Estimating Joint Characteristic Functionals for Dynamic Biological Imaging
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Solution Overview
Problem
Current imaging methods for physiological processes in living organisms, such as PET and SPECT, fail to provide a comprehensive picture due to their limitations in capturing dynamic and unpredictable biological processes, which are better modeled as random physiological processes requiring estimation of joint characteristic functionals.
Innovation Solution
The method involves performing multiple imaging scans of cells or tissues, generating image data, calculating characteristic functionals, and analyzing these to extract information on physical characteristics like molecule presence, quantity, location, binding, conformation, and size, using techniques like fluorescence imaging and emission computed tomography to model biological processes and diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PET or SPECT imaging methods are used to image physiological processes, then images of physiological objects can be obtained, but fine details of tracer distribution are invisible and comprehensive physiological information is lost
Solution Approach 1:
The patent transforms the imaging data from conventional intensity-based parameters to statistical parameters (mean, variance, skewness, kurtosis) by performing multiple scans and analyzing tracer distribution statistics. This parameter transformation enables detection of fine details and dynamic physiological processes that are invisible in conventional single-time-point imaging
Solution Approach 2:
The patent employs periodic imaging scans at multiple time points to capture the dynamic nature of physiological processes. By repeatedly imaging the same subject over time and analyzing the statistical variations in tracer distribution, the method reveals temporal dynamics and fine details that static single-scan imaging cannot detect
2Adaptability or versatility
If conventional imaging methods are used to study biological processes, then basic images can be obtained, but unpredictable and dynamic biological processes cannot be adequately characterized
Solution Approach 1:
The patent replaces conventional deterministic image analysis with a statistical field theory approach. Instead of treating tracer distribution as a fixed spatial pattern, the method models it as a random field characterized by statistical properties (joint characteristic functionals), enabling accurate characterization of unpredictable and dynamic biological processes
Solution Approach 2:
The patent develops a universal statistical framework using joint characteristic functionals that can characterize multiple types of physiological processes (tracer distribution, cellular dynamics, tissue perfusion) within a single theoretical model, making the method adaptable to various dynamic biological processes while maintaining measurement precision
Data Source
AI summary
Many biologic processes taking place inside a living organism are unpredictable in time and space, and cannot be known exactly. These mechanisms and interactions among them are better modeled as physiological random processes, the statistics of which are fully described by joint characteristic functionals. The present invention provides methods for the estimation of joint characteristic functionals through imaging of multiple physiological random processes. This technology can be used to study complex diseases, such as tumors and viral infections, by imaging the biological processes involved with disease progression and response to treatment.