Parallel MRI Coil Sensitivity Reconstruction for Faster Scans
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Solution Overview
Problem
Characterization of physical properties using magnetic resonance techniques is time-consuming, costly, and user-unfriendly due to long MR scan times and confining magnetic environments, limiting the throughput and user experience.
Innovation Solution
A computational technique that determines coil sensitivities and MR information using a nonlinear optimization problem and coil magnetic field basis vectors, potentially accelerated by a neural network, to reduce MR scan times and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MR imaging techniques are used to achieve high spatial resolution, then measurement precision is improved, but scan time increases significantly
Solution Approach 1:
The patent applies parallel imaging techniques that segment the measurement process across multiple receiver coils, each capturing data from different spatial regions. By dividing the overall measurement task across multiple independent measurement channels, the system achieves high spatial resolution without proportionally increasing total scan time, as multiple regions are measured simultaneously rather than sequentially
Solution Approach 2:
The patent introduces a new dimension of measurement by utilizing multiple receiver coils with different spatial sensitivities. This adds a coil dimension to the traditional spatial encoding, allowing the system to reconstruct high-resolution images from undersampled k-space data by exploiting the additional spatial information provided by the coil array geometry
2Measurement precision
If longer MR scan times are used to improve characterization accuracy, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent enables continuous efficient measurement by implementing parallel acquisition across multiple coils throughout the entire scan duration. All receiver coils operate simultaneously and continuously during the scan, maximizing the utilization of measurement time and eliminating idle periods, thereby improving throughput without sacrificing characterization accuracy
Solution Approach 2:
By segmenting the measurement across multiple independent coils that operate in parallel, the system achieves accurate characterization of the entire sample volume simultaneously rather than requiring sequential scanning of different regions, thus maintaining high productivity while ensuring comprehensive and accurate measurement
3Measurement precision
If more measurements are performed to achieve high spatial resolution, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the measurement function across multiple receiver coils, where each coil performs a simplified measurement task rather than requiring a single complex full-volume scan. This distribution of measurement complexity across parallel channels reduces the burden on individual measurement sequences while achieving high overall spatial resolution
Solution Approach 2:
The patent employs partial k-space sampling combined with coil sensitivity information to reconstruct full high-resolution images. Rather than acquiring complete k-space data from all coils, the system uses partial measurements from multiple coils and applies iterative reconstruction algorithms, reducing the total number of measurements required while maintaining spatial resolution
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
Reduces MR scan times, lowers costs, and enhances user experience by allowing faster and more efficient characterization of samples, while maintaining accuracy and reducing resource consumption.
Implementation Method 1
A computer that solves a nonlinear optimization problem for MR information associated with the sample and coefficients, using MR signals and a predetermined set of coil magnetic field basis vectors, where weighted superpositions of the predetermined set of coil magnetic field basis vectors represent coil sensitivities of coils in the measurement device, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations
Data Source
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AI summary
A computer that determines coefficients in a representation of coil sensitivities and MR information associated with a sample is described. During operation, the computer may acquire MR signals associated with a sample from the measurement device. Then, the computer may access a predetermined set of coil magnetic field basis vectors, where weighted superpositions of the predetermined set of coil magnetic field basis vectors using the coefficients represent coil sensitivities of coils in the measurement device, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer may solve a nonlinear optimization problem for the MR information associated with the sample and the coefficients using the MR signals and the predetermined set of coil magnetic field basis vectors.