0023

Spatial variations of ³¹P metabolites within the liver measured with 3D ³¹P MRSI at 7T

Lieke van den Wildenberg¹, Ayhan Gursan¹, Leonard Seelen¹, Tijl van der Velden¹, Mark Gosselink¹, Martijn Froeling¹, Wybe van der Kemp¹, Dennis Klomp¹, and Jeanine Prompers¹
¹Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Liver, Spectroscopy

³¹P MRS can be used in liver disease diagnosis and treatment monitoring. However, it is unclear whether ³¹P metabolite concentrations differ across the healthy liver, which would be important to take into account. Using an integrated ³¹P whole-body transmit coil for a 7T MRI scanner in combination with a 16-channel body receive array, we aimed to assess spatial variations of ³¹P metabolites within the liver in healthy subjects. We showed that there is significant spatial heterogeneity of various ³¹P metabolites levels within the liver, with marked differences for the PME and PDE metabolites between the left and right lobe.

Introduction

Phosphorus magnetic resonance spectroscopy (³¹P MRS) is a non-invasive technique allowing assessment of hepatic energy metabolism and membrane turnover¹, which could potentially be used in liver disease diagnosis and treatment monitoring^2–5. However, it is unclear whether ³¹P metabolite concentrations differ across the healthy liver, which is important to take into account when assessing (the progression of) liver disease and monitoring therapy response with ³¹P MRS. Using an integrated ³¹P whole-body transmit coil for a 7T MRI scanner⁶ in combination with a 16-channel body receive array, ³¹P signals can be measured throughout the torso and abdomen with increased sensitivity. The aim of this study was to assess spatial variations of ³¹P metabolites within the liver in healthy subjects, using this setup.

Methods

Ten healthy volunteers (four females and six males, age 34±12 (range: 25-62) years, BMI 24±4.0kg/m²) were scanned in a supine position using a whole-body 7T MR system (Philips Healthcare, Best, NL) with an in-house designed ³¹P whole-body birdcage transmit coil (diameter 60cm, 120.6MHz⁶). ³¹P signals were received with a local body array, containing 16³¹P loop coils integrated with 8 fractionated ¹H dipole antennas^7,8. Subjects were scanned twice on the same day. Image-based B₀ shimming was performed and transversal and coronal T₁-weighted images were acquired. ³¹P spectra were acquired with a 3D MRSI sequence with Hamming weighted k-space sampling without respiratory gating, using a block pulse for excitation (carrier frequency set to phosphocreatine, B₁=6μT, duration=0.33ms for 12° flip angle). The following parameters were used: FOV=500(LR)×280(AP)×360(FH)mm³, nominal resolution=20mm isotropic, TR=60ms, acquisition delay=0.56ms, BW=5000Hz, NSA=20, acquisition time=22:37min. A liver mask was manually drawn on the transversal T₁-weighted images. ³¹P MRSI data were reconstructed in Matlab and metabolites were fitted with AMARES⁹ using OXSA^10,11. PCA-based denoising¹² was applied before Roemer channel combination¹³. Twelve metabolite signals were fitted with Lorentzian line shapes with equal line widths, with soft constraints for the chemical shifts and fixed J-couplings for the ATP doublets and triplets. For visualisation of the spatial distribution of metabolite levels within the liver, metabolite maps were created based on the liver masks. For further quantitative analysis of spatial variations of ³¹P metabolites, only voxels in the liver mask with a fitted PCr or PtdC amplitude smaller than the α-ATP amplitude (to filter out muscle- and gall bladder-contaminated voxels) were taken into account and this filtered liver mask was divided into three (non-overlapping) volumes of interest (VOI) based on the Couinaud classification of hepatic segments¹⁴: (1) segment II-IV, (2) segment V-VI, and (3) segment VII-VIII. Segments II-IV correspond to the left lobe of the liver and the right lobe is composed of segments V-VIII. Fitted metabolite signal amplitudes were expressed as percentage of the sum of all fitted signals and were averaged over all voxels within each segment-based VOI. Two-way ANOVA for repeated measures with Bonferroni post-hoc analyses was performed to assess significant differences between the scans and segment-based VOI’s.

