Neuroimaging Database Pattern Matching for Brain Disease Analysis
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Solution Overview
Problem
Current neuroimaging technologies face challenges in effectively analyzing and comparing brain scans to diagnose and treat chronic brain diseases, as they lack efficient methods for comparing patient data across different states and treatments, and for identifying patterns indicative of disease progression or treatment efficacy.
Innovation Solution
A system and method utilizing neuroimaging databases that store and analyze brain scans using SPECT or fMRI data, allowing for statistical comparisons and pattern matching to identify deviations in brain perfusion levels, enabling the analysis of chronic brain diseases and treatment effectiveness by comparing patient records with normative data and generating Perfusion Pattern Index files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neuroimaging databases store and analyze brain scans using SPECT or fMRI data, then the ability to diagnose and treat chronic brain diseases is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex neuroimaging analysis into distinct functional modules: data storage module for organizing brain scan data, statistical comparison module for normative analysis, and pattern matching module for treatment effect analysis. Each module handles specific tasks independently, reducing overall system complexity while maintaining diagnostic reliability.
Solution Approach 2:
The patent introduces intermediary computational layers between raw neuroimaging data and clinical decisions. Statistical comparison algorithms serve as intermediaries that transform complex imaging data into standardized deviation metrics, while pattern matching algorithms act as intermediaries that compare individual cases against treatment outcome databases, facilitating more reliable diagnosis without requiring direct complex data processing.
2Measurement precision
If statistical comparisons and pattern matching are used to analyze brain scans, then the precision of disease analysis is improved, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing neuroimaging data into standardized formats and pre-establishing normative databases during system initialization. Statistical comparison parameters and pattern matching templates are prepared in advance, allowing rapid analysis of individual patient cases without requiring extensive computational resources during actual diagnosis.
Solution Approach 2:
The patent transforms complex neuroimaging data into simplified parameter representations through statistical comparison against normative values. By changing the data parameters from raw imaging signals to standardized deviation metrics, the system achieves precise disease analysis with reduced computational requirements for subsequent pattern matching operations.
Data Source
AI summary
Systems for and methods of utilizing a neuroimaging database are presented. The systems and methods include techniques for analyzing the pathophysiological basis of a chronic brain disease and/or the effectiveness of a treatment for a chronic brain disease, obtaining data for research of a chronic brain disease, searching for chronic brain disease symptoms identified in a clinical patient, searching a database by comparing the brain scan images of patients with suspected indications of chronic brain disease with other patients in the database to identify sets of patients with similar indications in their brain scan images, displaying brain scan information regarding a person, and using image pattern matching to analyze the pathophysiological basis of a chronic brain disease and/or the effectiveness of a proposed or previously administered treatment for a chronic brain disease.