Method for fast lipid reconstruction and removal processing in¹H MRSI of the brain

Abstract

Purpose To develop a new rapid spatial filtering method for lipid removal, fast lipid reconstruction and removal processing (FLIP), which selectively isolates and removes interfering lipid signals from outside the brain in a full‐FOV 2D MRSI and whole‐brain 3D echo planar spectroscopic imaging (EPSI). Theory and Methods FLIP uses regularized least‐squares regression based on spatial prior information from MRI to selectively remove lipid signals originating from the scalp and measure the brain metabolite signals with minimum cross contamination. FLIP is a noniterative approach, thus allowing a rapid processing speed, and uses only spatial information without any spectral priors. The performance of FLIP was compared with the Papoulis‐Gerchberg algorithm (PGA), Hankel singular value decomposition (HSVD), and fast image reconstruction with L2 regularization (L2). Results FLIP in both 2D and 3D MRSI resulted in consistent metabolite quantification in a greater number of voxels with less concentration variation than other algorithms, demonstrating effective and robust lipid‐removal performance. The percentage of voxels that met quality criteria with FLIP, PGA, HSVD, and L2 processing were 90%, 57%, 29%, and 42% in 2D MRSI, and 80%, 75%, 76%, and 74% in 3D EPSI, respectively. The quantification results of full‐FOV MRSI using FLIP were comparable to those of volume‐localized MRSI, while allowing significantly increased spatial coverage. FLIP performed the fastest in 2D MRSI. Conclusion FLIP is a new lipid‐removal algorithm that promises fast and effective lipid removal with improved volume coverage in MRSI.

Lipid removal in deuterium metabolic imaging (DMI) using spatial prior knowledge

“…Multiple methods incorporate prior knowledge on magnetic field heterogeneity from B 0 and/or B 1 maps into the SLIM processing pipeline (Bashir and Yablonskiy, 2006;Khalidov et al, 2007;Passeri et al, 2014;Adany et al, 2016). Other methods seek to optimize the k-space encoding scheme to minimize bleeding between ROIs (Von Kienlin and Mejia, 1991;Zhang et al, 2012), whereas another strategy rests on subdividing larger ROIs to decrease the heterogeneity across any one ROI (Adany et al, 2021;Dong and Hwang, 2006).…”

“…While pulse sequence modifications and hardware solutions can give excellent lipid suppression (Tkáč et al, 2021), the requirements for DMI are generally modest, making post-processing methods a logical choice while still retaining the simple DMI acquisition method. Many post-processing methods utilize MRI-based prior knowledge on the lipid spatial location to achieve lipid removal and include dual-density reconstruction (Hu et al, 1994;Metzger et al, 1999), data extrapolation (Haupt et al, 1996), L2 regularization (Bilgic et al, 2014), and spectral localization by imaging (SLIM; Hu et al, 1988) and its variants (Liang and Lauterbur, 1991;Von Kienlin and Mejia, 1991;Bashir and Yablonskiy, 2006;Khalidov et al, 2007;Zhang et al, 2012;Passeri et al, 2014;Adany et al, 2016Adany et al, , 2021.…”

Lipid Removal in Deuterium Metabolic Imaging (DMI) using Spatial Prior Knowledge

“…Multiple methods incorporate prior knowledge on magnetic field heterogeneity from B0 and/or B1 map into the SLIM processing pipeline (Bashir and Yablonskiy, 2006;Khalidov et al, 2007;Passeri et al, 2014;Adany et al, 2016). Other methods seek to optimize the k-space encoding scheme to minimize bleeding between ROIs (Von Kienlin and Mejia, 1991;Zhang et al, 2012), whereas another strategy rests on subdividing larger ROIs to decrease the heterogeneity across any one ROI (Adany et al, 2021;Dong and Hwang, 2006).…”

“…While pulse sequence modifications and hardware solutions can give excellent lipid suppression (Tkáč et al, 2021), the requirements for DMI are generally modest, making post-processing methods a logical choice while still retaining the simple DMI acquisition method. Many post-processing methods utilize MRI-based prior knowledge on the lipid spatial location to achieve lipid removal and include dual density reconstruction (Hu et al, 1994;Metzger et al, 1999), data extrapolation (Haupt et al, 1996), L2 regularization (Bilgic et al, 2014) and Spectroscopic Localization by Imaging (SLIM, (Hu et al, 1988)) and its variants (Liang and Lauterbur, 1991;Von Kienlin and Mejia, 1991;Bashir and Yablonskiy, 2006;Khalidov et al, 2007;Zhang et al, 2012;Passeri et al, 2014;Adany et al, 2016Adany et al, , 2021. Here we propose to use the SLIM algorithm (Hu et al, 1988) because it can (1) be applied to standard, 3D phase-encoded DMI without data acquisition modifications, (2) remove extracranial lipids without perturbing the brain metabolic profile and (3) be extended to provide regional brain signals from anatomy-matched compartments.…”