Quantitative PET Imaging With Automated Attenuation Map Correction
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Solution Overview
Problem
Attenuation maps in PET imaging often contain artifacts that can lead to faulty quantification of PET images, particularly in PET/MRT systems, due to user reliance on expertise and increased time and cost for correction, which is exacerbated by lack of specialist knowledge among technicians.
Innovation Solution
A method involving two evaluating units to assess and correct attenuation maps by detecting artifacts using reference data and trained algorithms, allowing for real-time correction during the imaging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attenuation maps are generated using MRT or CT data, then quantitative PET images can be obtained, but artifacts may occur in the attenuation maps leading to faulty quantification
Solution Approach 1:
The system performs preliminary assessment of the attenuation map for artifacts before using it for PET quantification. The first evaluating unit checks the attenuation map in advance, and if artifacts are detected, the system generates a corrected attenuation map or notifies the user, preventing faulty quantification from occurring in the first place.
Solution Approach 2:
The system implements a feedback mechanism where the attenuation map is automatically assessed by evaluating units that detect artifacts and trigger correction processes. This closed-loop feedback ensures that only artifact-free or corrected attenuation maps are used for quantitative PET imaging, resolving the contradiction between obtaining quantitative images and avoiding artifacts.
2Reliability
If users manually assess and compare corrected and uncorrected images, then artifacts can be detected, but this requires specialized expertise and increases time and effort
Solution Approach 1:
The system performs self-assessment of the attenuation map for artifacts using automated evaluating units with trained algorithms. This eliminates the need for users to manually assess and compare images, making the system independent of user expertise while maintaining high reliability in artifact detection.
Solution Approach 2:
The manual mechanical process of user assessment and comparison is replaced with an automated electronic evaluation system using algorithms and AI models. This substitution reduces operational complexity and eliminates the need for specialized user expertise while improving consistency and reliability in artifact detection.
3Measurement precision
If artifacts are detected late and the scan is repeated, then quantification accuracy can be improved, but this increases time and cost
Solution Approach 1:
The system performs preliminary assessment of the attenuation map immediately after its generation, before the PET quantification process begins. This early detection prevents the need for repeating scans, saving time and resources while ensuring quantification accuracy is maintained from the start.
Solution Approach 2:
The system prepares corrected attenuation maps in advance using AI-based correction algorithms, cushioning against potential quantification errors before they occur. This proactive approach ensures that even if artifacts are present, they are corrected beforehand, eliminating the need for time-consuming scan repetitions.
Data Source
AI summary
A method for creating a quantitative positron emission tomography (PET) image of a subject or object comprises recording correction information data with an imaging device; transmitting the correction information data to a first evaluating unit which includes at least one of preparing an attenuation map on based on the correction information data and passing on the attenuation map as checking data to a second evaluating unit or sending the correction information data as checking data to the second evaluating unit; assessing the checking data based on a quantity of reference data and detecting possible artifacts in the checking data by way of the second evaluating unit; at least one of initializing correction measures by way of the second evaluating unit or proposing correction measures to a user; and preparing a corrected attenuation map based on the correction measures.