Dynamic MRI Reconstruction via K-Space Segmentation
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Solution Overview
Problem
The slow imaging speed of Magnetic Resonance Imaging (MRI) limits its time resolution and introduces motion artifacts, degrading image quality, especially in dynamic imaging of moving organs like the heart, due to the relatively slow scanning speed and limited acceleration in existing Parallel Imaging methods.
Innovation Solution
A dynamic MRI method that transforms non-sparse MR signal data into sparse images by determining basic and differential k-space data, reconstructing images with regularization based on sparse image prior knowledge, and combining these to enhance scanning speed while maintaining image quality through equidistant undersampling and fullsampling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MRI scanning speed is increased to improve time resolution, then imaging speed improves, but image quality degrades due to motion artifacts and loss of spatial resolution
Solution Approach 1:
The patent segments the image reconstruction process into two parts: a basic image reconstructed from basic k-space data, and differential images reconstructed from differential k-space data. This segmentation allows the system to handle dynamic changes separately while maintaining overall image quality, resolving the contradiction between speed and quality.
Solution Approach 2:
The patent changes the sampling parameters by using equidistant undersampling in the k-space domain, specifically sampling only certain phase-encoding lines. This parameter change enables faster acquisition while the subsequent differential reconstruction algorithm compensates for the reduced sampling, maintaining image quality despite increased speed.
2Productivity
If equidistant undersampling is used to increase scanning speed, then productivity improves, but measurement precision deteriorates due to insufficient k-space data coverage
Solution Approach 1:
The patent performs preliminary action by acquiring basic k-space data that covers the essential low-frequency information before the dynamic imaging sequence. This preliminary data acquisition ensures that the most important spatial information is captured, allowing subsequent undersampled differential imaging to proceed at high speed without losing critical measurement precision.
Solution Approach 2:
The patent introduces differential k-space data as an intermediary that captures only the changes between successive imaging phases. This intermediary approach allows the system to skip redundant data acquisition while maintaining measurement precision for dynamic features, thereby increasing scanning speed without sacrificing essential information.
3Manufacturing precision
If fullsampling is used to maintain image quality, then manufacturing precision improves, but productivity decreases due to extended scanning time
Solution Approach 1:
The patent applies local quality by using fullsampling only for the basic k-space data acquisition, while using equidistant undersampling for the subsequent differential imaging phases. This localized application of fullsampling ensures that the most critical data is obtained with high quality, while less critical dynamic phases can be acquired faster, balancing overall image quality with scanning speed.
Data Source
AI summary
Dynamic magnetic resonance imaging methods and devices are provided. According to an example, a method includes: collecting respective k-space data for each of imaging phases by scanning a part of a subject via an equidistant undersampling manner, determining basic k-space data for the part of the subject, determining respective differential k-space data for each of the imaging phases based on the respective k-space data for each of the imaging phases and the basic k-space data, obtaining a basic image based on the basic k-space data, determining a respective sparse image for each of the imaging phases, reconstructing a respective differential image for each of the imaging phases from the respective differential k-space data for the imaging phase, and obtaining a respective magnetic resonance image for each of the imaging phases based on the respective differential image for the imaging phase and the basic image.