MRI Diffusion Weighted Image Processing for Quantitative Parameter Extraction
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face challenges in accurately diagnosing cancer using diffusion imaging due to issues like T2 shine through and direction-dependent contrast, leading to increased interpretation load and potential false diagnoses, especially when dealing with large volumes of data.
Innovation Solution
An image processing apparatus and method that calculates and displays diffusion coefficients and fractional anisotropy indices from diffusion-weighted image data, allowing for reduced interpretation load and improved diagnostic efficiency by selecting and compressing image information, and providing quantitative images that are less dependent on coordinate systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffusion-weighted imaging with MPG pulse is used to obtain DWI, then diffusion contrast is emphasized, but T2 shine through and direction-dependent contrast cause erroneous interpretation
Solution Approach 1:
The patent changes the parameter representation from signal intensity (which is affected by T2 shine through and direction dependency) to diffusion coefficient ADC values. This transformation converts the DWI data into a quantitative parameter that is independent of T2 weighting and MPG direction, thereby resolving the contradiction between obtaining diffusion contrast and avoiding erroneous interpretation.
2Area of stationary object
If whole body volume data is acquired for cancer screening, then comprehensive coverage is achieved, but data volume and interpretation load increase significantly
Solution Approach 1:
The patent extracts the essential diagnostic information by calculating ADC values from the diffusion-weighted images. This extraction process converts large volumes of complex DWI data into condensed quantitative parameter maps that retain the diagnostic information while dramatically reducing the data burden on clinicians for interpretation.
3Adaptability or versatility
If quantitative parameter images like ADC are generated, then coordinate-system independence is achieved, but additional data processing is required
Solution Approach 1:
The patent performs the computationally intensive ADC calculation and parameter extraction as a preliminary processing step immediately after data acquisition. By completing this transformation beforehand, the system produces ready-to-use quantitative parameter images that are coordinate-system independent and require minimal additional processing during clinical review, thus resolving the productivity concern.
Data Source
AI summary
An image processing apparatus includes a storage unit, a specifying unit, a calculation unit and a display unit. The storage unit stores diffusion weighted image data. The specifying unit specifies a calculation target region on the diffusion weighted image data. The calculation unit calculates at least one of a diffusion coefficient and a fractional anisotropy serving an index of diffusion anisotropy with regard to the calculation target region based on the diffusion weighted image data. The display unit displays at least one of the diffusion coefficient and the fractional anisotropy calculated by the calculation unit.