vSENSE MRI Reconstruction with Variable k-Space Sampling
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Solution Overview
Problem
Current MRI techniques face challenges in efficiently imaging nuclear magnetic resonance (NMR) parameters with variably-accelerated sensitivity encoding, particularly in heterogeneous systems, leading to long acquisition times and spatial image 'unfolding' artifacts when measuring parameters with low signal-to-noise ratios.
Innovation Solution
The method of variably-accelerated sensitivity encoding (vSENSE) involves using a plurality of MRI sequences to sample image k-space, with at least one sequence fully sampling and the others under-sampling, and employing incoherence absorption or artifact suppression strategies to estimate and apply sensitivity maps for reconstructing spatial images of NMR signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI sequences are used to acquire parameter-sensitive data, then measurement accuracy is maintained, but acquisition time becomes excessively long
Solution Approach 1:
The patent segments the k-space sampling process by applying different acceleration factors to different parameter maps. Fully-sampled k-space is acquired for at least one parameter map to ensure accurate sensitivity estimation, while partially-sampled k-space is used for other parameter maps to reduce acquisition time. This segmentation allows the system to balance measurement precision and acquisition time across multiple parameters.
Solution Approach 2:
The patent performs preliminary full k-space sampling for at least one parameter map before acquiring data for other parameter maps. This preliminary action enables accurate estimation of coil sensitivity maps and unfolding artifacts, which are then applied to accelerate the acquisition of subsequent parameter maps without sacrificing overall measurement accuracy.
2Productivity
If sensitivity encoding (SENSE) is applied to accelerate MRI acquisition, then scan time is reduced, but spatial image unfolding artifacts increase
Solution Approach 1:
The patent uses feedback by estimating unfolding artifacts from fully-sampled k-space data and using this information to correct the partially-sampled parameter maps. The artifact estimation from the fully-sampled reference map is applied back to the accelerated maps to suppress unfolding artifacts, creating a self-correcting system that maintains spatial accuracy while achieving acceleration.
Solution Approach 2:
The patent introduces fully-sampled k-space data as an intermediary element that mediates between the accelerated acquisition and the final image quality. This intermediary provides the reference information needed to estimate sensitivity maps and unfolding artifacts, which then serve to correct the partially-sampled data, enabling acceleration without proportionally increasing artifacts.
3Loss of time
If uniform acceleration is applied to all parameter maps, then overall acquisition time is reduced, but localization accuracy varies across different parameters
Solution Approach 1:
The patent implements dynamic acceleration by allowing each parameter map to have its own acceleration factor based on its specific requirements. The system dynamically adjusts the sampling strategy for each parameter, applying higher acceleration where tolerable and lower acceleration where precision is critical, rather than using a static uniform acceleration factor across all parameters.
Solution Approach 2:
The patent applies local quality by treating each parameter map with different sampling densities according to its specific needs. Fully-sampled k-space is acquired for parameters requiring high localization accuracy, while partially-sampled k-space is used for parameters where acceleration is more beneficial. This local differentiation optimizes the balance between acquisition time and measurement precision for each parameter individually.
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
vSENSE enables pixel-by-pixel or voxel-by-voxel measurement of NMR parameters with significantly higher acceleration factors than conventional methods, reducing acquisition time and minimizing localization errors and artifacts, while maintaining high-resolution image information.
Implementation Method 1
nuclear magnetic resonance (NMR) parameters
Data Source
AI summary
A method of spatially imaging a nuclear magnetic resonance (NMR)parameter whose measurement requires the acquisition of spatially localized NMR signals in a sample includes placing the sample in an MRI apparatus with a plurality of MRI detectors each having a spatial sensitivity map; and applying MRI sequences adjusted to be sensitive to the NMR parameter. At least one of the MRI sequences is adjusted so as to substantially fully sample an image k-space of the sample. The remainder of the MRI sequences is adjusted to under-sample the image k-space. The method further includes acquiring image k-space NMR signal datasets; estimating a sensitivity map of each of the MRI detectors using a strategy to suppress unfolding artefacts; and applying the estimated sensitivity maps to at least one of the image k-space NMR signal data sets to reconstruct a spatial image of NMR signals that are sensitive to the NMR parameter.