Double-Contrast Magnetic Resonance Fingerprinting for Faster T1/T2 Mapping
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Solution Overview
Problem
Existing magnetic resonance fingerprinting (MRF) methods face challenges in accurately quantifying T1 and T2 relaxation times due to non-optimal acquisition parameter settings, leading to longer scanning times and reduced patient diagnostic efficiency.
Innovation Solution
A method of double-contrast magnetic resonance fingerprinting that optimizes RF pulse parameters using Cramér-Rao lower bound (CRLB) optimization and incorporates a FISP and PSIF contrast module, with dual-contrast encoding and B-spline constraints, to enhance T1 and T2 quantification precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the length of the acquisition sequence is increased to improve quantification accuracy, then the accuracy of T1 and T2 quantification is improved, but the signal acquisition time is extended
Solution Approach 1:
The patent divides the acquisition sequence into multiple segments with different contrast modules (FISP, PSIF, SPGR) that can be selectively applied. Each segment targets specific parameter optimization, allowing comprehensive quantification without requiring a single excessively long sequence. This segmentation enables the system to achieve accurate T1 and T2 quantification through multiple shorter, optimized segments rather than one prolonged sequence.
Solution Approach 2:
The patent implements dynamic selection of contrast modules based on the current acquisition phase and target parameters. The system dynamically adjusts which contrast module (FISP for T1, PSIF for T2, or SPGR for both) is active at any given time, optimizing the acquisition process adaptively. This dynamic approach allows the system to achieve high quantification accuracy without requiring a fixed, overly long sequence duration.
2Ease of manufacture
If non-optimal acquisition parameter settings are used in MRF, then the implementation is simpler, but the quantification accuracy deteriorates
Solution Approach 1:
The patent systematically varies acquisition parameters (flip angle, repetition time, echo time) across different contrast modules and acquisition segments to optimize quantification accuracy. By implementing structured parameter changes rather than using fixed non-optimal settings, the system achieves high precision while maintaining implementation feasibility through automated parameter management.
Solution Approach 2:
The patent incorporates feedback mechanisms where acquisition results from previous segments inform the configuration of subsequent segments. The system uses the quantification results and signal characteristics from FISP and PSIF segments to optimize SPGR segment parameters, creating a feedback loop that progressively improves accuracy without requiring complex manual intervention.
3Measurement precision
If CRLB optimization is applied to acquisition parameters, then the quantification accuracy is improved, but the complexity of the optimization process increases
Solution Approach 1:
The patent applies CRLB optimization separately to each contrast module segment rather than attempting to optimize all parameters simultaneously across the entire sequence. This segmentation of the optimization process reduces computational complexity while maintaining the accuracy benefits of CRLB optimization. Each segment (FISP, PSIF, SPGR) undergoes independent optimization, making the overall process more manageable.
Solution Approach 2:
The patent performs preliminary CRLB optimization to determine optimal baseline parameters before the actual MRF acquisition. By pre-calculating optimal flip angles, repetition times, and echo times based on expected tissue parameter ranges, the system reduces the complexity of real-time optimization during scanning. This preliminary optimization establishes a foundation that simplifies the main acquisition process.
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
Improves the accuracy of T1 and T2 quantification while reducing scanning time and energy consumption, maintaining T1 accuracy and enhancing T2 accuracy through optimized parameter settings and dual-contrast encoding.
Implementation Method 1
Magnetic resonance technology utilizes the magnetic moment characteristics of atomic nuclei within molecules to perform qualitative, quantitative, and structural analysis of sample under investigation
Implementation Method 2
pattern matching techniques are applied to extract quantitative parameter values for each pixel in the acquired image, including longitudinal relaxation time (T1) and transverse relaxation time (T2)
Implementation Method 3
pattern matching techniques are applied to extract quantitative parameter values for each pixel in the acquired image, including longitudinal relaxation time (T1) and transverse relaxation time (T2)
Implementation Method 4
automatic differentiation of Bloch simulations is utilized within the CRLB optimization process, enabling efficient and flexible optimization
Data Source
AI summary
A method of double-contrast magnetic resonance fingerprinting including: optimizing, using Cramér-Rao lower bound (CRLB) via a computer system, radiofrequency (RF) pulse parameters in an MRF sequence; loading, via the computer system, the RF pulse parameters optimized into an MRI scanner; and capturing raw k-space data by selecting different signal contrast modules for dual-contrast encoding at varying repetition times (TR); reconstructing, via the computer system, at least one image including a plurality of pixels; defining, via the computer system, a dynamic range and step size for tissue parameters; and creating, via the computer system, a dictionary based on Bloch equations, the dynamic range, and the step size; and comparing, via the computer system, signal evolution for each of the plurality of pixels in the at least one image to the dictionary to determine quantitative parameters for each of the plurality of pixels, and generating a quantitative parameter map.