Off-resonant Encoded Analytical Parameter Quantification Using Multi-Dimensional Linearised Equations

Patent Abstract

A magnetic resonance imaging method is proposed for quantification of one or more system parameters The method comprises: performing (11, 12) a bSSFP data acquisition of an object of the system to obtain a plurality of image volumes, a respective image volume corresponding to an effective radio frequency phase increment; obtaining (13) voxel-wise a set of multi-dimensional bSSFP signal values for a set of voxels of the plurality of image volumes with a corresponding radio frequency phase increment; constructing (14) voxel-wise a set of multi-dimensional bSSFP profiles for the set of voxels from the set of multi- dimensional bSSFP signal values; applying (15) voxel-wise a mathematical transformation on the multi-dimensional bSSFP profiles to obtain a set of multi-dimensional characteristic coefficients or a set of approximated multi-dimensional characteristic coefficients for the set of voxels; using (16) voxel-wise the set of multi-dimensional characteristic coefficients or approximated multi-dimensional characteristic coefficients to determine a set of parameter bases for the set of voxels; applying (17) one or more analytical solution functions onto the set of parameter bases to quantify voxel-wise the one or more system parameters.

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This invention presents a method and associated computer program for analyzing magnetic resonance imaging (MRI) data—specifically using balanced steady-state free precession (bSSFP) sequences with phase-cycled RF pulses—to rapidly and analytically quantify a variety of system parameters (like relaxation times T1, T2, proton density, and magnetic field inhomogeneity) at each voxel of the scanned volume. The approach uses mathematical transformations (like Fourier transforms) on multidimensional bSSFP data to extract characteristic coefficients, enabling robust, high-speed, and highly accurate generation of quantitative parameter maps without reliance on slow and computationally intensive iterative or dictionary-based methods. The method is also capable of separating and quantifying parameters in both single- and multi-compartment systems, including challenging cases involving fat-water separation in tissues.

Use CasesContent extracted from patent full text and abstract with AI.

• Quantitative MRI for clinical diagnosis and research, providing rapid quantitative maps of tissue properties (T1, T2, proton density, B0 inhomogeneities).
• Early detection, diagnosis, and monitoring of neurological disorders such as multiple sclerosis, Alzheimer's, and Parkinson's diseases through precise tissue characterization.
• Liver, cardiac, or musculoskeletal imaging where tissue composition (like fat fraction) and relaxation parameters are important.
• Automated quantitative imaging in large-scale studies that demand speed, accuracy, and robustness.
• MRI post-processing tools/software for medical imaging centers and hospitals.
• Quantitative fat fraction mapping for disease diagnosis (e.g., nonalcoholic fatty liver disease, atherosclerosis).
• Development of new MRI scanner software or hardware integrating rapid quantitative mapping capabilities.

BenefitsContent extracted from patent full text and abstract with AI.

• Ultra-rapid and robust quantification of multiple MRI parameters, enabling much faster image processing compared to conventional iterative or dictionary-based methods.
• Higher precision and reproducibility: Offers quantitative maps that are highly consistent, reducing operator- or session-dependent variability.
• Comprehensive data utilization: Uses all encoded complex-valued information (magnitude and phase), increasing accuracy and information richness.
• Ability to handle complex tissue types and multi-compartment systems (e.g., water and fat), overcoming previous limitations in tissue separation and quantification.
• Enables generation of fully coregistered (spatially matched) quantitative maps for multiple parameters in a single workflow.
• Reduces computational demand, making integration feasible on clinical MRI systems and standard computing hardware.
• Potential to improve disease detection, tissue characterization, and patient management through enhanced imaging biomarkers.

Technical Classifications (CPCs)

Inventors & Applicants