Ghost MRI Reconstruction via K-Space Segmentation
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Solution Overview
Problem
Current MRI techniques face challenges in achieving high signal-to-noise ratio and are susceptible to artifacts, particularly 'ghost' artifacts, which limit diagnostic quality and require invasive contrast agents, making non-contrast alternatives and motion artifact suppression essential for effective vascular imaging.
Innovation Solution
A system and method that utilizes ghost artifacts to reconstruct background-suppressed MR images by acquiring and combining MR data sets from target and background tissues in different states, transforming the composite data set to produce images with desired ghost artifacts that depict the target component while excluding background signal, allowing for high-resolution imaging without contrast agents and motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI techniques are used to image vascular structures, then detailed images can be obtained, but ghost artifacts and noise reduce image quality and require invasive contrast agents
Solution Approach 1:
The patent converts ghost artifacts from harmful interference into useful signal carriers. By recognizing that ghost artifacts contain information about background tissue, the system uses them to generate background-suppressed images through a novel image reconstruction algorithm that separates target tissue signals from background signals using the ghost artifact information.
Solution Approach 2:
The patent segments the MR signal into target tissue components and background tissue components. By dividing the k-space data into different segments and applying different processing techniques to each segment, the system isolates the target tissue signal while suppressing background tissue signals, thereby eliminating ghost artifacts.
2Reliability
If contrast agents are used to enhance vascular imaging, then diagnostic capability improves, but invasive procedures and risks increase
Solution Approach 1:
The patent extracts the background tissue signal information from the MR data and uses it to suppress background signals in the final image. This extraction approach allows the system to achieve contrast enhancement without requiring external contrast agents, thereby eliminating the associated invasive procedures and risks.
Solution Approach 2:
The system uses the body's own tissue signals to generate the necessary contrast information. By utilizing the MR signals from background tissues and processing them through the image reconstruction algorithm, the system achieves self-contrast enhancement without requiring external contrast agents.
3Productivity
If motion artifacts are present in MRI scans, then imaging speed increases, but image quality deteriorates
Solution Approach 1:
The patent employs dynamic image reconstruction techniques that adapt to motion during the scanning process. By using dynamic algorithms that can handle varying signal conditions and motion artifacts, the system maintains image quality even when patients move during the scan, allowing for faster imaging without sacrificing diagnostic accuracy.
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 enables the creation of high-quality, background-suppressed MR images with improved signal-to-noise ratio and reduced artifacts, enhancing diagnostic capabilities in vascular imaging without the need for contrast agents or invasive procedures, and is versatile for various imaging applications.
Implementation Method 1
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency.
Implementation Method 2
An image is reconstructed from the acquired k-space data by transforming the k-space data set to an image space data set. With a Cartesian grid of k-space data that results from a 2D or 3D spin-warp acquisition, for example, the most common reconstruction method used is an inverse Fourier transformation ('2DFT' or '3DFT') along each of the 2 or 3 axes of the data set.
Data Source
AI summary
A system and method enables the creation of medical images using data related to ghost artifacts. The method thus allows components of an imaged subject to be segmented based on state changes in the components that lead to the controlled production of ghost artifacts. This is achieved in MR by performed a pulse sequence so that multiple sets of MR data are acquired in which the signals from a target tissue vary across the data sets while the signals from a background tissue do not vary across the data sets. A composite data set is generated by populating selected k-space lines of the composite data set with information from a first MR data set and populating the remaining k-space lines of the composite data set with information from a second MR data set. An MR image is then reconstructed from the composite data set. The MR image contains ghost artifacts that faithfully reproduce the 2D or 3D anatomic detail of the target tissues without signal contributions from the background tissues, allowing for background-suppressed or segmented MR images of a target tissue without the need for image subtraction.