Presentation + Paper
15 February 2021 A linear systems description of multi-compartment pulmonary 129Xe magnetic resonance imaging methods
Author Affiliations +
Abstract
Objective: We aimed to develop a linear-systems model to evaluate the impact of mathematical assumptions related to multi-compartment 129Xe MRI methods. We employed a linear-systems approach to fill a knowledge gap associated with a single-point Dixon approach currently used for magnetization calculations and physiologic estimates. Methods: We generated a linear-systems model for magnetization calculations needed to generate multi-compartment 129Xe MRI data. 1Lung tissue and red-blood-cell compartments were isolated by acquiring data 90° out of phase and aligned with perpendicular quadrature channels. Results: In our linear-systems model, a single-lobe sinc-pulse in the time domain was used to excite 129Xe atoms in the tissue and red blood-cell compartments. We assumed that T2* exponential decay in the time domain was equivalent to convolution with a complex Lorentzian function in the frequency domain, and the gradient echo envelope was equivalent to convolution with a rectangular pulse with phase shifting. A rectangular window function and Dirac comb sampling in the time domain modeled analog-to-digital recording. Fast Fourier transforms were modelled by Dirac combs in both time and frequency domains. To account for non-uniform sampling, k-space was re-sampled using a unique sampling function convolution followed by Cartesian sampling. Phase inhomogeneities were corrected using reference gas and spectroscopy data. Conclusion: The proposed linear-system analysis provides a framework for modelling decay constants, peak overlap, and magnetization evolution common in multi-compartment 129Xe MRI. Understanding the spectral properties of 129Xe MRI will provide a way to identify novel compartments and their abnormalities in diverse pulmonary diseases.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander M. Matheson, Grace Parraga, and Ian A. Cunningham "A linear systems description of multi-compartment pulmonary 129Xe magnetic resonance imaging methods", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000H (15 February 2021); https://doi.org/10.1117/12.2580947
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KEYWORDS
Magnetic resonance imaging

Imaging systems

Convolution

Data modeling

Mathematical modeling

Tissues

Lung

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