MRI K-space Sampling Density Control for Parameter Mapping
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) systems face inefficiencies in estimating parameters such as T1 and T2 values due to the lengthy process of generating multiple images with varying contrasts, leading to prolonged imaging times and difficulties in efficiently producing parameter mapping images.
Innovation Solution
The MRI apparatus employs a method to control sampling densities in k-space data acquisition based on predetermined imaging parameters, allowing for efficient estimation of parameters by adjusting phase encodes and sampling densities according to regions with varying pixel values, and using techniques like the Look-Locker method and multiecho spin echo sequences for T2 mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images with varying contrasts are generated to estimate parameters such as T1 and T2 values, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments k-space into multiple regions and applies different sampling densities to each region. High sampling density is applied to regions containing critical information for parameter estimation, while lower sampling density is used in other regions. This segmentation allows the system to maintain measurement precision for T1 and T2 parameter estimation while reducing the overall number of samples required, thereby decreasing imaging time.
Solution Approach 2:
The patent implements local quality by assigning different sampling densities to different regions of k-space based on their informational importance. Regions that contribute more significantly to parameter estimation are sampled at higher densities, while less critical regions are sampled at lower densities. This localized approach ensures that measurement precision is maintained where it matters most while reducing total acquisition time.
2Measurement precision
If sampling density is increased to improve parameter estimation accuracy, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent divides k-space into multiple regions and segments the sampling process accordingly. By identifying and segmenting the critical regions that contribute most to parameter estimation accuracy, the system can concentrate sampling resources where they provide the most benefit. This segmentation enables high measurement precision in parameter mapping while maintaining overall imaging efficiency through optimized resource allocation.
Solution Approach 2:
The patent applies partial action by sampling only the essential portions of k-space at high density rather than uniformly sampling the entire k-space. By identifying the minimum necessary sampling required for accurate parameter estimation and applying excessive sampling only to those critical regions, the system achieves high measurement precision without the productivity loss that would result from uniform high-density sampling of all k-space.
3Ease of operation
If uniform sampling density is applied across all k-space data, then ease of operation is maintained, but measurement precision deteriorates
Solution Approach 1:
The patent automatically segments k-space into multiple regions based on their informational content without requiring manual intervention. This automated segmentation maintains ease of operation by eliminating the need for users to manually identify critical regions, while simultaneously improving measurement precision by applying appropriate sampling densities to each segmented region based on its contribution to parameter estimation.
Solution Approach 2:
The system performs self-service by automatically analyzing k-space to identify critical regions and determining optimal sampling densities for each region without user input. This self-service capability maintains ease of operation for the user while achieving improved measurement precision through intelligent, adaptive sampling strategies that would otherwise require complex manual configuration.
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 significantly reduces imaging time by optimizing data acquisition patterns, enabling efficient estimation of parameters and generation of parameter mapping images like T1 and T2 mapping, thereby improving the overall efficiency of MRI procedures.
Implementation Method 1
magnetic resonance imaging (MRI) apparatus is an apparatus that visualizes atom distribution in a subject nondestructively using nature that atoms of hydrogen and the like arranged in a magnetic field selectively absorb and release only electromagnetic waves of a frequency determined depending on the types of the atoms and the magnetic field
Data Source
AI summary
A magnetic resonance imaging apparatus according to an embodiment includes first processing circuitry and second processing circuitry. The first processing circuitry executes a pulse sequence in a acquisition pattern set such that sampling densities of a plurality of pieces of k space data are made different in accordance with a predetermined imaging parameter when the pieces of k space data having different values of the imaging parameter are acquired while changing the values of the imaging parameter. The second processing circuitry generates an image based on the pieces of k space data.