Journal of magnetic resonance
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Homonuclear scalar coupling plays an important role in the elucidation of molecular structure and dynamics. However, complex multiplets due to 1H-1H scalar coupling splittings complicate the assignment of peaks in overcrowded spectral regions. ⋯ Herein, a simple data post-processing method based on the interleaved acquisition mode PSYCHEDELIC (Pure Shift Yielded by CHirp Excitation to DELiver Individual Couplings) is designed to acquire absorption-mode 2D J spectrum while eradicating axial peaks. This approach provides a high resolution and pure absorptive spectrum, permitting unambiguous and accurate measurement of scalar coupling constants involving a given proton.
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To demonstrate the feasibility of a new method for measuring T1 of short T2 species based on an adiabatic inversion recovery-prepared three-dimensional ultrashort echo time Cones (3D IR-UTE-Cones) sequence. ⋯ The 3D IR-UTE-Cones sequence could accurately measure short T1 values while providing high contrast images of short T2 species.
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Good B0 field homogeneity is considered an essential requirement to obtain high-quality MRS data. Many commonly used spectral fitting methods assume that all metabolite signals have Lorentzian or Gaussian shapes. However, B0 inhomogeneity can both broaden the linewidth and modify the lineshape. ⋯ The conventional approach, however quantifies metabolite concentrations with greater variations despite showing a supposedly improved fitting quality. The water lineshape basis set achieved single voxel spectroscopy accuracy that is less sensitive to the linewidth compared to the conventional spectral fitting method for the range of linewidths used in this study, but the precision deteriorated with worsening B0 field inhomogeneity. The beneficial effect was ascribed to a reduction in the number of degrees of freedom when using the water lineshape to generate the basis set.
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Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. ⋯ Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction.
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Phase and frequency corrections of magnetic resonance spectroscopic data are of major importance to obtain reliable and unambiguous metabolite estimates as validated in recent research for single-shot scans with the same spectral fingerprint. However, when using the J-difference editing technique 1H MEGA-PRESS, misalignment between mean edited (ON‾) and non-edited (OFF‾) spectra that may remain even after correction of the corresponding individual single-shot scans results in subtraction artefacts compromising reliable GABA quantitation. We present a fully automatic routine that iteratively optimizes simultaneously relative frequencies and phases between the mean ON‾ and OFF‾1H MEGA-PRESS spectra while minimizing the sum of the magnitude of the difference spectrum (L1 norm). ⋯ Automatically corrected data applying both, method (b) or method (c), showed distinct improvements of spectra quality as revealed by the mean Pearson correlation coefficient between corresponding real part mean DIFF‾ spectra of Rbd=0.997±0.003 (method (b) vs. (d)), compared to Rad=0.764±0.220 (method (a) vs. (d)) with no alignment between OFF‾ and ON‾. Method (c) revealed a slightly lower correlation coefficient of Rcd=0.972±0.028 compared to Rbd, that can be ascribed to small remaining subtraction artefacts in the final DIFF‾ spectrum. In conclusion, difference optimization performs robustly with no restrictions regarding the input data range or user intervention and represents a complementary tool to optimize the final DIFF‾ spectrum following the mandatory frequency and phase corrections of single ON and OFF scans prior to averaging.