MRI Tumor Growth Quantification via Gray Level Normalization
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Solution Overview
Problem
Current methods for quantifying brain tumor development, particularly low-grade gliomas, face challenges such as variability in image analysis, lack of reproducibility, and the need for multiple MRI examinations, which are costly and stressful for patients, due to differences in contrast and noise levels between images.
Innovation Solution
A method involving normalization of gray levels using a midway cumulative histogram and statistical tests based on Gaussian distributions to compare and analyze changes in MRI images over time, allowing for precise and reproducible tumor growth assessment without repeated examinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation of tumors is performed by doctors on MRI images, then tumor spatial extent can be appreciated at various cross-sectional levels and times, but the analysis is tedious and exhibits lack of reproducibility with intra-operator variability estimated at 15%
Solution Approach 1:
The patent replaces manual mechanical segmentation by doctors with an automated image processing system that uses signal processing techniques to segment tumors in MRI images. This substitution eliminates human variability and provides consistent, reproducible measurements across different operators and time points.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between the raw MRI images and the final tumor measurements. This intermediary process standardizes the segmentation procedure through algorithmic operations, ensuring that the same measurement criteria are applied consistently regardless of which operator performs the analysis.
2Measurement precision
If the largest diameters technique is used to quantify tumor growth, then tumor size can be measured, but the method is unsuitable following surgical operation due to post-operative cavity and is affected by different cross-sectional plane orientations
Solution Approach 1:
The patent changes the measurement parameters from simple linear diameters to a comprehensive volume-based approach that integrates multiple image slices. This parameter transformation allows the system to account for complex tumor shapes and adapt to post-surgical anatomical changes, providing reliable measurements regardless of cross-sectional orientation or surgical intervention.
Solution Approach 2:
The patent transitions from two-dimensional diameter measurements to three-dimensional volume measurements by integrating information across multiple cross-sectional image slices. This dimensional expansion enables the system to capture the full spatial extent of tumors and distinguish actual tumor tissue from post-surgical cavities through volumetric analysis.
3Extent of automation
If automatic tumor segmentation methods based on complex calculation and image processing algorithms are used, then tumor development can be determined automatically, but these methods offer relatively poor reliability and robustness due to non-linear changes in contrast between images
Solution Approach 1:
The patent applies normalization techniques that equalize the contrast levels across different MRI images taken at different times. By transforming the images to a common contrast reference frame, the system eliminates the non-linear contrast variations that would otherwise cause unreliable automatic segmentation, enabling consistent tumor boundary detection throughout the monitoring period.
4Measurement precision
If normalization techniques are used to make images comparable by learning longitudinal anatomical variabilities in noise, then image comparison is improved, but repeated MRI examinations are required to produce precise noise level cartographies, increasing cost and patient stress
Solution Approach 1:
The patent performs noise characterization and normalization using the initially acquired MRI images without requiring additional repeated examinations. The system learns the noise and anatomical variability characteristics from the available images and applies corrections to make them comparable, thereby eliminating the need for extra scanning sessions and reducing patient burden while maintaining measurement precision.
Data Source
AI summary
A method for quantifying the development of pathologies involving changes in volume of a body represented via an imaging technique, including normalizing gray levels by a midway technique for two images I1 and I2 representing the same scene, resulting in two normalized images I′1 and I′2; calculating a map of signed differences between the two normalized images I′1 and I′2; and performing one or more statistical tests based on the assumption of a Gaussian distribution of the gray levels for healthy tissues in the normalized images I′1 and I′2 and/or in the calculated difference map. Advantageously, results of two or more of the tests can be combined for a more specific characterization of the development.