MRI Navigator Image Generation for Respiratory Motion Detection
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Solution Overview
Problem
Current respiratory motion detection in MRI using 1D navigator profiles has an accuracy rate of only about 80% due to inaccurate boundary feature identification, leading to a high failure rate, and transitioning to 2D profiles is resource-intensive and time-consuming.
Innovation Solution
Generating a navigator image from multiple 1D navigator profiles using image segmentation and deep learning-based neural networks, such as U-Net, to accurately identify the boundary location and determine when to acquire MR image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 1D navigator profiles are used for respiratory motion detection, then scan time and processing burden are reduced, but detection accuracy decreases to about 80%
Solution Approach 1:
The patent transforms 1D navigator profiles into 2D navigator images by adding a temporal dimension. Multiple 1D profiles acquired over time are stacked to form 2D images, enabling more accurate boundary detection while maintaining efficient scan times. This dimensional transformation allows the system to leverage the strengths of both 1D (speed) and 2D (accuracy) approaches.
2Measurement precision
If 2D navigator profiles are used for respiratory motion detection, then boundary identification accuracy improves, but scan time and processing burden increase significantly
Solution Approach 1:
The patent segments the navigator acquisition process into two phases: a brief calibration phase that collects multiple 1D profiles to create a 2D reference image for accurate boundary identification, and a faster imaging phase that uses the established boundary location. This segmentation allows the system to use 2D processing only when necessary for accuracy while maintaining speed during routine imaging.
Solution Approach 2:
The patent applies partial 2D processing by using 2D navigator images only for boundary identification during calibration or when accuracy is critical, rather than continuously using full 2D processing. This partial application of the more resource-intensive 2D method achieves sufficient accuracy while minimizing the impact on scan time and processing burden.
3Device complexity
If traditional gating methods are used, then system complexity is reduced, but motion artifact reduction effectiveness decreases
Solution Approach 1:
The patent introduces navigator images as an intermediary between the simple 1D profile acquisition and the final gating decision. These 2D navigator images serve as a mediator that enhances motion detection accuracy without requiring a complete overhaul of the gating system architecture, thus improving reliability while maintaining reasonable complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of respiratory motion detection using 1D navigator data, reducing the need for 2D profiles and minimizing scan time and processing burdens while maintaining high detection precision.
Implementation Method 1
Magnetic resonance (MR) imaging is often used to obtain internal physiological information of a patient
Implementation Method 2
the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency
Implementation Method 3
If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or 'longitudinal magnetization', Mz, may be rotated, or 'tipped', into the x-y plane to produce a net transverse magnetic moment Mt
Implementation Method 4
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used
Data Source
AI summary
A method of controlling magnetic resonance (MR) image data acquisition includes generating a plurality of one-dimensional (1D) navigator profiles reflecting motion of an anatomic boundary region of an imaging subject over time at a measurement interval, and then generating a plurality of navigator image segments each for a corresponding 1D navigator profile of the plurality of 1D navigator profiles. A navigator image is then generated based on the plurality of navigator image segments, and a determination is made whether to acquire MR image data based on the navigator image.