Human Brain High-Resolution Diffusion MRI With Optimized, Slice-By-Slice, Zeroth and First Order <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {B}_0 $$</annotation></semantics> </math> Shimming in Head-Only High-Gradient MRI Systems.

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Abstract

To propose a brain tissue-selective, optimized slice-by-slice <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {B}_0 $$</annotation></semantics> </math> shimming for high-resolution brain diffusion MRI. We incorporated actual gradient fields of the <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>X</mi></mrow> <annotation>$$ X $$</annotation></semantics> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>Y</mi></mrow> <annotation>$$ Y $$</annotation></semantics> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>Z</mi></mrow> <annotation>$$ Z $$</annotation></semantics> </math> imaging gradient coils, which are already designed for fast switching and accessible to any MRI system, in the calculation of the shimming coefficients in dynamic slice-by-slice <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {B}_0 $$</annotation></semantics> </math> shimming to minimize <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {B}_0 $$</annotation></semantics> </math> field inhomogeneity (i.e., <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>Δ</mi> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ \Delta {B}_0 $$</annotation></semantics> </math> ) in deep-learning segmented brain tissues. Diffusion MRI with oscillating gradient spin echo (OGSE) at 55 Hz and pulsed gradient spin echo (PGSE) (approximated at 0 Hz) were obtained in phantoms and healthy volunteers using a head-only high-gradient 3T MRI system. In each diffusion MRI acquisition, standard static volumetric shimming and the proposed shimming were applied separately, and mean/axial/radial diffusivities (MD/AD/RD) and fractional anisotropy (FA) were estimated. Compared to static shimming, dynamic shimming reduced the root-mean-square of voxel displacement of each slice by a maximum of 5-10 voxels in single-shot EPI acquisition at 1-2 mm in-plane resolution in the phantom, and a maximum of 3 voxels in human brains. Improved accuracy of MD/AD/RD/FA in the frontal lobe, brainstem, and cerebellum was observed by applying dynamic shimming and/or two-shot EPI acquisition. MD (55 Hz)-MD (0 Hz) showed higher values in <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> </msub> </mrow> <annotation>$$ {T}_2 $$</annotation></semantics> </math> FSE hypo-intensity region by applying dynamic shimming. Furthermore, in phantom, the root-mean-square of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>Δ</mi> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ \Delta {B}_0 $$</annotation></semantics> </math> in areas with high gradient nonlinearity was reduced by 7 Hz when incorporating the actual gradient field in dynamic shimming. Diffusion MRI with brain tissue-selective, dynamic slice-by-slice <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>B</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {B}_0 $$</annotation></semantics> </math> effectively improves diffusivity characterization in high-resolution images.

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