Projection Data Inconsistency Assessment Using Center-of-Light Fluctuations
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Solution Overview
Problem
Patient motion during emission imaging acquisition leads to data inconsistency in projection images, resulting in poor-quality three-dimensional images that hinder accurate diagnosis and treatment, with conventional subjective assessment methods being unreliable and technician-dependent.
Innovation Solution
A system that determines the data quality of projection images by calculating fluctuations in center-of-light locations between successive images, using a mapping or artificial neural network to quantify data inconsistency, allowing for efficient evaluation and potential re-scanning before image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a technician visually assesses data inconsistency among projection images, then the assessment can be performed prior to image reconstruction, but the results are subjective and technician-dependent, lacking consistency and reliability
Solution Approach 1:
The patent replaces the manual visual assessment mechanism with an automated computational system. The system uses processing units to execute algorithms that calculate data inconsistency metrics from projection images, substituting the technician's subjective visual evaluation with objective computational analysis. This automation eliminates technician-dependency while maintaining the ability to assess data quality before image reconstruction.
2Quantity of substance
If projection images are acquired over a timespan of several hours, then sufficient data can be collected for three-dimensional image reconstruction, but patient motion during acquisition leads to data inconsistency
Solution Approach 1:
The patent performs preliminary assessment of data consistency before proceeding to image reconstruction. By calculating inconsistency metrics from the acquired projection images first, the system identifies data quality issues early, allowing for potential re-scanning before the patient is released. This preliminary action prevents wasting computational resources on reconstructing images from inconsistent data.
Solution Approach 2:
The system provides feedback on data quality by quantifying inconsistency among projection images. This feedback mechanism allows operators to understand the quality of acquired data and make informed decisions about whether to proceed with reconstruction or acquire additional data, creating a closed-loop quality control process.
3Productivity
If three-dimensional images are reconstructed from inconsistent projection images, then the reconstruction process can be completed, but the resulting images exhibit poor quality that hinders accurate diagnosis
Solution Approach 1:
The patent performs preliminary quality assessment of projection images before initiating the computationally intensive reconstruction process. By evaluating data consistency metrics in advance, the system prevents unnecessary reconstruction of poor-quality data, saving computational resources and time while ensuring that only adequate data proceeds to reconstruction.
Solution Approach 2:
The system performs self-assessment of data quality using automated algorithms that evaluate inconsistency metrics. This self-service capability allows the system to independently determine whether acquired data is sufficient for reconstruction without requiring manual intervention, thereby maintaining productivity while ensuring image quality standards are met.
Data Source
AI summary
A system and method include acquisition of a plurality of projection images of a subject, each of the projection images associated with a respective projection angle, determination, for each of the projection images, of a center-of-light location in a first image region, determination of a local fluctuation measure based on the determined center-of-light locations, and determination of a quality measure associated with the plurality of projection images based on the local fluctuation measure.