Vessel Contrast Inference for SIRT Microbead Dosimetry
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Solution Overview
Problem
Existing SIRT dosimetry methods face challenges in accurately determining microbead concentration in liver parenchyma and tumor tissue due to low visibility and diffusion of microbeads, requiring complex dual energy CBCT or subtraction procedures.
Innovation Solution
A method using X-ray imaging and a learned network to estimate microbead concentration in tissue based on visible concentrations in blood vessels, generating a composite distribution map without needing dual energy CBCT or subtraction, utilizing model-based or learning-based algorithms for extrapolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional dual energy CBCT or subtraction procedures are used to determine microbead concentration, then measurement precision is improved, but device complexity and ease of operation worsen due to complicated patient motion compensation and further recording requirements
Solution Approach 1:
The patent extracts only the essential information needed for dosimetry by using a single-energy CBCT scan combined with a learned network that directly estimates microbead concentration from the raw imaging data, eliminating the need for complex dual-energy procedures or subtraction methods while maintaining measurement precision
Solution Approach 2:
The patent uses a learned network trained on paired datasets (raw CBCT images and corresponding microbead concentration maps from gold standard methods) to create a computational model that copies the dosimetric information extraction process, enabling accurate concentration estimation without replicating the complex physical imaging procedures
2Measurement precision
If conventional dual energy CBCT or subtraction procedures are used to determine microbead concentration, then measurement precision is improved, but ease of operation worsens due to complicated patient motion compensation and further recording
Solution Approach 1:
The patent extracts only the essential information needed for dosimetry by using a single-energy CBCT scan combined with a learned network that directly estimates microbead concentration from the raw imaging data, eliminating the need for complex dual-energy procedures or subtraction methods while maintaining measurement precision
Solution Approach 2:
The learned network performs self-service by automatically estimating microbead concentration and generating dosimetric maps directly from the CBCT data without requiring manual patient motion compensation or additional recording steps, making the process operator-friendly while maintaining accuracy
3Loss of information
If imaging averages over voxel size to capture microbeads in parenchyma, then loss of information is reduced, but measurement precision worsens due to low concentrations of microbeads in parenchyma and fine microarteries
Solution Approach 1:
The patent introduces an intermediary computational approach by using a learned network that acts as a mediator between the raw CBCT imaging data and the final microbead concentration measurement, enabling the system to recover microbead distribution information that would otherwise be lost due to averaging effects while maintaining measurement precision through learned patterns from training data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately estimates microbead concentration in liver parenchyma and tumor tissue, providing suitable dosimetric calculations without complex patient motion compensation, enhancing treatment planning for SIRT.
Implementation Method 1
recording 3D data of an organ of interest after microbeads are administered into a patient to flow to the organ
Data Source
AI summary
A method includes recording (S2) 3D data of an organ of interest after administering microbeads into a patient to flow to the organ; segmenting (S3) arterial vascular structures with visible accumulations of the microbeads around the organ; determining (S4) diameters or lumens(s) of the arterial vascular structures; determining or estimating (S5) a degree of a filling factor in the arterial vascular structures; generating (S6) a microbead density map along the arterial vascular structures; extrapolating (S7) the microbead accumulation in parenchyma and/or tumor tissue in the organ; and generating (S8) a composite distribution map of the microbeads, the composite map including the arterial vascular structures with visible accumulations of the microbeads and the extrapolated microbead accumulation in the parenchyma and/or tumor tissue in the organ.