In vivo characterization of brain ultrashort-T2 components

Abstract

Purpose: Recent nuclear magnetic resonance and MRI studies have measured a fast-relaxing signal component with T2∗<1 ms in white matter and myelin extracts. In ex vivo studies, evidence suggests that a large fraction of this component directly arises from bound protons in the myelin phospholipid membranes. Based on these results, this ultrashort-T₂ component in nervous tissue is a new potential imaging biomarker of myelination, which plays a critical role in neuronal signal conduction across the brain and loss or degradation of myelin is a key feature of many neurological disorders. The goal of this work was to characterize the relaxation times and frequency shifts of ultrashort-T₂ components in the human brain.

Methods: This required development of an ultrashort echo time relaxometry acquisition strategy and fitting procedure for robust measurements in the presence of ultrashort T2∗ relaxation times and large frequency shifts.

Results: We measured an ultrashort-T₂ component in healthy volunteers with a median T2∗ between 0.5-0.7 ms at 3T and 0.2-0.3 ms at 7T as well as an approximately -3 ppm frequency shift from water.

Conclusion: To our knowledge, this is the first time a chemical shift of the ultrashort-T₂ brain component has been measured in vivo. This chemical shift, at around 1.7 ppm, is similar to the primary resonance of most lipids, indicating that much of the ultrashort-T₂ component observed in vivo arises from bound protons in the myelin phospholipid membranes. Magn Reson Med 80:726-735, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Keywords: myelin imaging; myelin membranes; relaxometry; ultrashort echo time MRI; ultrashort-T2.

Figure 1

Ultrashort echo time relaxometry acquisition strategy: (a,b) A 3D radial pulse sequence supporting ultrashort echo times (UTEs) was used, with variable echo times, TE_i. (c) We implemented sequential and random acquisition order strategies in which all TE_i for an individual radial spoke were acquired together. This was done to minimize potential motion artifacts.

Figure 2

ROI locations used for the the fit results in 4 and analysis in Table 1, overlaid on the shortest TE images. Purple corresponds to the posterior corpus callosum (pCC), light blue to mixed white matter (mixed WM) and dark blue to mixed gray matter (mixed GM). Note the latter ROIs are mixed, meaning they are primarily WM or GM but maybe include contributions from other tissue types including CSF.

Figure 3

Water sphere phantom results demonstrating the importance of randomized TE ordering for accurate relaxometry. The acquisition strategy is shown in Fig. 1. The sequential TE ordering has unexpected signal fluctuations, even at the same TE (red arrows), whereas the random TE ordered data better represents the expected mono-exponential decay.

Figure 4

Fit results at 3T and 7T for the same volunteer in several ROI locations identified in Fig. 2. The data shown was zero-order phase corrected and demodulated based on the estimated field map for these plots to illustrate the complex-valued signal oscillations. Fits to Eq. 3 with N = 1, 2, 3 components are shown as well as the fitting resiudals.

Figure 5

Spatial characteristics of the ultrashort- and long- $T_2^{\ast }$ component maps using water- and fat-frequency reconstructions. The UTE images and long- $T_2^{\ast }$ components show more clearly defined anatomical features with a water-frequency reconstruction, while the ultrashort- $T_2^{\ast }$ component is much more clearly defined with a fat-frequency reconstruction based fitting. The yellow arrows show apparent signal loss near the skull with the water-frequency reconstructions. The spatial blurring is exacerbated at 7T compared to 3T. At 7T, the fitting of the ultrashort- $T_2^{\ast }$ component failed near the frontal sinus as shown in the top of these images.

Figure 6

Representative UTE images and parameter maps from a two-component fit at 3T. The parameter maps are only shown for regions with AIC values less than −260. The ultrashort- $T_2^{\ast }$ components were calculated based on the fat-frequency reconstruction. The ultrashort- $T_2^{\ast }$ component frequency offset, Δf₁ − Δf₂, includes correction for B0 inhomogeneity. The AIC maps show the fitting performed worse near the sinuses due to susceptibility shifts and in the cerebellum. Additional maps resulting from the fitting are shown in Supporting Fig. S2.

Figure 7

Representative UTE images and parameter maps from a two-component fit at 7T. The parameter maps are only shown for regions with AIC values less than − 235. The ultrashort- $T_2^{\ast }$ components were calculated based on the fat-frequency reconstruction. The ultrashort- $T_2^{\ast }$ component frequency offset, Δf₁ − Δf₂, includes correction for B0 inhomogeneity. The AIC maps show the fitting performed worse near the sinuses due to susceptibility shifts and near the skull due to contamination from skull lipid signals. The AIC values were higher at 7T compared to 3T (Fig. 6). Additional maps resulting from the fitting are shown in Supporting Fig. S2.

Figure 8

Whole-brain histograms of the $T_2^{\ast }$ and frequency offset, corrected for B0 inhomogeneity, of the ultrashort- $T_2^{\ast }$ component at 3T and 7T using a two-compartment signal model. The two volunteers shown approximately represent the range of values observed, and histograms for all 5 volunteers are shown in Supporting Fig. S3. The dashed lines indicate the median values across the entire brain but excluding regions of poor fitting based on the AIC with the same criteria as in Figs. 6 and 7.