Results

Figure 1 shows four slices of a transversal T₁-weighted MRI overlaid with the ³¹P MRSI grid and the masks for the different liver segment-based VOI’s, including ³¹P MR spectra in each slice. For all volunteers, on average 28±15 and 28±11 voxels were included in the mask for segment II-IV for scan 1 and scan 2, respectively, 34±15 and 30±11 voxels for segment V-VI, and 37±20 and 42±14 voxels for segment VII-VIII. Mean quantified metabolite levels were significantly different among the segment-based VOI’s for Pi, GPC, GPE, PC, NADH, UDPG, and PCr, all independent of the scan session (Figure 2). GPC was lower in segment II-IV compared to segment V-VI, and GPE was lower and PC was higher in segment II-IV compared to segments V-VI and VII-VIII, resulting in a lower PDE and higher PME/PDE in the left lobe (segments II-IV) compared to the right lobe (segments V-VIII). Figure 3 shows liver metabolite maps for 4 consecutive transversal slices in one subject. Whereas the α-ATP level is rather homogeneous throughout the liver, for PtdC a clear hotspot is observed in the most inferior slice, corresponding with the location of the gall bladder. For the GPE and PC levels, and the PME/PDE ratio, clear differences between the right and left liver lobes can be observed.

Discussion and conclusion

For various ³¹P metabolites, we observed spatial heterogeneity within the liver, which was consistent between repeated scans. Most significant differences in ³¹P metabolite levels were found for the PMEs and PDEs between the left lobe and right lobe of the liver. Interestingly, in ¹H MRI studies it has been shown that also the proton density fat fraction varies over the liver and that it is significantly higher in the right lobe than in the left lobe in subjects with NAFLD, obesity, or a high risk of developing type 2 diabetes, which was speculatively associated with differences in perfusion of and/or different contributions of mesenteric versus splenic blood supply to the right and left liver lobe^15–17. The observed spatial differences in ³¹P metabolites across the liver will be important to take into account when assessing (the progression of) liver disease and monitoring therapy response with ³¹P MRS.

Acknowledgements

H2020-FETopen:NICI

References

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Figures

Figure 1: Four slices of the transversal T₁-weighted MRI scan of one of the subjects, showing the ³¹P MRSI grid and the location of the voxels in the masks for the different liver segment-based VOI’s: segment II-IV (dark green), segment V-VI (yellow), and segment VII-VIII (light green). ³¹P MR spectra are shown for one liver voxel (red squares) in each slice. Spectra were apodized with 20 Hz and zero-filled to 512 points, only for visualization purposes. The annotated 12 metabolite signals (α-, β-, and γ-ATP, Pi, GPC, GPE, PC, PE, NADH, UDPG, PtdC, and PCr) were fitted with AMARES.

Figure 2: ³¹P metabolite quantification for the different liver segment-based VOI’s. Metabolite levels were averaged over all voxels within the liver segment-based VOI and are expressed as percentage of the sum of all fitted signals. β-ATP was not included, because of the limited bandwidth of the excitation pulse. Total PME and PDE levels and PME/PDE were calculated from the fit results of the individual PC, PE, GPC and GPE signals. SD = standard deviation. ^ap < 0.05 vs. segment II-IV independent of the scan session, ^b p < 0.05 vs. segment V-VI independent of the scan session.

Figure 3: Liver metabolite maps for α-ATP, PtdC, GPC, GPE, PC, PE, and PME/PDE for 4 consecutive transversal slices in one subject, overlaid on the transversal T₁-weighted MRI scan. The maximum value of each color bar is equal to the maximum value of the relative signal amplitudes of the specific metabolite in the shown slices. Metabolite maps were interpolated to the same spatial resolution as the T₁-weigthed image by using bilinear interpolation in the axial plane, i.e., the output pixel value is a weighted average of the pixels in the nearest 2-by-2 neighborhood.

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)

0023

DOI: https://doi.org/10.58530/2023/0023