Cumulant expansion framework for internal gradient distributions tensors

Magnetic resonance imaging is a powerful, non invasive tool for medical diagnosis. The low sensitivity for detecting the nuclear spin signals, typically limits the image resolution to several tens of micrometers in preclinical systems and millimeters in clinical scanners. Other sources of informatio...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Pedraza Perez, Leonardo A, Alvarez, Gonzalo A
Format: Article
Language:English
Subjects:
Biomarkers
Image resolution
Magnetic permeability
Magnetic resonance imaging
Mathematical analysis
Micrometers
Morphology
Nuclear spin
Porous media
Tensors
Tissues
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Summary:Magnetic resonance imaging is a powerful, non invasive tool for medical diagnosis. The low sensitivity for detecting the nuclear spin signals, typically limits the image resolution to several tens of micrometers in preclinical systems and millimeters in clinical scanners. Other sources of information, derived from diffusion processes of intrinsic molecules as water in the tissues, allow getting morphological information at micrometric and submicrometric scales as potential biomarkers of several pathologies. Here we consider extracting this morphological information by probing the distribution of internal magnetic field gradients induced by the heterogeneous magnetic susceptibility of the medium. We use a cumulant expansion to derive the dephasing on the spin signal induced by the molecules that explore these internal gradients while diffuse. Based on the cumulant expansion, we define internal gradient distributions tensors (IGDT) and propose modulating gradient spin echo sequences to probe them. These IGDT contain microstructural morphological information that characterize porous media and biological tissues. We evaluate the IGDT effects on the magnetization decay with typical conditions of brain tissue and show their effects can be experimentally observed. Our results thus provide a framework for exploiting IGDT as quantitative diagnostic tools.
ISSN:2331-8422
DOI:10.48550/arxiv.2304.02065