MR Image Intensity Standardization via Histogram Alignment
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Solution Overview
Problem
Current methods for analyzing magnetic resonance (MR) images are subjective, qualitative, and inefficient, leading to inaccurate diagnostic results and difficulties in comparing images from different acquisition systems and vendors, which hinders the understanding of neurological diseases like Alzheimer's and multiple sclerosis.
Innovation Solution
A system and method for processing MR images through intensity standardization, involving scaling and shifting of image slices to align them with a reference histogram, reducing variability and enabling more accurate comparisons across different imaging protocols and vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual analysis of medical images is performed manually, then diagnostic interpretation can be obtained, but the process is subjective, qualitative, error prone, and inefficient
Solution Approach 1:
The patent replaces manual visual analysis (mechanical human observation) with automated computational image processing algorithms. The system uses digital image processing techniques to objectively measure and quantify image features, eliminating subjectivity and observer-dependent variability while significantly improving analysis efficiency and productivity.
2Adaptability or versatility
If images are acquired from multiple vendors and acquisition systems, then more diverse data can be collected, but variations across images make comparisons difficult
Solution Approach 1:
The patent applies local quality adjustments through histogram matching and intensity standardization techniques. The system processes images from different vendors by aligning their intensity distributions to a reference histogram, ensuring that corresponding tissue types have consistent intensity values across different acquisition systems. This enables accurate comparisons while maintaining compatibility with diverse data sources.
3Adaptability or versatility
If different acquisition parameters and reconstruction algorithms are used, then imaging flexibility is increased, but image variations increase making standardization difficult
Solution Approach 1:
The patent applies parameter changes through histogram transformation and intensity scaling. The system transforms image intensity parameters by matching histograms to a reference distribution, scaling intensity values to compensate for variations caused by different acquisition parameters and reconstruction algorithms. This standardization process maintains imaging flexibility while achieving intensity consistency across different protocols.
Data Source
AI summary
Methods and systems for processing a digital magnetic resonance (MR) image volume in an image data set using intensity standardization to provide standardized MR image slices are described which generally involve determining an image volume scaling factor to align an image volume landmark of a volume histogram of the digital MR image volume with a reference volume landmark of a reference histogram of a reference image volume; and scaling intensity values of the digital MR image volume based on the image volume scaling factor to generate a scaled digital MR image volume. Methods and systems are also described for generating standardized image slices that can be used in various MR image processing methodologies such as image segmentation and object detection.