Tissue Similarity Mapping for MS Lesion Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current magnetic resonance (MR) imaging techniques struggle to accurately identify and differentiate tissues with similar vascular responses, particularly in conditions like multiple sclerosis, where traditional methods fail to fully leverage temporal information.
Innovation Solution
The method involves processing time-resolved MR imaging data to generate a tissue similarity map (TSM) by comparing the temporal behavior of a reference region to other regions, using metrics like mean square error to identify similar tissue behavior and generate comparison data indicative of tissue similarity, which can help in diagnosing and segmenting tissues based on vascular responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MR imaging techniques are used, then imaging is noninvasive and useful, but the ability to accurately identify and differentiate tissues with similar vascular responses is poor
Solution Approach 1:
The patent applies preliminary action by processing the time-resolved MR imaging data before final tissue identification. Specifically, the system compares temporal behavior of reference regions to test regions, calculates similarity metrics (such as mean square error), and generates comparison data that highlights tissue similarities before final diagnosis. This preliminary processing of temporal information enables accurate identification of tissues with similar vascular responses that would be invisible to traditional single-time-point imaging.
2Measurement precision
If time resolved MR imaging data is processed to generate tissue similarity maps, then tissue differentiation capability is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the imaging process into distinct functional components: (1) acquiring time-resolved MR images, (2) selecting reference regions and test regions, (3) comparing temporal behavior between regions, (4) calculating similarity metrics, and (5) generating tissue similarity maps. This segmentation of the processing workflow into manageable steps reduces the perceived complexity while maintaining high tissue differentiation capability. Each step can be implemented with standard computational tools.
Solution Approach 2:
The patent uses comparison data as an intermediary between the raw time-resolved MR imaging data and the final tissue similarity maps. The comparison data, generated by calculating similarity metrics between reference and test regions, serves as a mediator that simplifies the complex temporal information into actionable insights. This intermediary representation makes the processing more manageable and the results more interpretable.
3Reliability
If traditional imaging techniques are used for diagnosing conditions like multiple sclerosis, then diagnostic process is simple, but diagnostic accuracy is insufficient
Solution Approach 1:
The patent implements feedback by using the generated tissue similarity maps to validate and refine the diagnostic process. The comparison data and similarity metrics provide feedback about tissue characteristics that can be used to confirm or adjust diagnostic conclusions. This feedback mechanism enhances diagnostic accuracy by allowing iterative refinement of the diagnostic process while maintaining ease of operation through automated processing.
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 effectively identifies tissues with similar vascular responses, enhancing the detection of lesions and improving diagnostic accuracy by providing high contrast and signal-to-noise ratios, particularly in conditions like multiple sclerosis, and can be used to differentiate between various tissues and blood vessels.
Implementation Method 1
Magnetic resonance (MR) imaging is a useful noninvasive method for imaging the internal components of a wide array of objects
Implementation Method 2
the echo based imaging sequence includes at least one from the list consisting of: an echo planar scan and a gradient echo scan
Implementation Method 3
for each time, Fourier transforming the corresponding data to generate an image in the series of MR images
Data Source
AI summary
A method is disclosed including: receiving a time resolved series of magnetic resonance (MR) images of an imaged region of a subject; processing the images to generate comparison data by comparing a temporal behavior of a reference region of the MR images to at least one other region of the MR images; an generating an output based on the comparison data. The method may be applied in a variety of contexts, including perfusion weighted imaging, determination of T2*, and other time series functions.