Adaptive Time Window for Dynamic MRI Data Interpolation
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Solution Overview
Problem
Current MRI techniques for accelerated imaging often result in inadequate data estimation, leading to suboptimal image quality due to undersampling, particularly in dynamic imaging applications like cardiac or perfusion imaging, where missing data is not accurately reconstructed.
Innovation Solution
The method involves determining an adaptive time window for each image data set to synthesize missing data using sampled data from adjacent time points, leveraging spatiotemporal correlations and performing a 1D Fast Fourier Transform to convert data from k-space to hybrid space, allowing for accurate interpolation of missing data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerated imaging techniques are used to reduce scan time, then imaging speed is improved, but data completeness deteriorates leading to inadequate data estimation
Solution Approach 1:
The system performs preliminary actions by acquiring fully sampled reference data sets at selected time points during the dynamic imaging sequence. These reference data sets serve as a foundation for subsequent interpolation, allowing the system to estimate missing data in accelerated data sets more accurately while maintaining reduced scan times.
Solution Approach 2:
The system uses reference data sets as an intermediary between the accelerated undersampled data and the fully sampled data. By interpolating missing data using both the accelerated data and reference data, the system bridges the gap between speed and completeness, enabling accurate reconstruction without requiring full sampling at every time point.
2Loss of time
If less data is acquired to speed up imaging, then scan time is reduced, but image quality deteriorates due to undersampling artifacts
Solution Approach 1:
The system changes the sampling parameters dynamically by using different sampling strategies at different time points. Fully sampled reference data sets are acquired at selected time points while accelerated sampling is used at intermediate time points. This parameter variation allows the system to maintain image quality through accurate interpolation while reducing overall scan time.
Solution Approach 2:
The system adds a temporal dimension to the data reconstruction process by utilizing data from multiple time points (both reference and accelerated data sets). By interpolating missing data in the temporal dimension using spatiotemporal correlations, the system can reconstruct high-quality images from undersampled data without requiring complete sampling at each time point.
3Device complexity
If conventional data estimation techniques are used for accelerated imaging, then processing complexity is reduced, but reconstruction accuracy deteriorates
Solution Approach 1:
The system uses a composite approach by combining multiple data sources (accelerated undersampled data sets and fully sampled reference data sets) with different sampling characteristics. This composite data structure allows the system to leverage the strengths of both sampling strategies, achieving high reconstruction accuracy through interpolated data that incorporates information from both fully sampled and accelerated data.
Data Source
AI summary
In one embodiment, a method for processing magnetic resonance imaging data is provided. The method includes accessing the magnetic resonance imaging data, the data including a plurality of image data sets defining reconstructable images representative of a subject at different points in time. Each data set includes sampled data for sampled phase encoding points but is missing data for unsampled phase encoding points. An adaptive time window is determined for each image data set, and the missing data of at least one of the image data sets is determined based upon the sampled data for the respective data set and sampled data from at least one other data set within the time window for the respective data set.