THESIS MRI Signal Separation Method
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Solution Overview
Problem
In magnetic resonance imaging (MRI), existing methods struggle to effectively separate dynamic from static signals, leading to artifacts in images of static structures due to dynamic signals such as blood flow or respiratory motion.
Innovation Solution
The method, known as Temporal Harmonic Encoding and Separation in Space (THESIS), involves accessing magnetic resonance data, determining temporal positions of data acquisition, generating phase-modified data by weighting k-space samples with phasors based on temporal positions, and reconstructing images from these modified data to separate static and dynamic signal components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-band imaging is used to acquire data from multiple slices simultaneously, then scan time is reduced and productivity increases, but artifact suppression decreases and noise increases
Solution Approach 1:
The patent segments the k-space data by temporal position, dividing the acquisition into multiple shots at different time points. This allows separate processing of static and dynamic signal components, enabling artifact suppression while maintaining the accelerated scan time benefits of multi-band imaging.
Solution Approach 2:
The patent extracts and removes dynamic signal components (artifacts) from the static image data by identifying and eliminating time-varying signal patterns. This extraction process separates the harmful dynamic artifacts from the desired static anatomical information, improving image quality without sacrificing scan speed.
2Adaptability or versatility
If dynamic signals are present during scanning, then functional information can be captured, but image artifacts are introduced that degrade static structure visualization
Solution Approach 1:
The patent segments signals into static and dynamic components by analyzing temporal variations across multiple shots. This segmentation allows the static anatomical information to be isolated from dynamic physiological artifacts, enabling both functional imaging capability and clean static structure visualization.
Solution Approach 2:
The patent extracts and removes dynamic signal components that cause artifacts from the final static image. By identifying and eliminating time-varying signal patterns, the method preserves functional imaging capabilities while removing harmful artifacts that degrade static structure visualization.
3Ease of manufacture
If spiral arms are collected in linear order, then data acquisition is simple, but ring-like artifacts appear around dynamic signal sources
Solution Approach 1:
The patent introduces temporal ordering to the spiral arm collection process, making the acquisition dynamic rather than static. By collecting k-space data at different time points and ordering spiral arms temporally, the method transforms simple linear acquisition into a time-aware process that eliminates ring-like artifacts while maintaining acquisition simplicity.
Solution Approach 2:
The patent utilizes periodic temporal sampling of k-space data at multiple time points during the spiral acquisition. This periodic action allows the system to distinguish between static and dynamic signals, eliminating ring-like artifacts that arise from periodic oscillations in linear spiral ordering by adding temporal dimensionality to the acquisition.
Data Source
AI summary
Described here are systems and methods for separating magnetic resonance signals that are changing over a scan duration (i.e., dynamic signals) from magnetic resonance signals that are static over the same duration. As such, the systems and methods described in the present disclosure can be used to remove artifacts associated with dynamic signals from images of static structures, or to better image the dynamic signal (e.g., pulsatile blood flow or respiratory motion).