K-space Generation in MRI via Motion-Conditional Acquisition
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) systems face challenges in efficiently generating accurate k-space data, particularly due to motion artifacts such as respiratory motion, which can lead to incomplete or erroneous image acquisition and prolonged processing times.
Innovation Solution
The system employs a navigator unit to track repetitive motion of the object, allowing for dynamic adjustment of k-space data acquisition by dividing the k-space into areas and acquiring data only within predetermined motion ranges, thereby optimizing data collection and reducing unnecessary data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system acquires k-space data for all portions irrespective of motion, then complete k-space data is obtained, but acquisition time increases and motion artifacts are introduced
Solution Approach 1:
The patent segments the k-space data acquisition into two distinct portions: a first portion acquired only when motion is within the predetermined range, and a second portion acquired irrespective of motion status. This segmentation allows the system to optimize acquisition time by conditionally acquiring only critical k-space data when motion is present, while maintaining complete data acquisition when conditions permit.
2Loss of time
If the system stops acquiring k-space data when motion exceeds the predetermined range, then acquisition time is reduced, but image quality deteriorates due to incomplete data
Solution Approach 1:
The patent divides k-space into two portions with different acquisition strategies. The first portion (central region) is acquired conditionally based on motion criteria, while the second portion (outer region) is acquired unconditionally. This ensures that critical anatomical information from the central k-space region is obtained only when motion is acceptable, while peripheral information is always collected to maintain overall image quality.
Solution Approach 2:
Different quality standards and acquisition strategies are applied to different regions of k-space. The central region receives conditional acquisition with motion verification to ensure high quality, while the outer region receives unconditional acquisition. This local differentiation optimizes the balance between acquisition time and image quality by applying stricter criteria only where most critical.
3Productivity
If the system acquires k-space data conditionally based on motion range, then acquisition speed increases, but data completeness may be compromised
Solution Approach 1:
The patent segments k-space acquisition into conditional and unconditional portions. The first portion is acquired conditionally when motion is within the predetermined range, improving speed by avoiding redundant acquisitions. The second portion is acquired unconditionally to ensure data completeness. This segmentation strategy achieves both speed improvement and data completeness by strategically applying conditional acquisition only to the most critical k-space region.
Data Source
AI summary
A magnetic resonance (MR) image processing system includes a data collection unit that acquires image data from a target region of an object. A navigator unit acquires a motion signal indicating motion comprising motion of at least a portion of an object. A data processing unit derives k-space data for a k-space data array from the acquired image data, by acquiring k-space data for a first portion of the k-space from the acquired image data in response to the motion signal indicating motion is within a predetermined range and acquiring k-space data for a second portion of the k-space from the acquired image data irrespective of the predetermined range.