Diffusion-Weighted MRI Subregion Sampling Strategy
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Solution Overview
Problem
Current magnetic resonance imaging techniques face challenges in efficiently generating diffusion-weighted image data due to signal losses and artifacts associated with undersampling, particularly in echo-planar MR imaging, where reducing echo time is crucial for maintaining image quality and patient comfort.
Innovation Solution
The method involves at least two recordings with different scan patterns, where the raw data memory is subdivided into a fully sampled first subregion and an undersampled second subregion, allowing for improved signal-to-noise ratio and reduced artifacts by averaging repeated data acquisitions with the same b-value, thereby optimizing the sampling of the raw data space without prolonging the MR control sequence duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If undersampling is used to reduce acquisition time, then productivity is improved, but manufacturing precision deteriorates due to ring artifacts and signal losses
Solution Approach 1:
The raw data memory is divided into a first subregion and a second subregion. The first subregion is fully sampled to maintain image quality and avoid artifacts, while the second subregion is undersampled to reduce overall acquisition time. This segmentation allows different sampling strategies to be applied to different parts of the data space.
Solution Approach 2:
Different quality requirements are applied to different regions of the raw data memory. The first subregion, which contains critical data for image reconstruction, is fully sampled to ensure high quality. The second subregion, which can tolerate lower quality, is undersampled to improve efficiency. This local differentiation resolves the contradiction between speed and quality.
2Reliability
If echo time is reduced to prevent signal loss, then reliability is improved, but productivity deteriorates due to longer acquisition time
Solution Approach 1:
Instead of fully sampling all raw data points (excessive action), the method applies partial sampling to the second subregion while maintaining full sampling in the first subregion. This partial action approach reduces the total number of measurements needed, thereby reducing echo time and preventing signal loss while maintaining sufficient image quality.
3Productivity
If resolution is reduced to decrease the number of raw data points, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
Instead of reducing resolution in the image domain, the method applies undersampling in the raw data domain (k-space). By strategically undersampling the second subregion of raw data while maintaining full sampling in the first subregion, the method reduces the number of measurements without directly compromising the resolution of the final image, as the critical low-frequency information is preserved in the first subregion.
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 enhances the efficiency of diffusion-weighted image data generation by increasing the signal-to-noise ratio, reducing artifacts, and allowing for shorter echo times, thus improving image quality and patient comfort while avoiding the need for complex algorithms used in partial Fourier or parallel imaging methods.
Implementation Method 1
an examination object, particularly a patient, is exposed to a relatively strong main magnetic field of, for example, 1.5 or 3 or 7 tesla... RF pulses, for example excitation pulses, are emitted, which cause the nuclear spins of particular atoms excited into resonance by the RF pulses... During relaxation of these excited nuclear spins, RF signals known as magnetic resonance signals (MR signals) are emitted
Implementation Method 2
gradient pulses are applied by a gradient coil arrangement... RF pulses, for example excitation pulses, are emitted, which cause the nuclear spins of particular atoms excited into resonance by the RF pulses are deflected through a defined flip angle relative to the magnetic field lines of the basic magnetic field
Implementation Method 3
During relaxation of these excited nuclear spins, RF signals known as magnetic resonance signals (MR signals) are emitted, and are received by suitable RF antennae, and then further processed
Implementation Method 4
The raw data are reconstructed by a multidimensional Fourier transform into image data
Data Source
AI summary
In a magnetic resonance method and apparatus for generating diffusion-weighted image data, at least two recordings are implemented in which raw data are acquired at raw data points of a raw data memory weighted with a b-value. The raw data memory has a first subregion and a second subregion, the first subregion being more than half of the total raw data points of the raw data memory. In each of the at least two recordings of the first subregion, full sampling takes place, and the second subregion is differently undersampled in the respective recordings. The raw data are combined and reconstructed into image data weighted with the b-value.