Background R2* correction for χ-separation (chi-separation)

1693 

Abstract Submission 

Kyeongseon Min¹, Jongho Lee¹

¹Seoul National University, Seoul, Seoul

Kyeongseon Min  
Seoul National University
Seoul, Seoul

Jongho Lee  
Seoul National University
Seoul, Seoul

χ-separation (chi-separation) is an advanced quantitative susceptibility mapping (QSM) technique that jointly utilizes tissue phase and transverse relaxation rate (R2* and R2) maps to separate paramagnetic (χpara) and diamagnetic susceptibility (χdia) contributions in QSM (Shin, 2021). Inaccurate tissue field or R2* (or R2) maps may result in erroneous χpara and χdia maps. When creating a tissue field map, background field from susceptibility difference (e.g. air-tissue interface) and incomplete shimming is removed by applying background field removal techniques (Liu, 2011) to the phase of gradient-echo data. Background field variation also causes intravoxel dephasing, resulting in magnitude modulation of gradient-echo data and overestimation of R2* (Ordidge, 1994). Therefore, this additional R2*, named as background R2*, should be corrected when conducting χ-separation to prevent potential bias in χpara and χdia maps. In this study, we demonstrated that background R2* correction can reduce artifactual overestimation of χpara and χdia. Moreover, we examined the effect of background R2* correction under varying k-space filtering, confirming that background R2* correction leads to consistent χ-separation maps across different k-space filtering.

A healthy volunteer was scanned using Siemens 3 T MRI with a 3D multi-echo gradient-echo sequence and a 2D multi-echo spin-echo sequence. The effect of intravoxel field gradient on 3D multi-echo gradient-echo data was modelled using voxel spread function method (Yablonskiy, 2013). In this model, the modulated signal from a voxel by linearly varying intravoxel field can be expressed as multiplication with a shifted sinc function in k-space, where the shift is proportional to the intravoxel field gradient. The voxel spread function can be calculated by inverse Fourier transform of this shifted sinc function. When k-space data is filtered with a windowing function, the effect of windowing can be accommodated by multiplying the shifted sinc function with the windowing function.
The schematic flowchart of background R2* correction for χ-separation is presented in Fig. 1. First, the field gradient was estimated from the numerical gradient of combined phase of multi-echo gradient-echo data. The voxel spread function was calculated from the field gradient map. Using this voxel spread function, the magnitude modulation by the field gradient was estimated, and utilized to generate a background-corrected R2* map. For χ-separation, a tissue field map was prepared by removing background field from the combine phase map (Wu, 2012). The R2 map were derived from the multi-echo spin-echo data. Finally, χpara and χdia maps were acquired with χ-sepnet (Kim, 2022) utilizing the corrected or uncorrected R2*, R2, and tissue field maps as inputs.

In Fig. 2A, the effect of background R2* correction on χpara and χdia maps were examined at the regions close to air cavities. While uncorrected χpara and χdia maps exhibited overestimation at regions marked with cyan lines, corrected χpara and χdia values were in normal ranges, as confirmed by the χpara, χdia, and R2* profile, which was plotted along cyan lines. Overall, the χpara and χdia bias from background R2* at regions close to nasal, frontal, and tympanic cavities were properly reduced in the corrected χpara and χdia maps. In Fig. 2B, the same k-space data was reconstructed after Tukey-windowing with different low-pass level. By applying background R2* correction, the overestimation in χpara and χdia was corrected, providing more consistent χpara and χdia maps across different low-pass levels.

Background R2* correction for χ-separation successfully reduced overestimation in χpara and χdia maps. As MRI vendors provide reconstructed images at different low-pass levels, the background R2* differs vendor-to-vendor. Therefore, applying background R2* correction is crucial for consistent χ-separation maps across venders.

Exploratory Modeling and Artifact Removal ¹

Subcortical Structures

White Matter Anatomy, Fiber Pathways and Connectivity ²

Modeling

Myelin

STRUCTURAL MRI

Thalamus

WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC