Introduction

In vivo proton exchange rate (k_ex) imaging of human brain based on water direct saturation (DS) removed omega plot has been implemented for healthy subjects. ^{1, 2} In this study, at the first time, we attempt to perform k_ex MRI of ischemic stroke patients and to evaluate the potential value of k_ex imaging for detecting physiopathologic changes of brain tissues due to ischemia.

Methods

A total seventeen patients with ischemic stroke (12 men, 5 women, mean age 54 years, age range 32–81 years, duration of stroke onset from 2 to 18 days) were recruited for this study. All MRI examinations were performed on a 3T MRI system (GE Medical Systems, Discovery MR750, Waukesha, WI, USA) with a 32-channel head coil. Several routine MRI protocols included transverse T1 fluid-attenuated inversion recovery (T1-FLAIR), T2 fast spin echo (T2-FSE), T2-FLAIR, and diffusion weighted imaging (DWI) based on spin-echo echo-planar imaging sequence, which were used for localizing the locations of the infarct lesions. Z-spectral data were acquired with a single-shot fast low-angle shot (FLASH) sequence on a transverse section with the largest infarct region referencing to DWI. The parameters were as follows: TR/TE = 3000/22.6 ms, matrix size = 128×128, FOV = 240 mm× 240 mm, slice thickness = 5 mm, NEX = 2, CEST saturation power (B₁) = 1.5, 2.5 and 3.5 μT, saturation duration = 1500 , saturation offsets including 0 to ±5 ppm at an increment of 0.25 ppm, ±6, +15.6, and +39.1 ppm as the reference image, a total of 33 saturation offsets, and acquisition time of 3 min 18 s. After B₀ correction based on 1.5 μT Z-spectrum, the Z-spectra were fitted as a linear combination of multiple Lorentzian functions and water DS effect was estimated and removed for constructing pixel-wise omega plots, producing proton exchange rate maps of brain. k_ex between infarct and contralateral normal brain tissues were compared using a two tailed paired Student’s t-test. In addition, k_ex imaging were also compared to other conventional MRI methods including DWI, chemical exchange saturation transfer (CEST) and magnetization transfer (MT) computed at +3.5 ppm from the water resonance. All raw data processing and analysis were performed using home-built programs developed in MATLAB.

Results

Compared with contralateral normal tissues, k_ex in lesion regions were found to increase significantly (893±52 vs. 739±34 s⁻¹, P < 0.001). A representative k_ex image for a stroke brain was shown in Figure 1 in which the infarct exhibited apparently higher k_ex compared to the contralateral tissues. Furthermore, k_ex maps were also found to be different from conventional contrasts from DWI, CEST and MT MRI. k_ex MRI typically showed larger lesion areas than DWI in most patients, which was demonstrated in Figure 2. While k_ex MRI provided us a physical parameter map, CEST contrast map for the definition of stroke lesions was highly dependent on the saturation power B₁. On the other hand, MT map could hardly detect stroke lesions at any B₁ (Figure 3).

Discussion

In the study, k_ex MRI detected ischemic lesions with elevated signal over the contralateral brain tissues. As we know, k_ex may be contributed mainly by pH, temperature, or reactive oxygen species (ROS). ³ Given that tissue acidosis induced pH reduction in stroke can only lead to reduced k_ex and brain temperature is generally well controlled and stable, we hypothesize that the increased k_ex is predominately due to the elevated ROS production. The increase of metabolites with fast exchanging protons may also increase the observed tissue k_ex. However, this is not the case in stroke. Furthermore, our results indicated that k_ex presents different characterization and definition of stroke than conventional DWI, CEST, and MT MRI contrasts.

Conclusion

As a novel, independent, and noninvasive molecular MRI technique, k_ex MRI at 3T may serve as a potential surrogate biomarker to reflect the metabolic changes, better understand the evolution, and facilitate the treatment of ischemic stroke.

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. 81570462, No. 81730049 and No. 81801666), the NIH grants R21EB023516, R01AG061114, and R21AG053876.