Dynamic glucose enhanced imaging using direct water saturation

Abstract

Purpose: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based linewidth ( $LW$ ) broadening of the direct water saturation (DS) curve of the water saturation spectrum (Z-spectrum) during and after glucose infusion (DS-DGE MRI).

Methods: To estimate the glucose-infusion-induced $LW$ changes ( $\Delta LW$ ), Bloch-McConnell simulations were performed for normoglycemia and hyperglycemia in blood, gray matter (GM), white matter (WM), CSF, and malignant tumor tissue. Whole-brain DS-DGE imaging was implemented at 3 T using dynamic Z-spectral acquisitions (1.2 s per offset frequency, 38 s per spectrum) and assessed on four brain tumor patients using infusion of 35 g of D-glucose. To assess $\Delta LW$ , a deep learning-based Lorentzian fitting approach was used on voxel-based DS spectra acquired before, during, and post-infusion. Area-under-the-curve ( $AUC$ ) images, obtained from the dynamic $\Delta LW$ time curves, were compared qualitatively to perfusion-weighted imaging parametric maps.

Results: In simulations, $\Delta LW$ was 1.3%, 0.30%, 0.29/0.34%, 7.5%, and 13% in arterial blood, venous blood, GM/WM, malignant tumor tissue, and CSF, respectively. In vivo, $\Delta LW$ was approximately 1% in GM/WM, 5% to 20% for different tumor types, and 40% in CSF. The resulting DS-DGE $AUC$ maps clearly outlined lesion areas.

Conclusions: DS-DGE MRI is highly promising for assessing D-glucose uptake. Initial results in brain tumor patients show high-quality $AUC$ maps of glucose-induced line broadening and DGE-based lesion enhancement similar and/or complementary to perfusion-weighted imaging.

Keywords: CEST; Z-spectra; direct saturation (DS); dynamic glucose enhanced (DGE) MRI; glucoCEST.

FIGURE 1

Normoglycemic ($C_p^a=6.15\text{mM}$) and hyperglycemic ($C_p^a=19.8\text{mM}$) simulated Z-spectra for tissue compartments (left) and total tissue (right). The lower row shows a zoomed-in view comparing the Z-spectral intensities around half-maximum for normoglycemia and hyperglycemia. Only Z-spectra with a sufficiently large change are visualized for tissue compartments (lower left). Saturation parameters: $B_{1\text{peak}}=0.5\mu \text{T}$, 10 consecutive 50-ms sinc-Gauss pulses for $t_{\text{sat}}=0.5\text{s}(\text{TR}=1.2\text{s})$.

FIGURE 2

Patient with recurrent IDH-wildtype glioblastoma showing thin Gd-enhancement around the resection cavity. (Left) $\Delta LW$ maps during the scan (averaged over a period of 76 s corresponding to two $\Delta LW$ images). (Right top) Anatomical images (${\text{Gd-T}}_1\text{w}$, FLAIR), together with parametric maps from DCE MRI ($K^{\text{trans}},V_{\text{e}}$), DSC MRI (corr. CBV, K2) and DS-DGE MRI ($AUC$ grayscale and color-coded). (Right bottom) Graph of linewidth change versus time obtained from regions of interest (ROIs) placed in the DS-DGE peri-cavity infiltrative tumor region, located anterior to the cavity, and ventricle (purple diamonds and blue squares, respectively). The ROIs are overlayed on the DS-DGE MRI $AUC$ map located in the graph as purple and blue areas, respectively. To visualize the trend in glucose uptake, $\Delta LW\left(t\right)$ curves were temporally smoothed with a 3-point moving average (purple and blue lines). $\Delta LW$, glucose-infusion-induced $LW$ change; $AUC$, area-under-curve; corr. $CBV$, corrected cerebral blood volume; $DCE$, dynamic contrast enhanced; $DSC$, dynamic susceptibility contrast; $DS-DGE$, direct water saturation-dynamic glucose enhanced; $\mathit{\text{FLAIR}}$, fluid-attenuated inversion recovery; $Gd$, gadolinium; $K2$, leakage; $K^{\text{trans}}$, volume transfer constant; $LW$, linewidth, ${\text{T}}_1\text{w},T_1$ weighted; $V_{\text{e}}$, interstitial volume.

