Longitudinal Deviation Map for Brain Volumetric Change Assessment
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Solution Overview
Problem
Current methods for measuring volumetric changes in brain structures are time-consuming and require extensive interpretation of tabular reports, lacking a concise and precise way to visualize and quantify changes across multiple structures simultaneously, which hinders efficient clinical assessment of neurological disorders.
Innovation Solution
A method and system that generate a longitudinal deviation map using a dense 3D voxel dataset, comparing individual patient data to a reference model, allowing for automatic determination and visualization of volumetric changes in brain structures, enabling fast and precise analysis of volumetric changes across the whole brain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tabular reports of annualized percent change values for each segmented brain structure are used, then comprehensive quantitative information is provided, but the analysis becomes time-consuming and complex
Solution Approach 1:
The patent segments the complex tabular data into a visual map where each brain structure's volumetric change is represented as a distinct visual element. The longitudinal deviation map divides the brain into multiple structures, with each structure's deviation from reference values visually encoded, allowing clinicians to simultaneously assess multiple structures without manually scanning extensive tables.
Solution Approach 2:
The patent creates a visual copy or representation of the volumetric change data through the longitudinal deviation map. Instead of presenting raw numerical data, the system generates a visual map that copies and transforms the quantitative information into an intuitive spatial representation, preserving the quantitative accuracy while dramatically improving interpretability and reducing analysis time.
2Measurement precision
If multiple brain structures are analyzed individually with separate volumetric values, then detailed structure-specific information is obtained, but the overall pattern recognition becomes difficult
Solution Approach 1:
The patent merges multiple individual structure assessments into a single integrated longitudinal deviation map. All brain structures are displayed simultaneously in their anatomical context, with each structure's volumetric change visually represented. This merging allows clinicians to recognize overall patterns of atrophy or hypertrophy across the brain while preserving structure-specific quantitative information.
Solution Approach 2:
The patent adds a visual dimension to the data representation by creating a spatial map where volumetric changes are encoded as visual properties (such as color intensity or size) overlaid on the brain anatomy. This dimensional transformation converts one-dimensional numerical values into two-dimensional visual patterns, enabling intuitive pattern recognition across multiple structures while maintaining structure-specific precision.
3Loss of information
If extensive tabular data and trending plots are provided for volume changes, then complete reference information is available, but the clinical interpretation becomes complex and time-consuming
Solution Approach 1:
The patent uses color encoding to represent volumetric deviation values in the longitudinal deviation map. Different colors or color intensities indicate different magnitudes or directions of deviation from reference values, allowing clinicians to quickly interpret the clinical significance of volume changes without reading numerical tables. This visual encoding maintains complete information while dramatically improving interpretation ease.
4Measurement precision
If traditional volumetric measurement methods are used, then accurate volume calculations are achieved, but the visualization and comparison with reference populations is inefficient
Solution Approach 1:
The patent introduces the longitudinal deviation map as an intermediary between raw volumetric measurements and clinical interpretation. This intermediate visual representation automatically compares patient data with reference populations and presents the results in an intuitive format, eliminating the need for manual comparison and interpretation of extensive tabular data while preserving volumetric measurement accuracy.
Data Source
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AI summary
System and method for measuring volumetric changes of brain structures, the method comprising: i. initializing (101) an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0; ii. for all voxels that belongs to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing (102) the following steps: - calculating (103) a deviation D of a volume change for the segmented brain structure from the longitudinal reference model; - normalizing (104) the deviation D to obtain a quantitative value Q of the volume change on a same scale for voxel's belonging to different brain structures; - sets (107) the intensity value of the voxels to the previously obtained quantitative value Q; - displaying (108) the voxels of the 3D voxel dataset in a longitudinal deviation map.