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Application of a stretched-exponential model for morphometric analysis of accelerated diffusion-weighted 129Xe MRI of the rat lung

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Abstract

Objective

Diffusion-weighted, hyperpolarized 129Xe MRI is useful for the characterization of microstructural changes in the lung. A stretched exponential model was proposed for morphometric extraction of the mean chord length (Lm) from diffusion-weighted data. The stretched exponential model enables accelerated mapping of Lm in a single-breathhold using compressed sensing. Our purpose was to compare Lm maps obtained from stretched-exponential model analysis of accelerated versus unaccelerated diffusion-weighted 129Xe MRI data obtained from healthy/injured rat lungs.

Material and methods

Lm maps were generated using a stretched-exponential model analysis of previously acquired fully sampled diffusion-weighted 129Xe rat data (b values = 0 … 110 s/cm2) and compared to Lm maps generated from retrospectively undersampled data simulating acceleration factors of 7/10. The data included four control rats and five rats receiving whole-lung irradiation to mimic radiation-induced lung injury. Mean Lm obtained from the accelerated/unaccelerated maps were compared to histological mean linear intercept.

Results

Accelerated Lm estimates were similar to unaccelerated Lm estimates in all rats, and similar to those previously reported (< 12% different). Lm was significantly reduced (p < 0.001) in the irradiated rat cohort (90 ± 20 µm/90 ± 20 µm) compared to the control rats (110 ± 20 µm/100 ± 15 µm) and agreed well with histological mean linear intercept.

Discussion

Accelerated mapping of Lm using a stretched-exponential model analysis is feasible, accurate and agrees with histological mean linear intercept. Acceleration reduces scan time, thus should be considered for the characterization of lung microstructural changes in humans where breath-hold duration is short.

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Acknowledgements

This work was funded by the Canadian Institutes for Health Research (Operating grant MOP-123431), Ontario Research Fund (Ontario Preclinical Imaging Consortium) and the Natural Sciences and Engineering Research Council, and the Alpha-1 Foundation (USA). We thank J. F. P. J. Abascal for providing the MatLab code for image reconstruction.

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Authors and Affiliations

  1. Department of Physics and Astronomy, Western University, London, ON, Canada

    Alexei V. Ouriadov, Matthew S. Fox & Hacene Serrai

  2. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada

    Alexei V. Ouriadov & Matthew S. Fox

  3. Translational Medicine Program, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada

    Andras A. Lindenmaier, Elaine Stirrat & Giles Santyr

  4. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    Andras A. Lindenmaier & Giles Santyr

  5. School of Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada

    Alexei V. Ouriadov

  6. The University of Western Ontario, 1151 Richmond Street, London, ON, N6A 3K7, Canada

    Alexei V. Ouriadov

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Correspondence to Alexei V. Ouriadov.

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10334_2020_860_MOESM1_ESM.tif

Supplementary material 3 (TIF 53 kb) Supporting Figure S1: Relationships for mean linear intercept (cylinder model) with mean diffusion length specific to airway (stretched exponential model). Relationship for LmD = mean diffusion length with Lm and histological mean-linear-intercept for irradiated (solid squares) and control (solid circles) (R = 0.94; y = 1.8x – 150 µm; p < 0.001) rats. Open squares and open circles show histological mean-linear-intercept estimates obtained for irradiated (I4 and I5) and control (C3 and C4) animals (Table 1). Histological data (MRI-based LmD vs histological mean-linear-intercept) are shown for demonstration purposes only. MLI = histological mean linear intercept

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Ouriadov, A.V., Fox, M.S., Lindenmaier, A.A. et al. Application of a stretched-exponential model for morphometric analysis of accelerated diffusion-weighted 129Xe MRI of the rat lung. Magn Reson Mater Phy 34, 73–84 (2021). https://doi.org/10.1007/s10334-020-00860-6

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