAIN (FMRIB) Software Library (FSL – version 6.0; see Supplementary Figure 1).21 First, both the T1 and T2FLAIR images were brain extracted using BET.22 Tissue segmentation into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) was performed on the T1 image using FSL Automated Segmentation Tool (FAST).23 Additionally, an edema mask was created based on the T1 and T2FLAIR images using the lesion growth algorithm as implemented in the Lesion Segmentation Tool (LST, https://www.statistical-modelling.de/lst.html) for SPM.24 Based on previous experience, the initial threshold was set at 0.14. The resulting edema mask was manually adapted to eliminate any false positives or false negatives from the LST edema mask. The CT image was registered to the T1 image using FMRIB’s Linear Image Registration Tool (FLIRT).22,25
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