Iterative Respiratory Motion Correction for Free-Breathing MRI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MRI techniques face challenges in acquiring high-resolution cardiac images due to respiratory motion, leading to prolonged scan times and low navigator efficiency, as existing motion compensation methods either prolong scan time or compromise image quality.
Innovation Solution
A respiratory motion compensation technique that sorts k-space data into bins based on navigator data, estimates motion correction parameters, and iteratively applies these parameters to maximize image sharpness, allowing for a widened gating window without introducing artifacts, thus improving navigator efficiency and reducing scan time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If respiratory gating techniques are used to correct for motion during cardiac MRI, then image quality is improved, but scan time is substantially increased
Solution Approach 1:
The patent applies preliminary action by acquiring low-resolution navigator echoes before the main imaging sequence to establish a reference grid for respiratory motion correction. This preliminary motion mapping allows the subsequent high-resolution imaging to proceed without requiring restrictive gating windows, thereby maintaining image quality while reducing scan time.
Solution Approach 2:
The patent segments the k-space data acquisition into multiple trajectories (radial, spiral, or Cartesian) that can be interleaved with navigator echoes. This segmentation allows independent optimization of imaging and motion tracking, enabling the system to collect motion correction data without sacrificing imaging efficiency or requiring extended scan times.
2Reliability
If navigator pulse sequences are interleaved into the scan to measure subject motion, then motion correction capability is improved, but scan time and disruption of magnetization equilibrium are increased
Solution Approach 1:
The patent merges the navigator echo acquisition with the main imaging sequence by using the same RF excitation and gradient system. The navigator echoes are acquired as additional signals during the imaging process itself, rather than as separate interleaved sequences. This merging eliminates the need for additional scan time while maintaining full motion correction capability.
Solution Approach 2:
The patent implements multi-functionality by using a single pulse sequence design that simultaneously performs both imaging and respiratory motion tracking. The same RF pulse and gradient system serves dual purposes: acquiring high-resolution cardiac images and capturing low-resolution navigator data for motion correction, thereby eliminating the need for separate dedicated navigator sequences.
3Measurement precision
If a narrow gating window is used to gate data during free-breathing acquisition, then respiratory motion correction accuracy is improved, but navigator efficiency and scan time are worsened
Solution Approach 1:
The patent applies feedback by continuously monitoring the subject's actual breathing pattern through navigator echoes and using this information to dynamically adjust the gating window parameters. The system measures respiratory phase and amplitude in real-time, then optimizes the gating window size and position based on the observed breathing characteristics, achieving both high accuracy and efficiency.
Solution Approach 2:
The patent implements dynamics by making the gating window adaptive rather than fixed. The gating window size and position are dynamically adjusted based on the subject's individual breathing pattern, respiratory rate, and amplitude. This dynamic adaptation allows the system to maintain high motion correction accuracy while maximizing navigator efficiency for each subject's unique respiratory characteristics.
Data Source
AI summary
A method for free-breathing magnetic resonance imaging (MRI) using iterative image-based respiratory motion correction is provided. An MRI system is used to acquired k-space data and navigator data from a subject. The k-space data is then sorted into a plurality of data bins using the navigator data. A motion correction parameter is estimated for each data bin and is applied to the respective k-space data in that bin. The corrected k-space data segments are then combined to form a corrected k-space data set, from which an image is reconstructed. The process may be iteratively repeated until an image quality metric is optimized; for example, until an image sharpness measure is sufficiently maximized.