Echo Sharing in Multi-Echo MR Imaging Sequences
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Solution Overview
Problem
Current MR imaging techniques requiring multiple echo times and delay times result in lengthy scan times, even with acceleration factors, due to the need for full k-space acquisitions.
Innovation Solution
The method involves acquiring multiple k-space data sets with different echo times and delays, then combining segments with lower k-space line density to generate a fully sampled k-space data set, allowing for echo sharing and reduced acquisition time by utilizing non-acquired data lines from other acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple echo times and delay times are used with full k-space acquisitions, then image quality and contrast are improved, but scan time increases significantly
Solution Approach 1:
The k-space is divided into multiple segments (first segment closer to center, second segment further away). Different segments are sampled at different densities - the first segment is fully sampled while the second segment is partially sampled. This segmentation allows the system to acquire essential contrast information from the first segment while reducing acquisition time in the second segment through echo sharing.
Solution Approach 2:
Different sampling densities are applied to different regions of k-space. The first segment (inner region) uses high sampling density to preserve contrast information, while the second segment (outer region) uses low sampling density since outer k-space components primarily contribute to image resolution rather than contrast. This local differentiation optimizes the balance between image quality and scan time.
2Loss of information
If full k-space data is acquired at multiple echo times, then contrast information is obtained, but acquisition time becomes excessively long
Solution Approach 1:
The method extracts and utilizes only the necessary k-space data for obtaining contrast information. By identifying that the first segment contains the essential contrast information, the system focuses acquisition resources on this segment while using echo sharing for the second segment, thereby extracting only the necessary information and eliminating redundant acquisitions.
Solution Approach 2:
Echo sharing acts as an intermediary mechanism that allows the system to obtain missing k-space data from alternative sources (other echo times) rather than requiring direct acquisition. This intermediary approach enables the system to reconstruct necessary data without performing the full acquisition sequence, reducing overall acquisition time while preserving contrast information.
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 the overall scan time by sharing echoes from outer segments, which do not contribute to contrast but to image resolution, while maintaining image quality.
Implementation Method 1
a multi-repetition sequence with multiple delay times can be used. Based on this information, the relaxation times are calculated on a per-pixel basis and used later for generating synthetic contrasts
Data Source
AI summary
Techniques are described for generating an MR image of an object using a multi spin-echo based imaging sequence with a plurality of k space segments using a preparation pulse. The technique included acquiring a first k-space dataset of the object using a first echo time and a first delay after the preparation pulse before the several spin-echoes are acquired. The technique further includes acquiring a second k space dataset of the object using a second echo time and a second delay after the preparation pulse, with at least one of the second echo time and the second delay time being different from the corresponding first echo time and the first delay time, generating a combined k space, and generating the MR image based on the combined k space dataset.