MRI Slice Calibration via k-Space Data Rearrangement
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Solution Overview
Problem
Current simultaneous multislice (SMS) magnetic resonance imaging (MRI) techniques face challenges with reduced signal-to-noise ratio, separation artifacts, and incorrect assignments due to limited reference lines in k-space, leading to longer scan times and increased patient movement risks.
Innovation Solution
The method involves generating a fully sampled reference dataset with rearranged data points to create a calibration dataset, which is then used to calculate a reconstruction dataset for assigning MR imaging data to individual slices, allowing for a higher signal-to-noise ratio and reduced artifacts by utilizing more data points efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If reference lines are deleted in k-space for SMS acquisition, then scan time is reduced, but signal-to-noise ratio is reduced and separation artifacts increase
Solution Approach 1:
The patent applies preliminary action by performing calibration with all reference lines before the actual SMS acquisition. The calibration dataset is created in advance using fully sampled reference data, allowing the reconstruction algorithms to be optimized beforehand. This prevents the need to delete reference lines during the actual scan, maintaining high signal-to-noise ratio while still achieving accelerated imaging through the pre-computed calibration.
2Loss of time
If reference lines are deleted in k-space for SMS acquisition, then scan time is reduced, but separation artifacts increase
Solution Approach 1:
The patent performs slice separation calibration in advance using all available reference lines before the actual SMS scan. The slice separation matrices are computed during this preliminary calibration phase when full reference data is available. During the actual accelerated SMS acquisition, these pre-computed matrices are applied without needing to delete reference lines, thus maintaining high separation accuracy while achieving time reduction.
3Reliability
If the number of reference lines is increased, then signal-to-noise ratio is improved, but scan time is extended
Solution Approach 1:
The patent resolves this contradiction by performing the calibration that requires all reference lines in a preliminary step before the actual SMS acquisition. The calibration dataset is fully sampled with all reference lines present, enabling accurate reconstruction algorithm training. The actual SMS scan then uses accelerated sampling without reference line deletion, as the calibration is already complete. This separates the time-consuming high-quality calibration from the accelerated imaging phase.
Solution Approach 2:
The patent segments the imaging process into distinct phases: a calibration phase where all reference lines are used to build reconstruction datasets, and an acquisition phase where accelerated SMS imaging is performed using pre-computed calibration data. This segmentation allows the system to benefit from both full reference line utilization (for quality) and accelerated sampling (for speed) in different stages of the overall process.
4Measurement precision
If TSE reference scan is used for calibration, then calibration accuracy is improved, but SAR load increases and T2 decay reduces signal
Solution Approach 1:
The patent applies partial action by using a gradient echo reference scan instead of a full TSE reference scan for calibration purposes. The gradient echo sequence provides sufficient calibration data with lower SAR load and reduced T2 decay effects. The calibration accuracy is maintained by ensuring adequate sampling and proper reconstruction algorithm design, while avoiding the excessive energy deposition and signal loss associated with TSE sequences.
Data Source
AI summary
The disclosure relates to a method for calibration in a magnetic resonance (MR) imaging procedure, in which MR imaging data is acquired simultaneously from a multiplicity of slices of a subject under examination, wherein at least one subsampled calibration dataset is generated from a fully sampled reference dataset of an individual slice by rearranging an order of the data points in the reference dataset. In addition, a reconstruction dataset, which is used to assign MR imaging data to the individual slice, is calculated based on the rearranged order of the at least one calibration dataset, wherein the MR imaging data of the individual slice is subsampled in k-space.