Poster No:
1616
Submission Type:
Abstract Submission
Authors:
Astrid Cancino1,2, Pablo Muñoz3, Pablo Cox4,5, Lilian Acevedo4, Sebastian Castillo4, Aldo Letelier4, Sebastian Espinoza6, Alejandro Veloz7, Maria Rodriguez-Fernandez8,9, Stéren Chabert10,9
Institutions:
1Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 2PhD Program in Sciences and Engineering for Health. Universidad de Valparaíso, Valparaíso, Chile, 3Center for Applied Neurological Sciences, Faculty of Medicine, Universidad de Valparaíso, Valparaíso, Chile, 4Hospital Carlos Van Buren, Valparaíso, Chile, 5Radiology Department, Universidad de Valparaiso, Valparaíso, Chile, 6Universidad de Valparaíso, Valparaíso, Chile, 7School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso , Chile, 8Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 9Millenium Institute for Intelligent Healthcare Engineering iHealth, Santiago, Chile, 10School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile
First Author:
Astrid Cancino
Millennium Institute for Intelligent Healthcare Engineering, iHEALTH|PhD Program in Sciences and Engineering for Health. Universidad de Valparaíso
Santiago, Chile|Valparaíso, Chile
Co-Author(s):
Pablo Muñoz
Center for Applied Neurological Sciences, Faculty of Medicine, Universidad de Valparaíso
Valparaíso, Chile
Pablo Cox
Hospital Carlos Van Buren|Radiology Department, Universidad de Valparaiso
Valparaíso, Chile|Valparaíso, Chile
Alejandro Veloz
School of Biomedical Engineering, Universidad de Valparaíso
Valparaíso , Chile
Maria Rodriguez-Fernandez
Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile|Millenium Institute for Intelligent Healthcare Engineering iHealth
Santiago, Chile|Santiago, Chile
Stéren Chabert
School of Biomedical Engineering, Universidad de Valparaíso|Millenium Institute for Intelligent Healthcare Engineering iHealth
Valparaíso, Chile|Santiago, Chile
Introduction:
An ischemic stroke is a perfusion impairment, and it ranks as the second-leading cause of death globally and the third-leading cause when death and disability are combined (Feigin 2022). The imaging management of stroke involves the gold standard diffusion-weighted imaging (DWI) and/or perfusion MRI with or without contrast agents (Powers 2018). Intravoxel Incoherent motion (IVIM) explores the biexponential decay of the DWI signal obtained with multiple b-values. The pseudo-perfusion (D*) and the free water diffusion of free water (D) coefficients have been related to the microperfusion (Chabert 2020). IVIM has been suggested as an alternative technique for stroke assessment (Falk 2019 ; Pavila 2023). Cerebrovascular reactivity (CVR) is traditionally evaluated with a CO2 challenge to induce vasodilation (Gupta 2012). In the past year, an alternative method has been proposed for measuring CVR (Yang 2007) in a resting state fMRI (Frank 2023). These two MRI methods could provide a better understanding of the microvascular status during ongoing ischemia and their correlation with motor outcomes six months later
Methods:
23 volunteers enrolled with an ischemic stroke in the first 48 hours. Indication of thrombolysis or thrombectomy was an exclusion criterion. This study received full approval from the local ethics committee. All patients, or legal representatives, gave informed consent to participate. The severity of symptoms was assessed by NIHSS. The fMRI was acquired in a 1.5 Tesla. We used the task residual fMRI to explore the background connectivity as a resting state (rs) signal to generate the ALFF mapping as an approximation of CVR. DWI with 16 b values was used for the IVIM parameters fitting according to the biexponential model in two steps (Chabert 2020). All The analyses were undertaken in Regions of Interest (ROI) from the ischemic ROI and the contralateral non-ischemic ROI. Clinical follow-up at 6 months was realized with clinical scales: Barthel Index and Functional Independence Measure (FIM) for motor and functional Independence performance. The clinical data, CVR, and IVIM (D and D*) parameters were averaged over all patients. The ischemic and non-ischemic ROI were reported as mean ± standard deviation. The paired student t-test was used to compare the mean or U Mann Whitney in non-normal distribution. The Pearson correlation analysis was performed for all the subjects to explore relationships between the MRI parameters and clinical assessment
Results:
The mean age of participants was 66.7 years (±12.8), with 21.7% (n=5) being female. The median NIHSS score at admission was 4 (1-21) The DWI lesions at admission was 12.9 ml (± 28.4), with a median volume of 1.85 ml. The CVR values in ischemic and non ischemic do not present differences in the mean values. A significant difference for the D (Z = 3.7, p < .001) and D*values (W+ = 131, p =0 .048). in ischemic and non ischemic ROI. Qualitative in Figure 1.B Dmaps and D*maps of 3 participants., the reduction of D values in the ischemic region is evident.
The ischemic CVR value correlates with ischemic volume -0.6 (0.03); The ischemic D value correlates with the Delta CVR (non ischemic – ischemic) 0.78 (0.005). Concerning the motor outcome, the ischemic D* value correlated with FIM outcome -0.71 (0.006)

·Characterization of variables of interest. Vol.isch = Ischemic volume; NIHSS = National Institutes of Health Stroke Scale. Isch_CVR = Ischemic CVR value in ROI ; Non_isch_CVR = Non ischemic CVR value

·A. Example of DWI at b=1000 to identify the ischemic ROI and CVR maps, first participant (P25) has decreased CVR value and the third participant (P11) has increased CVR value. B. Maps of diffusion co
Conclusions:
This work shows a promising approach as it provides a quantitative measure of vascular flow dysfunction and establishes a strong correlation with motor outcomes six months later. This vascular impairment is supported by data in the literature. However, the main limitation of this study was the heterogeneous presentation of the stroke, along with the loss of data due to technical issues. More questions are still open and involve the ischemic neuroinflammatory cascade and its impact on the hemodynamic response function.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Motor Behavior:
Motor Behavior Other
Keywords:
Blood
Cerebrovascular Disease
FUNCTIONAL MRI
Motor
Other - IVIM ; CVR ; Stroke
1|2Indicates the priority used for review
Provide references using author date format
1. Chabert, S., (2020). Impact of b-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects. Magnetic resonance in medical sciences: MRMS: an official journal of Japan Society of Magnetic Resonance in Medicine, 19(3), 216–226
2. Falk, D.A.,(2019) 'Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging—a comprehensive overview'. Insights Imaging. 10:84.
3. Frank, L.E., (2023), Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs. Brain Behavior. 2023 Jun;13(6):e3015.
4. Feigin, V. L. (2022), World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. International journal of stroke: official journal of the International Stroke Society, 17(1), 18–29.
Gupta, A., 'Cerebrovascular reserve and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis. ' Stroke 2012;43:2884-2891.
6. Pavilla, A., (2023). Intravoxel incoherent motion and diffusion kurtosis imaging at 3T MRI: Application to ischemic stroke. Magnetic resonance imaging, 99, 73–80.
Powers, W.J. (2018), ' Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association ' . Stroke. 49:e46–e110.
Yang, H. (2007), ' The amplitude of low-frequency fluctuation within visual areas revealed by resting-state functional MRI ', Long XY, Yang Y, Yan H, Zhu CZ, Zhou XP, Zang YF, Gong QY. Neuroimage. May 15;36(1):144-52.