Quantitative Susceptibility Mapping for Multiple Sclerosis Remyelination Monitoring
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Solution Overview
Problem
Conventional MRI struggles to accurately monitor remyelination in multiple sclerosis patients, particularly in regions behind the blood-brain barrier, due to challenges in sensitizing disease activities and therapeutic responses.
Innovation Solution
The implementation of quantitative susceptibility mapping (QSM) using multiecho complex magnetic resonance imaging data, incorporating deep neural networks and regularization terms to enhance susceptibility map quality, suppresses artifacts, and provides precise magnetic susceptibility values, enabling accurate remyelination monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI sequences (T1, T2, FLAIR) are used to monitor multiple sclerosis lesions, then lesion size and location can be identified, but the ability to sensitize disease activities and therapeutic responses behind the blood-brain barrier is insufficient
Solution Approach 1:
The patent employs quantitative susceptibility mapping (QSM) which changes the measurement parameter from conventional signal intensity to magnetic susceptibility values. This allows detection of iron and myelin changes behind the blood-brain barrier by measuring the magnetic field effects rather than direct signal changes, thereby improving disease activity detection sensitivity without requiring complex contrast agent protocols
Solution Approach 2:
The patent replaces the conventional MRI signal-based detection mechanism with a magnetic field-based detection mechanism. By measuring the magnetic susceptibility effects of iron and myelin on the local magnetic field, the system can detect therapeutic responses and disease activities that are invisible to conventional T1, T2, and FLAIR sequences
2Measurement precision
If quantitative susceptibility mapping is performed using gradient echo data, then magnetic susceptibility information can be obtained, but the inverse problem of nonlinear signal models creates challenging nonconvex optimization
Solution Approach 1:
The patent performs preliminary field estimation from the gradient echo phase data before attempting susceptibility inversion. This preliminary step creates an initial estimate that guides the subsequent optimization process, transforming the challenging nonconvex problem into a more manageable iterative refinement process with multiple regularization terms
Solution Approach 2:
The patent transforms the nonlinear signal model inversion problem into a linearized optimization problem by using the relationship between magnetic field and susceptibility. By formulating the cost function with data fidelity terms and regularization terms, the complex nonconvex optimization is converted into a structured convex optimization that can be efficiently solved
3Measurement precision
If multiecho complex MRI data is used for susceptibility mapping, then quantification accuracy improves, but data processing and reconstruction complexity increases
Solution Approach 1:
The patent merges multiple echo data into a unified susceptibility map by combining the information from different echo times. The multiecho complex data is processed together in a single optimization framework that simultaneously utilizes all echo information, improving quantification accuracy while avoiding the need for separate processing of each echo
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach results in higher-quality susceptibility maps that improve diagnostic accuracy and experimental reproducibility, allowing for the identification of remyelinated, chronic active, and chronic inactive lesions, facilitating better understanding and treatment evaluation in multiple sclerosis.
Implementation Method 1
QSM is able to unravel novel information related to the magnetic susceptibility (or simply referred as susceptibility), a physical property of underlying tissue that depends on magnetic chemical compositions
Implementation Method 2
The gradient echo (GRE) sequence is widely accessible and commonly used to acquire data for QSM. GRE data comprise magnitude information with T2* contrast and phase information
Data Source
AI summary
Quantitative susceptibility mapping methods, systems and computer-accessible medium include generating images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach. The tissue magnetism images is then used to monitor remyelination, such as remyelination in multiple sclerosis patients in response to therapy. Multiple sclerosis lesions defined on magnetic resonance imaging are further characterized on tissue magnetism images into hyperintense, isointense and hypointense parts for measuring remyelination. Thus, magnetic susceptibility information and other tissue properties associated with at least one structure are determined.