FIGURE 3

(Left) Normoglycemic and hyperglycemic experimental Z-spectra from the glioblastoma patient in Figure 2 and the corresponding DL Lorentzian fits. (Right) A zoomed-in view demonstrating the linewidth difference between normoglycemic and hyperglycemic experimental Z-spectra. ROI locations are displayed in the ${\text{Gd-T}}_1\text{w}$ image and DS-DGE $AUC$ map. $AUC$, area-under-curve; $DS-DGE$, direct water saturation-dynamic glucose enhanced; $Gd$, gadolinium; $\mathit{\text{hcgl}}$, hyperglycemic; $ngl$, normoglycemic; ${\text{T}}_1\text{w},T_1$ weighted.

FIGURE 4

Patient with grade 2 IDH-mutated astrocytoma. Anatomical images (${\text{Gd-T}}_1\text{w}$, FLAIR) together with corr. $CBV,K2,K^{\text{trans}},V_{\text{e}}$, and DS-DGE MRI maps ($AUC$ in both grayscale and color-coded). Color-coded $AUC$ calculated from the infusion block only is also shown. A DS-DGE $AUC$ map overlayed on fused ${\text{Gd-T}}_1\text{w}$ /FLAIR is shown for reference. Graph of linewidth change versus time obtained from region of interest (ROI) placed in the DS-DGE contrast-enhanced area (purple overlayed on DS-DGE AUC map). To visualize the trend in glucose uptake, $\Delta LW\left(t\right)$ curves (purple dots) were temporally smoothed with a 3-point moving average (purple line). $\Delta LW$, glucose-infusion-induced $LW$ change; $AUC$, area-under-curve; corr. $CBV$, corrected cerebral blood volume; $DS-DGE$, direct water saturation-dynamic glucose enhanced; $\mathit{\text{FLAIR}}$, fluid-attenuated inversion recovery; $Gd$, gadolinium; $K2$, leakage; $K^{\text{trans}}$, volume transfer constant; $LW$, linewidth; ${\text{T}}_1\text{w},T_1$ weighted; $V_{\text{e}}$, interstitial volume.

FIGURE 5

Patient with a grade 2 IDH-mutated astrocytoma. Anatomical images (${\text{Gd-T}}_1\text{w}$, FLAIR) together with parametric maps from DCE MRI ($K^{\text{trans}},V_{\text{e}}$), DSC MRI (uncorr. $CBV$, corr. $CBV,K2$) and DS-DGE MRI (color-coded $AUC$) from four slices. $AUC$, area-under-curve; corr. $CBV$, corrected cerebral blood volume; $DCE$, dynamic contrast enhanced; $DSC$, dynamic susceptibility contrast; $DS-DGE$, direct water saturation-dynamic glucose enhanced; $\mathit{\text{FLAIR}}$, fluid-attenuated inversion recovery; $Gd$, gadolinium; $K2$, leakage; $K^{\text{trans}}$, volume transfer constant; ${\text{T}}_1\text{w},T_1$ weighted; uncorr. $CBV$, uncorrected cerebral blood volume; $V_{\text{e}}$, interstitial volume.

FIGURE 6

Patient with brain metastasis from anaplastic lymphoma kinase-mutated non–small-cell lung cancer. Anatomical images (${\text{Gd-T}}_1\text{w}$, FLAIR) together with $K^{\text{trans}},V_{\text{e}}$, uncorr. $CBV$, corr. $CBV$, and DS-DGE MRI maps ($LW$ map from third dynamic, $AUC$ maps in both grayscale and color-coded). A color-coded $AUC$ calculated from the infusion block only is also shown. A graph of linewidth change versus time obtained from ROIs placed in the DS-DGE contrast-enhanced area and contralateral frontal WM is shown to the right (purple dots and orange diamonds, respectively). Regions of interest (ROIs) are overlayed on the DS-DGE MRI $AUC$ map in the graph as purple and orange areas, respectively. To visualize the trend in glucose uptake, $\Delta LW\left(t\right)$ curves were temporally smoothed with a three-point moving average (purple and orange lines). $\Delta LW$, glucose-infusion-induced $LW$ change; $AUC$, area-under-curve; corr. $CBV$, corrected cerebral blood volume; $DS-DGE$, direct water saturation-dynamic glucose enhanced; $\mathit{\text{FLAIR}}$, fluid-attenuated inversion recovery; Gd, gadolinium; $K^{\text{trans}}$, volume transfer constant; $LW$, linewidth; uncorr. $CBV$, uncorrected cerebral blood volume; ${\text{T}}_1\text{w},T_1$ weighted; $V_{\text{e}}$, interstitial volume.