Motion-Based MRI B0 Map Updating Without Rescanning
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Solution Overview
Problem
Magnetic Resonance Imaging (MRI) reconstruction is hindered by spatial inhomogeneities in the magnetic field (B0), which are exacerbated by subject movement during the scan, leading to reduced image quality and artifacts.
Innovation Solution
A method for updating the magnetic field map (B0 map) during an MRI examination using a B0 prediction model, which incorporates initial B0 map data and motion data to estimate changes in the B0 map due to subject movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a B0 map is acquired before the actual MRI scan to correct B0 inhomogeneities, then image quality is improved, but scanning time increases significantly
Solution Approach 1:
The system performs a preliminary B0 map acquisition at the start of the examination to establish baseline magnetic field characteristics. This preliminary action enables subsequent motion-based predictions without requiring repeated full B0 map acquisitions, thus reducing total scanning time while maintaining image quality through proactive field characterization.
Solution Approach 2:
Instead of acquiring new B0 maps after subject movement, the system creates copies or predictions of the B0 map based on motion data and the initial B0 map. This copying approach through prediction models allows the system to generate updated B0 maps without performing additional time-consuming scanning procedures.
2Manufacturing precision
If a B0 map is updated after subject movement to maintain accuracy, then image quality is maintained, but additional scanning time is required
Solution Approach 1:
The system replaces the mechanical scanning process with a computational prediction model. Instead of physically re-scanning the B0 map after subject movement, the system uses a prediction model that takes motion data and the initial B0 map as inputs to generate updated B0 maps, substituting time-consuming mechanical scanning with rapid computational processing.
Solution Approach 2:
The system changes the parameters used for B0 map updates from direct magnetic field measurements to motion-based parameters. By using motion data (translation, rotation, deformation) as input parameters alongside the initial B0 map, the system generates updated B0 maps through parameter transformation rather than repeated physical measurement.
3Measurement precision
If motion data is incorporated to predict B0 map changes, then B0 map accuracy is improved, but computational complexity increases
Solution Approach 1:
The prediction model processes motion data by segmenting it into distinct components: rigid body motion (translation and rotation) and non-rigid deformation. This segmentation allows the system to handle different types of motion separately, applying appropriate computational methods to each component, thereby managing complexity while maintaining accuracy.
Solution Approach 2:
The system implements a dynamic prediction model that adapts to different types of subject motion. The model dynamically adjusts its processing based on the detected motion characteristics, using motion data to predict B0 map changes in real-time during the MRI examination, thereby maintaining accuracy across varying motion conditions.
Data Source
AI summary
Proposed concepts thus aim to provide schemes, solutions, concept, designs, methods and systems pertaining to updating a magnetic field (B0) map of a subject during a magnetic resonance imaging (MRI) examination or during image reconstruction. In particular, when a subject moves, the magnetic field inside the magnetic bore changes. As a result, any B0 map obtained prior to the movement of the subject may be inaccurate. Accordingly, an initial B0 map is updated to reflect changes in the B0 map caused by the movement of the subject. This can be achieved by determining a B0 map of the subject based on a B0 prediction model, instead of spending additional scanning time to acquire another B0 map.