Silent Dynamic MRI Using Golden-Angle Spiral Trajectories
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Solution Overview
Problem
Dynamic magnetic resonance imaging (MRI) techniques, such as fMRI, face challenges with loud acoustic noise from rapid gradient switching and low image resolution due to high sampling rates, necessitating separate anatomical scans for accurate brain function localization, which increases scan time and introduces registration errors.
Innovation Solution
A silent dynamic MRI method that acquires functional MR datasets with a sound level similar to background noise by using a sequence of gradients and RF pulses, allowing for golden angle updates in k-space orientation, enabling the generation of both anatomical and functional images from a single dataset without separate anatomical imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fast switching of magnetic field gradients is used to increase sampling rate, then temporal resolution is improved, but acoustic noise increases
Solution Approach 1:
The patent applies dynamics by transitioning from static Cartesian gradient switching to dynamic golden-angle spiral trajectories. The gradient directions are continuously rotated according to golden angle increments, creating a dynamic sampling pattern that reduces abrupt gradient switching and associated acoustic noise while maintaining high temporal resolution through continuous k-space coverage.
Solution Approach 2:
The patent employs curved spiral trajectories in k-space instead of straight Cartesian lines. By sampling along spiral paths at golden angles, the method achieves smooth gradient transitions that reduce acoustic noise. The spiral geometry allows continuous coverage of k-space with minimal abrupt changes in gradient direction, solving the noise-speed contradiction.
2Speed
If high sampling rate is used to improve temporal resolution, then functional imaging capability is improved, but image resolution decreases
Solution Approach 1:
The patent transitions from 2D Cartesian k-space sampling to 3D volumetric spiral sampling. By extending the spiral trajectory into the third dimension and using golden-angle rotations, the method achieves efficient coverage of 3D k-space. This dimensional change allows simultaneous acquisition of high-resolution anatomical information and temporal functional dynamics without the resolution loss typical of high-rate sampling.
Solution Approach 2:
The patent makes the functional imaging sequence universal by enabling it to produce both functional images and high-resolution anatomical images from the same golden-angle spiral acquisition. The multi-functional capability eliminates the need for separate anatomical scans, providing both temporal resolution and high spatial resolution simultaneously.
3Manufacturing precision
If separate anatomical scan is performed to identify functional activity location, then image resolution is improved, but scan time increases
Solution Approach 1:
The patent merges the acquisition of functional and anatomical information into a single integrated scan. By using golden-angle spiral trajectories that simultaneously capture both functional dynamics and high-resolution anatomical details, the method eliminates the need for separate anatomical scanning. This consolidation reduces total scan time while maintaining both resolution requirements.
Solution Approach 2:
The patent creates a universal imaging sequence that performs both functional imaging and high-resolution anatomical imaging in one acquisition. The golden-angle spiral approach is multi-functional, producing both types of images from the same data, thereby eliminating the time penalty of separate scans.
4Measurement precision
If separate anatomical scan is performed for accurate localization, then registration accuracy is improved, but errors from registration are introduced
Solution Approach 1:
The patent combines functional and anatomical imaging in a single acquisition, eliminating the registration step entirely. By acquiring both types of information simultaneously from the same golden-angle spiral data, the method avoids registration errors while maintaining accurate localization. The functional activations are directly mapped onto the high-resolution anatomical structure from the same scan.
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 reduces scan time, minimizes registration errors, and enhances image resolution by allowing flexible temporal reconstruction of functional images, while maintaining a quiet imaging environment suitable for noise-sensitive patients.
Implementation Method 1
magnetic resonance (MR) imaging method of dynamically imaging a subject
Implementation Method 2
applying one or more radio-frequency (RF) excitation pulses to the subject while the sequence of gradients are applied, and collecting the FID dataset corresponding to an RF emission from the subject immediately following each RF excitation pulse
Implementation Method 3
applying a sequence of gradients to the subject, each gradient of the sequence of gradients corresponding to a k-space spoke
Data Source
AI summary
A magnetic resonance (MR) dynamically imaging method is provided. The method includes acquiring a functional MR dataset including frames of k-space datasets, while a functional stimulus is applied to the subject. Acquiring the functional MR dataset includes, acquiring a frame of k-space datasets by setting an orientation as an initial angle, and acquiring a free induction decay (FID) dataset. Acquiring an FID dataset includes applying a sequence of gradients, each gradient of the sequence corresponding to a k-space spoke, wherein the sequence of k-space spokes define a k-space segment having the orientation in a 3D k-space volume. Acquiring a frame of k-space datasets also includes acquiring a gradient echo dataset corresponding to the FID dataset, and updating the orientation as golden angles. The method also includes generating anatomical MR images and functional images based on the functional MR dataset.