TY - JOUR
T1 - Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping
AU - Meng, Ziyu
AU - Guo, Rong
AU - Wang, Tianyao
AU - Bo, Bin
AU - Lin, Zengping
AU - Li, Yudu
AU - Zhao, Yibo
AU - Yu, Xin
AU - Lin, David J.
AU - Nachev, Parashkev
AU - Liang, Zhi Pei
AU - Li, Yao
N1 - This work was supported in part by the Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University under Grant 21TQ1400203, in part by the National Natural Science Foundation of China under Grant 81871083, in part by Shanghai Jiao Tong University Scientific and Technological Innovation Funds under Grant 2019QYA12, in part by the Key Program of Multidisciplinary Cross Research Foundation of Shanghai Jiao Tong University under Grant YG2021ZD28 and Grant YG2023ZD22, in part by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning. The work of Parashkev Nachev was supported in part by the Wellcome Trust under Grant 213038/Z/18/Z and in part by UCLH NIHR Biomedical Research Centre.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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 (0-24 h, n = 23) or acute (24 h-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.
AB - 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 (0-24 h, n = 23) or acute (24 h-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.
KW - Ischemic stroke
KW - T mapping
KW - magnetic resonance spectroscopic imaging
KW - stroke onset time
UR - https://www.scopus.com/pages/publications/85160260146
UR - https://www.scopus.com/pages/publications/85160260146#tab=citedBy
U2 - 10.1109/TBME.2023.3277546
DO - 10.1109/TBME.2023.3277546
M3 - Article
C2 - 37200119
AN - SCOPUS:85160260146
SN - 0018-9294
VL - 70
SP - 3147
EP - 3155
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 11
ER -