Dynamic PET Parameter Estimation with Tissue Compartment Models
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Solution Overview
Problem
Conventional PET parameter determination methods are computationally expensive due to the high number of pixel points in whole-body PET scans and require tracer equilibrium, leading to significant parameter estimation errors.
Innovation Solution
A method involving PET scanning data acquisition, image reconstruction, tissue compartmental model determination, and linear estimation to calculate dynamic parameters such as flow velocity and net inflow rate, using tracer identifiers and activity addition expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional nonlinear estimation methods are used for determining PET parameters, then parameter estimation accuracy is maintained, but computational cost increases significantly due to the high number of pixel points in whole-body PET scans
Solution Approach 1:
The patent transforms the parameter estimation approach by changing from nonlinear estimation methods to linear estimation methods. This parameter change in the estimation algorithm fundamentally reduces computational complexity while maintaining accuracy, directly addressing the contradiction between computational cost and measurement precision.
Solution Approach 2:
The patent segments the PET parameter determination process into multiple stages: image reconstruction, tissue compartmental model determination, and linear estimation. This segmentation allows each stage to be optimized independently, reducing the overall computational burden while maintaining precision through the linear estimation stage.
2Use of energy by moving object
If linear regression of graphical estimation methods is used for PET parameter determination, then computational cost is reduced, but parameter estimation errors increase significantly when tracer equilibrium is not achieved
Solution Approach 1:
The patent replaces the traditional linear regression approach with a linear estimation method based on tissue compartmental models. This substitution maintains the computational efficiency of linear methods while eliminating the dependency on tracer equilibrium, thereby avoiding significant parameter estimation errors.
Solution Approach 2:
The patent introduces tissue compartmental models as an intermediary between the PET imaging data and the parameter estimation. This intermediary structure allows linear estimation to proceed without requiring tracer equilibrium, mediating between the computational efficiency need and the accuracy requirement.
3Measurement precision
If whole-body PET scanning is performed with high quality dynamic images, then parameter estimation accuracy is improved, but the number of pixel points increases leading to higher computational cost
Solution Approach 1:
The patent changes the estimation approach from nonlinear to linear methods, which fundamentally alters how the large number of pixel points are processed. This parameter change in the estimation algorithm allows efficient handling of the high-dimensional data from whole-body PET scans without proportionally increasing computational cost.
Solution Approach 2:
The patent employs dynamic tissue compartmental models that adapt to the specific characteristics of each pixel and tissue region. This dynamic approach allows the system to handle the variability in whole-body PET data efficiently, maintaining accuracy while managing computational complexity through adaptive modeling rather than uniform processing.
Data Source
AI summary
Disclosed are a PET parameter determination method and apparatus, and a device and a storage medium. Comprises: extracting a tracer identifier from the PET scanning data; performing image reconstruction on the PET scanning data, so as to obtain a PET image set; according to the PET image set, determining a sampling time activity curve corresponding to each pixel, and according to the tracer identifier and a pre-created correlation between a tracer identifier and a tissue compartmental model, determining a tissue compartmental model corresponding to the sampling time activity curve; on the basis of the tissue compartmental model, modifying an activity addition expression corresponding to the intensity of each pixel point corresponding to the tissue compartmental model, so as to update the activity addition expression; and according to the updated activity addition expression, determining the numerical value of at least one dynamic parameter corresponding to the PET image set.