PET Raw-Scan Correction With Synthetic Lesion Response Models
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Solution Overview
Problem
Existing medical imaging systems, particularly PET imaging, face challenges in comparing lesion recovery values due to varying parameters such as patient dose, condition, acquisition system, and reconstruction techniques, leading to inconsistent and inaccurate quantification across different scans.
Innovation Solution
A data-driven method that involves inserting synthetic raw scan data with known lesion values into original scan data, reconstructing both sets of data separately, extracting information, determining a system response specific to the imaging system and technique, and using this response to correct the original scan data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different acquisition and reconstruction techniques are used, then system flexibility and adaptability improve, but measurement precision and quantification consistency deteriorate
Solution Approach 1:
The patent introduces a system response model as an intermediary that mediates between different acquisition/reconstruction techniques and the final lesion quantification. This model characterizes the specific response of each imaging system and technique combination, allowing measurements from different systems to be compared on a common basis. The system response acts as a transfer function that can be measured and used to correct quantification across varying conditions.
Solution Approach 2:
The patent changes the parameter representation by introducing system response parameters that describe how each imaging system transforms the true lesion values into measured values. By characterizing these transformation parameters for different systems and techniques, the patent enables consistent comparison despite variations in acquisition and reconstruction parameters.
2Adaptability or versatility
If multiple varying parameters (patient dose, condition, acquisition system, reconstruction parameters) are used, then system versatility and clinical adaptability improve, but measurement precision and result comparability deteriorate
Solution Approach 1:
The system response model serves as a mediator that accounts for all varying parameters (patient dose, condition, acquisition system, reconstruction parameters) by characterizing their combined effect on lesion quantification. This intermediary model allows the system to adapt to different clinical conditions while maintaining measurement precision through systematic correction.
Solution Approach 2:
The patent employs feedback by using the measured system response to correct future measurements. The system response is determined from reference data and then used to adjust and correct lesion values from subsequent scans, creating a feedback loop that continuously improves measurement accuracy across varying clinical conditions.
3Manufacturing precision
If system-specific reconstruction techniques are used, then optimization for specific systems improves, but universal comparability and standardization deteriorate
Solution Approach 1:
The system response model acts as an intermediary layer between system-specific reconstruction techniques and cross-system comparison. Each system's unique reconstruction characteristics are captured in its specific response model, which then serves as a bridge to enable standardized comparison across different systems by translating all measurements to a common reference framework.
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
This approach enables accurate quantification and harmonization of lesion values across different imaging systems and techniques, allowing for robust comparison and correction of system biases, thereby improving the accuracy of PET imaging results.
Implementation Method 1
positron emission tomography (PET) may utilize a radiopharmaceutical that is administered to a patient and whose breakdown results in the positron emission of gamma rays
Implementation Method 2
the radiopharmaceutical breaks down or decays within the patient, releasing a positron which annihilates when encountering an electron and produces a pair of gamma rays
Implementation Method 3
When these photons arrive and are detected at the detector elements at the same or nearly the same time, this is referred to as coincidence or coincidence event (COIN)
Data Source
AI summary
A method includes obtaining raw scan data from a clinical scan of a subject with a medical imaging system. The method includes inserting synthetic raw scan data with one or more known lesion values into the raw scan data to generate modified raw scan data. The method includes separately reconstructing the raw scan data and the modified raw scan data to respectively generate a first reconstructed image and a second reconstructed image. The method includes extracting information from the first reconstructed image and the second reconstructed image. The method includes determining a system response to the inserted synthetic raw data based on the extracted information and one or more target lesion values, and wherein the system response is specific to the medical imaging system and a reconstruction technique utilized by the medical imaging system. The method includes utilizing the system response to correct the raw scan data.