Institutional review board (IRB) approval of the University of Pennsylvania was obtained for this prospective study and informed consent was collected from the participants. All methods were carried out in accordance with relevant guidelines and regulations. 32 subjects with intra-axial brain mass suggestive of high-grade glioma, who were referred to radiology department of hospital of University of Pennsylvania from March 2018 to February 2020 were included in this study.
All MRI scans were performed on a Magnetom Tim Trio 3 Tesla scanner (Siemens, Erlangen, Germany) using a 12-channel phased array head coil. Conventional MRI sequences included axial T1-weighted (T1) before and after administration of gadolinium contrast agent (T1-Gd) with matrix size = 192×256×192, resolution = 0.98×0.98×1.00 mm3, repetition time (TR in ms)/echo time (TE in ms) = 1760/3.1, T2-weighted (T2) with matrix size = 208×256×64, resolution = 0.94×0.94×3.00, TR/TE = 4680/85; T2 fluid-attenuated inversion recovery (T2-FLAIR) with matrix size = 192×256×60, resolution = 0.94×0.94×3.00, TR/TE = 9420/141.
DSC-MR imaging was performed by a gradient-echo echo-planar (GE-EPI) imaging sequence during a second 0.1-mmol/kg bolus of Dotarem (Gadoterate Meglumine) with the following parameters: TR/TE = 2000/45 ms, FOV = 22 × 22 cm2, resolution = 1.72 × 1.72 × 3 mm3, 20 sections. A bolus of contrast agent with a dose of 0.1 mmol/kg which was done for DCE (dynamic-contrast-enhanced) imaging served as a preload dose for DSC imaging to reduce the effect of contrast agent leakage on relative cerebral blood volume (rCBV) measurements.
Acquisition of pH‐sensitive information was performed through an amine contrast specific for single-echo CEST-EPI sequence15,28. MR imaging acquisition parameters included the following: FOV= 240–256× 217–256 mm, matrix size = 128 × 116–128, slice thickness= 4 mm with no inter-slice gap, 25 consecutive slices, excitation pulse flip angle=90°, TE=27 ms, bandwidth= 1628 Hz, and generalized auto-calibrating partially parallel acquisition factor = 2–3. Off-resonance saturation was applied using a pulse train of 3 × 100 ms Gaussian pulses with a peak amplitude of 6 microtesla. A total of 29 off-resonance or z-spectral points were sampled at frequency offsets of -3.5 to-2.5 ppm, -0.3 to +0.3 ppm, and -2.5 to +3.5 ppm, all in increments of 0.1 ppm. A reference scan (S0) was obtained with the same acquisition parameters, without the saturation pulses. Total scan time for CEST-EPI was approximately 6 minutes.
For each patient, all MRI volumes (T1, T2, T2-FLAIR, DSC-MRI and MTRasym) were rigidly co-registered with their corresponding T1-Gd using the Greedy registration method 29 (https://github.com/pyushkevich/greedy). Subsequently, all conventional MRI scans (T1, T1-Gd, T2, T2-FLAIR) were smoothened to remove any high frequency intensity variations (i.e., noise) 30, corrected for magnetic field inhomogeneities using N4ITK method31 and skull-stripped using FSL BET32 followed by manual revision when needed. For brain tumor segmentation in the images, DeepMedic33, a Deep Learning (DL)-based segmentation algorithm in Cancer Imaging Phenomics Toolkit (CaPTk) v.1.7.8 34,35 which had been trained on BraTS 2017 training data, was applied to the co-registered conventional MRI scans. Brain tumor segmentation delineated three regions of interest (ROIs), i.e., enhancing tumor (ET), necrosis (NC), and peritumoral edema (ED), in the GBM tumors.
Amine CEST-EPI post-processing
Clinical post-processing of CEST-EPI consisted of affine motion correction (MCFLIRT; FSL, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MCFLIRT) and B0 correction via a z-spectra-based K-means clustering and Lorentzian fitting algorithm36. An integral of the width of 0.4 ppm was then obtained around both the -3.0 and +3.0 ppm (-3.2 to -2.8 and +2.8 to +3.2 ppm, respectively) spectral points of the inhomogeneity-corrected data. These data points were combined with the S0 image to calculate the asymmetry in the magnetization transfer ratio (MTRasym) at 3.0 ppm as defined by equation MTRasym (ω)= S(-ω)-S(ω)/S, where ω is the offset frequency of interest (3.0 ppm). All resulting maps were registered to high-resolution post-contrast T1- weighted images for subsequent analyses.
Temporal principal component analysis
Principal component analysis (PCA) is a dimensionality reduction method17 which was used in this study to distill the DSC-MRI time series down to a few components that capture the temporal dynamics of blood perfusion. All hemodynamic perfusion curves were aligned and normalized for the baseline and maximum drop across the patients37. We randomly selected voxels in each tumor subregion, i.e., ET, NC, and ED, and generated their signal intensity – time curves (Figure 1(A)). PCA was subsequently applied to capture the variance of the time series in all the ROIs and all subjects. Because of the relative consistency in the perfusion patterns of the various ROIs, seven principal components were sufficient to capture more than 99% of the variance in the perfusion signal for all tumor subregions and all patients.
Generation of MTRasym images based on PCs using machine learning
We built several regression models for tumor subregions using support vector machine regression (SVR) aiming to predict the MTRasym values from the seven PCs on a voxel-by-voxel basis to create a PC-derived MTRasym image, referred to as constructed MTRasym image. Leave-one-subject-out cross-validation of these predictive models was performed to ensure that the model and the associate estimates of accuracy would likely generalize to new patients. We trained the SVR models separately in ET, NC, and ED regions using Gaussian kernel functions with an automatic kernel scale and sequential minimal optimization (SMO) configuration. Performance of the SVR method was evaluated using Spearman’s correlation. All machine learning and statistical analyses was performed in MATLAB 18.104.22.1689201 (R2018a) Update 6.