CIMRI Susceptibility Tomography for MRI Phase Deconvolution
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques, specifically T2*MRI, fail to provide an exact representation of magnetic susceptibility distributions due to nonlinearity and local averaging, leading to noisy and blurry phase images that are not directly useful for iron measurement, and existing solvers for susceptibility mapping face challenges such as large matrix problems, stripe artifacts, and energy shifts.
Innovation Solution
The development of a computed inverse magnetic resonance imaging (CIMRI) model using a 3D total-variation-regularized deconvolution method with split Bregman iteration for reconstructing magnetic susceptibility distributions from T2*MRI phase images, which involves calculating the fieldmap and susceptibility distribution, and implementing post-reconstruction filtering to reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If T2*MRI is used to detect magnetic susceptibility, then noninvasive 3D imaging is achieved, but the output image is not an exact representation of the susceptibility source due to nonlinearity and local averaging
Solution Approach 1:
The patent applies inverse imaging techniques to reverse the forward T2*MRI transformation. By solving the inverse problem through deconvolution algorithms, the system recovers the original susceptibility distribution from the distorted T2*MRI signal, effectively undoing the nonlinearity and local averaging effects that degraded measurement precision.
Solution Approach 2:
The patent replaces direct physical measurement with computational modeling. Instead of trying to directly measure susceptibility without distortion, the system uses computer-based deconvolution algorithms to mathematically reverse the imaging distortion, substituting physical measurement accuracy with computational correction.
2Measurement precision
If matrix inverse methods are used for susceptibility mapping, then susceptibility distribution can be recovered, but large matrix problems arise that are difficult to solve
Solution Approach 1:
The patent segments the large 3D imaging problem into smaller, manageable components by processing data in the frequency domain (k-space) rather than directly in the spatial domain. This transforms the large matrix inversion problem into smaller, more manageable operations that can be solved more efficiently.
Solution Approach 2:
The patent transforms the problem from the spatial domain to the frequency domain through Fourier transforms. This dimensional change converts the difficult spatial matrix inversion problem into easier frequency-domain operations, reducing computational complexity while maintaining measurement precision.
3Measurement precision
If 3D convolution transformation is applied during susceptibility magnetization, then magnetic field distribution is disturbed, but the fieldmap appears morphologically different from the χ source: textural, noisy, and blurry
Solution Approach 1:
The patent uses computational deconvolution algorithms to reverse the blurring and noise effects introduced by the 3D convolution transformation. By applying inverse filtering in the frequency domain, the system mathematically removes the harmful morphological distortions, restoring the fieldmap to its true representation.
Solution Approach 2:
The patent turns the harmful noise and blurriness introduced by the convolution transformation into a solvable mathematical problem. By recognizing these distortions as characteristic patterns, the system applies targeted deconvolution algorithms that use the very same convolution properties to reverse and eliminate the distortions.
4Measurement precision
If T2*MRI is used for iron measurement, then quantitative iron measurement is possible, but the output image suffers from non-negativity and edge-effect pitfalls
Solution Approach 1:
The patent applies inverse imaging to correct the non-negativity and edge effects inherent in T2*MRI. By reversing the forward imaging transformation through deconvolution, the system recovers the true susceptibility distribution that accurately represents iron concentration, eliminating the artifacts that would otherwise bias the measurement.
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 allows for accurate reconstruction of magnetic susceptibility maps, improving the fidelity of iron measurement and brain functional mapping by providing a more direct and truthful representation of internal iron distribution, capable of handling large volumes and reducing noise effectively.
Implementation Method 1
In magnetic susceptibility tomography, the internal distribution of magnetic susceptibility of an object is determined by applying various configurations of magnetic fields and measuring how the object perturbs them
Implementation Method 2
T2*MRI refers to the detection of transverse magnetization dephasing signal that is caused by a combination of spin-spin relaxation (T2 effect) and magnetic field inhomogeneity (T2′ effect)
Implementation Method 3
T2*MRI refers to the detection of transverse magnetization dephasing signal that is caused by a combination of spin-spin relaxation (T2 effect) and magnetic field inhomogeneity (T2′ effect)
Implementation Method 4
The development of a computed inverse magnetic resonance imaging (CIMRI) model using a 3D total-variation-regularized deconvolution method with split Bregman iteration for reconstructing magnetic susceptibility distributions from T2*MRI phase images
Implementation Method 5
The development of a computed inverse magnetic resonance imaging (CIMRI) model using a 3D total-variation-regularized deconvolution method with split Bregman iteration for reconstructing magnetic susceptibility distributions
Data Source
AI summary
Magnetic susceptibility is the physical property for T2*-weighted magnetic resonance imaging (T2*MRI). The invention relates to methods for reconstructing an internal distribution (3D map) of magnetic susceptibility values, χ (x,y,z), of an object, from 3D T2*MRI phase images, by using Computed Inverse Magnetic Resonance Imaging (CIMRI) tomography. The CIMRI technique solves the inverse problem of the 3D convolution by executing a 3D Total Variation (TV) regularized iterative convolution scheme, using a split Bregman iteration algorithm. The reconstruction of χ (x,y,z) can be designed for low-pass, band-pass, and high-pass features by using a convolution kernel that is modified from the standard dipole kernel. Multiple reconstructions can be implemented in parallel, and averaging the reconstructions can suppress noise. 4D dynamic magnetic susceptibility tomography can be implemented by reconstructing a 3D susceptibility volume from a 3D phase volume by performing 3D CIMRI magnetic susceptibility tomography at each snapshot time.