Improved method for post‐processing correction of B₁ inhomogeneity in glutamate‐weighted CEST images of the human brain

Abstract

Glutamate-weighted CEST (gluCEST) imaging is nearly unique in its ability to provide non-invasive, spatially resolved measurements of glutamate in vivo. In this article, we present an improved correction for B 1 inhomogeneity of gluCEST images of the human brain. Images were obtained on a Siemens 7.0 T Terra outfitted with a single-volume transmit/32-channel receive phased array head coil. Numerical Bloch-McConnell simulations, fitting and data processing were performed using inhouse code written in MATLAB and MEX (MATLAB executable). "Calibration" gluCEST data was acquired and fit with a phenomenological functional form first described here. The resulting surfaces were used to correct experimental data in accordance with a newly developed method. Healthy volunteers of varying ages were used for both fitted "calibration" data and corrected "experimental" data. Simulations allowed us to describe the dependence of CEST at 3.0 ppm (gluCEST) on saturation B 1 using a new functional form, whose validity was confirmed by successful fitting to real human data. This functional form was used to parameterize surfaces over the space (B 1 , T 1 ), which could then be used to correct the signal from each pixel. The resulting images show less signal loss in areas of low B 1 and greater contrast than those generated using the previously published method. We demonstrate that, using this method with appropriate nominal saturation B 1 , the major limitation of correcting for B 1 inhomogeneity becomes the effective flip angle of the acquisition module, rather than inability to correct for inhomogeneous saturation. The lower limit of our correction ability with respect to both saturation and acquisition B 1 is about 40% of the nominal value. In summary, we demonstrate a more rigorous and successful approach to correcting gluCEST images for B 1 inhomogeneity. Limitations of the method and further improvements to enable correction in regions with severe pathology are discussed.

Glutamate-Weighted Magnetic Resonance Imaging (GluCEST) Detects Effects of Transcranial Magnetic Stimulation to the Motor Cortex

“…Average gluCEST values range from 7-9%, typical for gray matter regions in healthy subjects using this protocol 31,32 . Interestingly, there appears to be some anatomic variability in gluCEST values in this slice at baseline; however, we did not attempt to further interpret these results, given the limited number of subjects.…”

“…CEST-weighted images were corrected for the B0 field distribution using the B0 image generated by the WASSR scan, as described in 30 . CEST images were corrected for B1 inhomogeneity using a recently developed procedure based on B1 and T1 mapping 32 . T1 and T2 weighted fullbrain structural images were used for segmentation by Freesurfer's 'Recon All' function 39 (Freesurfer: Martinos Center for Biomedical Imaging, Charlestown, MA, USA).…”

Repeatability of B1+ inhomogeneity correction of volumetric (3D) glutamate CEST via High‐permittivity dielectric padding at 7T

“…So, while the dielectric pads were of great benefit in correcting inhomogeneities, they do not eliminate the need for adequate post-processing, as clearly illustrated in the work that originally presented this type of post-processing correction method. 14 Also, while largely effective across the whole brain, a difference in dielectric pad effectiveness was observed in tissue segmentation analysis between GM and WM regions. In combination with inherent B 1 inhomogeneities present at ultra-high fields, a portion of the present B 1 drop off was due to the oblique slice taken in alignment with the hippocampus.…”

“…GluCEST‐weighted images were corrected for the B 0 field distribution using the acquired WASSR 26 and relative B 1 + field maps 27 . A further post‐processing correction strategy, as described in Cember et al, 14 was used to correct gluCEST contrast and quantitative accuracy in images acquired with and without dielectric padding. This post‐processing correction was used as a standard step when analyzing gluCEST data in later sections.…”

A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging

“…The first is to interpolate a value corresponding to rB 1 (relative B 1 , defined as real B 1 /nominal B 1 ) = 1 on the MTR-rB 1 plane or Z-rB 1 plane pixel by pixel [ 88 ]. The second is to fit the data on the MTR-rB 1 or Z-rB 1 plane with a selectively constructed function for each pixel group, divided based on the tissue types of T 1 values [ 89 , 90 ]. The third is to perform Bloch-McConnell fitting on the Z-spectra and generate new Z-spectra with B 1 values that are corrected from real ones to nominal ones [ 91 ].…”