Use of multi-modal non-contrast MRI to predict functional outcomes after stroke: A study using DP-pCASL, DTI, NODDI, and MAP MRI

Julia Diamandi; Christian Raimondo; Keenan Piper; Joanna Roy; Stephanie Serva; Mahdi Alizadeh; Adam Flanders; Stavropoula Tjoumakaris; Reid Gooch; Pascal Jabbour; Robert Rosenwasser; Nikolaos Mouchtouris

doi:10.1016/j.nicl.2025.103742

Use of multi-modal non-contrast MRI to predict functional outcomes after stroke: A study using DP-pCASL, DTI, NODDI, and MAP MRI

Neuroimage Clin. 2025:45:103742. doi: 10.1016/j.nicl.2025.103742. Epub 2025 Jan 24.

Affiliations

¹ Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, United States.
² Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States.
³ Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, United States.
⁴ Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, United States. Electronic address: nikolaos.mouchtouris@jefferson.edu.

Abstract

Purpose: This study aims to assess whether water exchange rate (k_w), a surrogate for blood-brain barrier (BBB) permeability, is associated with functional outcomes in patients with acute ischemic stroke (AIS).

Methods: We studied 22 AIS patients enrolled from 1/2022 to 4/2024 who underwent multi-modal non-contrast imaging on a 3.0-Tesla scanner, including DP-pCASL, DTI, NODDI and MAP imaging. For each parametric map, the intensity and standard deviation (SD) were calculated for the infarcted region. The diffusion maps included were b0, Fractional Anisotropy (FA), Mean Diffusivity (MD), Intra-cellular Volume Fraction (ICVF), Free Water Fraction (FWF), and Orientation Dispersion Index (ODI), q-Space Mean Square Displacement (QMSD), Return-to-Axis Probability (RTAP), Return-to-Plane Probability (RTPP), Return-to-Origin Probability (RTOP), Propagator Anisotropy (PA), and non-Gaussianity (NG). The perfusion-based maps were cerebral blood flow (CBF), arterial transit time (ATT), and k_w. The outcome variable was modified Rankin Scale (mRS).

Results: Twenty-two patients were included. The average age was 69.5 ± 13.5, the mean NIHSS of 12.4 ± 7.7, and the median infarct of 25.7 (8.4-98.8) ml. Multivariable linear regression identified lower k_w (β = -0.029, p = 0.041), longer time to MRI (β = 0.012, p = 0.013) and larger stroke volume (β = 0.014, p = 0.006) as predictive of higher mRS. Higher CBF (β = 0.660, p = 0.003) and RTAP (β = 1.528, p = 0.010), and lower SD RTAP (β = -0.709, p = 0.016), RTPP (β = -2.132, p = 0.006), and NG (β = -1.036, p = 0.011) were identified as most predictive of k_w through multivariable linear regression analysis.

Conclusions: Lower k_w is predictive of higher mRS in patients with AIS. Increasing CBF and RTAP and lower SD RTAP, RTPP, and NG were correlated with higher k_w.

Keywords: Acute Ischemic Stroke (AIS); Blood-Brain Barrier (BBB); Diffusion Tensor Imaging (DTI); Mean Apparent Propagator (MAP) MRI; Neurite Orientation Dispersion and Density Imaging (NODDI); Water Exchange Rate (kw).

MeSH terms

Aged
Aged, 80 and over
Blood-Brain Barrier / diagnostic imaging
Diffusion Tensor Imaging / methods
Female
Humans
Ischemic Stroke* / diagnostic imaging
Ischemic Stroke* / physiopathology
Magnetic Resonance Imaging* / methods
Male
Middle Aged
Multimodal Imaging* / methods
Recovery of Function / physiology
Stroke* / diagnostic imaging
Stroke* / physiopathology