Meng, Ziyu;
Guo, Rong;
Wang, Tianyao;
Bo, Bin;
Lin, Zengping;
Li, Yudu;
Zhao, Yibo;
... Li, Yao; + view all
(2023)
Prediction of Stroke Onset Time with Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.
IEEE Transactions on Biomedical Engineering
pp. 1-9.
10.1109/TBME.2023.3277546.
(In press).
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Abstract
OBJECTIVE: The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS: Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0×3.0×3.0 mm3) and quantitative T2 values (1.9×1.9×3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24h, n = 23) or acute (24h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS: In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p<0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p<0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION: The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE: Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.
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