Single-Channel MRI Reconstruction via Multi-Shot Variable Auto-Calibration
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Solution Overview
Problem
Single channel diffusion weighted magnetic resonance imaging (MRI) suffers from higher distortions, longer echo times, and poor image quality due to B0-inhomogeneity, and current reconstruction techniques are limited to multi-channel acquisitions.
Innovation Solution
A computer-implemented method and system for performing echo-planar diffusion weighted imaging (EP-DWI) using multi-shot variable auto-calibrating reconstruction (vARC) for single channel MRI, which involves obtaining k-space data, sampling it for multiple shots with varying central calibration region widths, and reconstructing images using partial Fourier constant sampling, autocalibrating reconstruction, and projection on convex sets, followed by low-rank regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-shot acquisition is used to acquire higher spatial resolution, then spatial resolution is improved, but ghosting in the reconstructions increases due to motion-induced phase errors
Solution Approach 1:
The patent segments the k-space data acquisition into multiple shots, where each shot acquires a portion of the k-space. This segmentation allows for higher spatial resolution while managing the complexity of motion-induced phase errors through separate processing of each shot's data.
Solution Approach 2:
The patent introduces an intermediary processing step that treats multiple shots as multiple channels. This intermediary approach enables the use of channel-based reconstruction techniques to resolve aliasing and reduce ghosting while maintaining the benefits of multi-shot acquisition for spatial resolution.
2Ease of operation
If single-shot acquisition is used for single channel DWI, then acquisition simplicity is maintained, but distortions increase, echo time lengthens, and image quality deteriorates
Solution Approach 1:
The patent dynamically adjusts the treatment of multiple shots as channels based on the acquisition parameters. This dynamic approach allows the system to optimize between single-shot simplicity and multi-shot quality by adaptively applying channel-based processing when multi-shot acquisition is performed.
Solution Approach 2:
The patent changes the interpretation parameter of the acquired data, treating multiple shots as multiple channels rather than as temporal repetitions. This parameter change enables the application of channel-based reconstruction methods that reduce distortions and improve image quality while maintaining relative acquisition simplicity.
3Manufacturing precision
If parallel imaging is used to reduce distortion, then distortion is reduced, but it cannot be applied with single channel acquisition
Solution Approach 1:
The patent creates a universal reconstruction approach that works for both single-channel and multi-channel acquisitions. By treating multiple shots as multiple channels, the method provides the distortion-reduction benefits of parallel imaging universally, regardless of whether the physical hardware has multiple channels or uses temporal multi-shot acquisition instead.
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
The method reduces distortion and improves signal-to-noise ratio by allowing multi-shot data combination without aliasing, and produces good quality images where traditional methods fail, operating at a smaller matrix size and incorporating reverse polarity gradient to further reduce distortion.
Implementation Method 1
During MRI, when a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency.
Implementation Method 2
The resulting set of received nuclear magnetic resonance (NMR) signals are digitized and processed to reconstruct the image
Implementation Method 3
utilizing a low-rank regularization algorithm in an iterative manner to generate a reconstructed image for each shot of the plurality of shots
Data Source
AI summary
A method includes obtaining k-space data acquired by an MRI scanner from a single channel body coil utilizing a multi-shot EP-DWI pulse sequence and sampling the k-space data for a plurality of shots so that for each shot both a central k-space is fully sampled to form a central calibration region and an outer k-space is partially sampled by a factor equal to a number of shots. The method includes reconstructing an initial fully sampled k-space estimate for each shot utilizing both partial Fourier constant sampling and projection on convex sets reconstruction, wherein the plurality of shots is treated as a plurality of channels for filling in missing k-space for a respective shot. The method includes utilizing a low-rank regularization algorithm in an iterative manner to generate a reconstructed image for each shot, wherein the initial fully sampled k-space estimate for each shot is utilized as an initial guess.