MR Image Movement Correction via PPA k-Space Linking
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Solution Overview
Problem
Magnetic resonance (MR) imaging is hindered by object movements during and between imaging sequences, leading to reduced image quality and potentially erroneous diagnoses, especially in triggered recordings where movement artifacts cause ghosting artifacts and require time-consuming re-recording.
Innovation Solution
A robust retrospective movement correction method for MR imaging using partially parallel acquisition (PPA) techniques, specifically linking impaired k-space data from one echo train to corresponding reconstructed data from other echo trains, primarily through the GRAPPA method, to correct movement-impaired k-space data and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object movement occurs during MR imaging sequences, then image quality deteriorates with ghosting artifacts, but re-recording increases time consumption
Solution Approach 1:
The patent applies preliminary action by performing movement detection and correction during the imaging process itself. Navigators are acquired concurrently with the imaging sequence, and movement parameters are calculated in real-time to correct k-space data before image reconstruction, preventing the need for re-recording and maintaining image quality without time loss
Solution Approach 2:
The patent implements feedback by using navigators to continuously monitor object movement during imaging. The measured movement parameters are fed back to adjust the imaging process dynamically - specifically, to correct phase errors and reposition k-space data points, thereby compensating for motion artifacts and maintaining diagnostic image quality
2Measurement precision
If navigators or external sensors are used to establish trigger time points, then recording precision improves, but image quality reduces when erroneous detection or additional movement occurs
Solution Approach 1:
The patent applies self-service by using the imaging data itself to detect and correct movement. Instead of relying on external sensors that may fail, the system uses the acquired k-space data and navigators to automatically detect movement and perform self-correction through phase error calculation and k-space data adjustment, ensuring both precision and reliability
Solution Approach 2:
The patent performs preliminary movement detection using navigators acquired at the beginning and during the sequence. This preliminary action establishes baseline movement parameters that are used to predict and correct subsequent motion artifacts, preventing image quality degradation before it occurs
3Productivity
If PPA methods are used for fast image generation, then productivity increases, but movement correction capability is reduced
Solution Approach 1:
The patent applies segmentation by dividing the k-space data into multiple segments corresponding to different echo trains. Each segment is independently corrected for movement using navigator-derived parameters, allowing parallel processing and maintaining fast image generation while ensuring accurate movement correction for each segment
Solution Approach 2:
The patent changes parameters by dynamically adjusting phase corrections and k-space data positioning based on measured movement parameters. This allows the system to adapt to motion while maintaining the accelerated imaging speed provided by PPA methods, as the corrections are applied efficiently during the reconstruction process
Data Source
AI summary
A method for correcting magnetic resonance (MR) object movements includes performing a recording of an MR object with multiple echo trains. k-space data pertaining to an echo train regarded as impaired by an MR object movement is corrected by linking the k-space data to corresponding k-space data reconstructed from k-space data of other echo trains by a PPA method.