Dynamic MRI T1-Weighted Imaging via Non-T1 Factor Suppression
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Solution Overview
Problem
In T1 weighted dynamic imaging, non-T1 factors such as proton density, T2* relaxation effect, and receiving coil sensitivity introduce errors and biases to signal analysis, affecting image reconstruction and physiological analysis.
Innovation Solution
Collecting multiple MR data sets based on varying scan parameters and performing division operations to determine second MR data sets that minimize the influence of non-T1 factors, generating T1 weighted images with improved accuracy and sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional T1 weighted dynamic imaging is performed, then T1 information can be obtained, but non-T1 factors (proton density, T2* relaxation, coil sensitivity) introduce errors and biases to signal analysis
Solution Approach 1:
The patent applies parameter changes by acquiring MR data at multiple different flip angles and repetition times (TR values). By varying these scan parameters and performing division operations on the acquired data sets, the method isolates T1-weighted signal components while eliminating confounding non-T1 factors such as proton density, T2* relaxation effects, and coil sensitivity variations, thereby improving both measurement precision and reliability
Solution Approach 2:
The patent introduces an intermediary processing step where multiple MR data sets acquired at different parameters are divided to generate corrected data sets. This intermediary division operation acts as a mediator that separates the T1 information from non-T1 factors, enabling accurate T1 mapping without the biases introduced by other physiological and equipment-related parameters
2Measurement precision
If multiple MR data sets are collected based on different scan parameters to eliminate non-T1 factors, then T1 weighted image accuracy is improved, but scanning time and data processing complexity increase
Solution Approach 1:
The patent employs partial action by acquiring data at a limited number of strategically selected flip angles and TR values rather than exhaustive sampling. This partial sampling approach, combined with division operations, achieves sufficient T1 weighting accuracy without requiring complete characterization of all parameter spaces, thereby reducing scanning time while maintaining image accuracy
Data Source
AI summary
A method for magnetic resonance imaging (MRI) may include obtaining a plurality of first magnetic resonance (MR) data sets related to a region of interest (ROI) of a subject. The plurality of first MR data sets may be collected based on two or more different values of a scan parameter. The method may also include determining a plurality of second MR data sets based on the plurality of first MR data sets. Each of the plurality of second MR data sets may correspond to at least two of the plurality of first MR data sets. The method may also include generate, based on the plurality of second MR data sets, a plurality of T1 weighted images of the ROI each of which corresponds to a target time point.