MRI Diffusion Group Cycling to Reduce Gradient Amplifier Stress
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Solution Overview
Problem
Magnetic resonance imaging (MRI) diffusion gradient cycling methods cause significant stress on gradient amplifiers, leading to overheating and prolonged repetition times, which extend the acquisition time of diffusion-weighted imaging volumes, and are incompatible with state-of-the-art motion correction algorithms.
Innovation Solution
Implementing diffusion group cycling by organizing diffusion encoding gradients into N groups, allowing all slices to be acquired back-to-back within An*TR, where N is two or larger, ensuring each gradient is applied at least once per TR, and utilizing algorithms to optimize gradient allocation for load balancing and motion correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If diffusion gradient cycling is implemented to reduce stress on gradient amplifiers, then overheating and repetition time are improved, but the acquisition time of diffusion-weighted imaging volume is extended
Solution Approach 1:
The patent segments the diffusion gradients into N groups (where N ≥ 2), with each group containing multiple diffusion gradient directions. This segmentation allows the system to cycle through groups of gradients rather than applying all gradients sequentially, reducing the temporal footprint of each volume while distributing the thermal load across multiple TR cycles. The imaging slices are acquired back-to-back for all diffusion gradients corresponding to a group within An*TR time, where An is the number of diffusion gradients in each group.
Solution Approach 2:
The patent implements periodic cycling through N groups of diffusion gradients, where each group is applied repeatedly across multiple TR cycles. This periodic action pattern (group cycling) allows the system to maintain load balancing and reduce peak thermal stress on gradient amplifiers while completing the full diffusion weighting within a shorter total acquisition time compared to traditional sequential gradient cycling.
2Stress or pressure
If diffusion gradient cycling is implemented to manage thermal stress, then gradient amplifier stress is reduced, but motion sensitivity increases
Solution Approach 1:
By segmenting diffusion gradients into N groups and acquiring all slices for a group back-to-back within An*TR, the patent creates discrete temporal blocks that isolate motion artifacts to specific groups rather than spreading them across the entire diffusion scan. This segmentation allows motion correction algorithms to more effectively track and correct motion within each group's acquisition window.
Solution Approach 2:
The patent incorporates feedback mechanisms through its group cycling structure, where the acquisition pattern is designed to be compatible with state-of-the-art motion correction algorithms. The load balancing and temporal distribution of gradients create feedback loops that allow real-time monitoring and adjustment of motion artifacts, improving the robustness of diffusion imaging.
3Stress or pressure
If traditional diffusion gradient cycling is used, then gradient stress is managed, but compatibility with motion correction algorithms is lost
Solution Approach 1:
The patent introduces dynamic adaptability by designing the group cycling structure to accommodate various motion correction algorithms. The flexible grouping of diffusion gradients (where N ≥ 2 and each group has An gradients) allows the system to dynamically adjust acquisition patterns to match the requirements of different motion correction methods, maintaining versatility and adaptability in clinical settings.
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 reduces the temporal footprint of each volume, minimizes motion sensitivity, and maintains load balancing, while being compatible with state-of-the-art motion correction algorithms, resulting in more robust and efficient diffusion imaging compatible with multi-center studies.
Implementation Method 1
magnetic field gradients (Gx, Gy, and Gz) are employed
Implementation Method 2
The resulting set of received nuclear magnetic resonance (NMR) signals are digitized and processed
Implementation Method 3
Long diffusion encoding gradients create significant stress for the semiconductors in the gradient amplifiers. To avoid overheating
Data Source
AI summary
A computer-implemented method and system for performing magnetic resonance diffusion weighted imaging of an object includes generating, via a processor, a diffusion weighted imaging sequence where all diffusion gradients are allocated into N groups such that all imaging slices are acquired for all the diffusion gradients corresponding to a group of the N groups back-to-back in An*TR, wherein N is two or larger, wherein An is a number of diffusion gradients of each group of the N groups and TR is repetition time, and wherein in each TR every diffusion gradient of the group of the N groups is applied at least once. The computer-implemented method and system also includes acquiring, via the processor, imaging slices for multiple groups of the N groups utilizing the diffusion weighted imaging sequence during a scan of the object utilizing a magnetic resonance imaging scanner.