Real-Time MR Imaging Using a Pre-Learned Spatial Subspace
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Solution Overview
Problem
Generating on-the-fly MR images with sufficient temporal resolution for MR-based therapies and treatments is challenging due to the complexity involved, leading to slow image generation.
Innovation Solution
A method and system utilizing a pre-learned spatial subspace for real-time MR imaging, involving k-space data decomposition and transformation to rapidly construct dynamic images, enabling rapid image generation with high temporal resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional spatio-temporal decomposition methods are used for real-time MRI, then image generation can be performed, but the process is very slow and cannot achieve sufficient temporal resolution
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing a spatial subspace basis during an initial training phase. This pre-learned spatial subspace is then reused during real-time imaging to rapidly construct dynamic images without repeating the full decomposition process, thereby achieving fast image generation with temporal resolution under 50 milliseconds.
2Measurement precision
If high temporal resolution is achieved through rapid image construction, then real-time tracking is enabled, but the complexity of the image generation process increases
Solution Approach 1:
The patent segments the image generation process into two distinct phases: an offline training phase where the spatial subspace is pre-computed, and an online real-time phase where only simple linear combinations are performed. This segmentation allows high temporal resolution to be achieved during real-time imaging while the computational complexity is confined to the initial training phase, not affecting the real-time performance.
Data Source
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AI summary
A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.