Subject-Specific Hemodynamic Response Function fMRI Analysis
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Solution Overview
Problem
Current clinical techniques for functional Magnetic Resonance Imaging (fMRI) assume a constant hemodynamic response function, which is not accurate for individual subjects, leading to potential inaccuracies in fMRI studies and lacking diagnostic value.
Innovation Solution
A medical system and method that calculates a subject-specific hemodynamic response function by processing a time series of R2-star maps and stimulus signals, using computational systems integrated into various configurations, including cloud-based systems, workstations, and MRI control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a constant hemodynamic response function is assumed for all subjects, then the complexity of fMRI analysis is reduced, but the accuracy and diagnostic value of the results deteriorate
Solution Approach 1:
The patent applies parameter changes by transitioning from a fixed, constant hemodynamic response function to a variable, subject-specific hemodynamic response function. The system determines individual HRF parameters (such as peak time, width, and amplitude) for each subject based on their physiological characteristics, thereby improving measurement precision while managing complexity through automated parameter estimation methods
Solution Approach 2:
The invention implements self-service by enabling the system to automatically determine subject-specific hemodynamic response functions without requiring manual intervention or complex user configuration. The computational system autonomously processes fMRI data, estimates HRF parameters, and generates subject-specific models, reducing the burden on operators while maintaining high accuracy
2Measurement precision
If subject-specific hemodynamic response functions are determined, then the accuracy and diagnostic value improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing fMRI data to extract relevant physiological signals before determining the hemodynamic response function. The system performs initial data cleaning, normalization, and feature extraction to prepare the data for HRF parameter estimation, thereby simplifying the subsequent computational steps and reducing overall processing complexity
Solution Approach 2:
The invention replaces complex mechanical or manual analysis methods with computational algorithms and automated processing systems. Instead of manual HRF parameter determination, the system uses computational models and automated fitting procedures to estimate subject-specific parameters, reducing computational complexity through algorithmic efficiency
3Measurement precision
If subject-specific hemodynamic response functions are determined, then diagnostic capability is enhanced, but the time required for processing increases
Solution Approach 1:
The patent applies partial action by focusing the hemodynamic response function determination on key physiological parameters and critical brain regions rather than analyzing all possible parameters throughout the entire brain. This selective approach maintains diagnostic value while reducing processing time by concentrating computational resources on the most relevant features
Data Source
AI summary
Disclosed herein is a medical system (100, 300) where execution of machine executable instructions (120) causes a computational system (104) to: receive (200) a time series of a R2-star map (122) for a brain volume (500); receive (202) a stimulus signal (124) descriptive of an occurrence of a sensory stimulus; receive (204) a selection of one or more seed voxels (126) identified in the time series of the R2-star map; calculate (206) a denoised time series of the R2-star map (128); calculate (208) a correlation map (130) between the seed voxels and the denoised time series of the R2-star map; determine (210) an activated region (132) of the brain volume using voxels identified in the correlation map; provide (212) a hemodynamic response (134) function for each voxel and each occurrence of the sensory stimulus; and provide (214) a subject specific hemodynamic response function (136) by averaging the hemodynamic response functions.