Medical Imaging Processing Datasets for Multi-Energy CT Visualization
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Solution Overview
Problem
In medical imaging, particularly with multi-energy computed tomography, evaluation algorithms struggle to process diverse visualization formats, limiting the user's ability to select desired visualization formats and compromising diagnostic capabilities due to the need for separate training for each format and the inability to handle formats with clinical benefits like vessel visualization without contrast agents.
Innovation Solution
A computer-implemented method that receives user information for desired visualization formats, determines suitable processing datasets, and applies evaluation algorithms to these datasets, allowing for the generation of evaluation information that can be combined with the original dataset for enhanced visualization and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If evaluation algorithms are trained on a generalized data pool for multi-energy computed tomography, then the algorithms can be applied broadly, but they cannot adequately handle the diversity of visualization formats (such as monoenergetic images, material decomposition, and material discrimination)
Solution Approach 1:
The patent segments the processing datasets into multiple variants, each optimized for specific visualization formats (e.g., monoenergetic images, material decomposition, material discrimination). Instead of using a single generalized dataset, the system creates specialized processing datasets that can be selectively applied based on the desired evaluation task, thereby maintaining both broad applicability and task-specific precision.
Solution Approach 2:
The patent introduces dynamic selection of processing datasets based on the specific evaluation algorithm and visualization format requirements. The system adaptively chooses which processing dataset to use depending on the clinical question and algorithm being applied, allowing the data representation to change dynamically rather than remaining static across all applications.
2Measurement precision
If separate training is performed for each visualization format, then reliable results are obtained for that specific format, but the effort and resource investment required becomes prohibitively large
Solution Approach 1:
The patent creates a universal framework where a single evaluation algorithm can be applied across multiple visualization formats by using multiple processing datasets. Instead of training separate algorithms for each format, the system makes one algorithm universally applicable through data transformation, significantly reducing training time and resource investment while maintaining reliability.
Solution Approach 2:
The patent changes the parameters of the input data (processing datasets) rather than changing the evaluation algorithm itself. By transforming the same underlying image data into different processing datasets with different characteristics, the system adapts to various visualization formats without requiring separate algorithm training for each format.
3Reliability
If the acquisition mode is fixed to produce similar image properties, then evaluation algorithms can be trained effectively, but the user's ability to select diverse visualization formats is limited
Solution Approach 1:
The patent performs preliminary processing of the acquired image data to create multiple processing dataset variants before the evaluation algorithm is applied. By pre-computing different representations (monoenergetic images, material decomposition results, etc.), the system ensures that when the algorithm runs, it has access to appropriately formatted data for various visualization formats, maintaining training effectiveness while enabling user flexibility.
Solution Approach 2:
The patent introduces processing datasets as intermediary representations between the raw acquired image data and the evaluation algorithm. These intermediaries transform the data into formats suitable for different visualization purposes while maintaining the underlying information integrity, allowing the algorithm to work reliably across diverse formats without direct exposure to format variations.
4Reliability
If users are restricted to visualization formats suitable for evaluation algorithm input, then reliable diagnostic assessment is possible, but the full scope of spectral X-ray imaging possibilities cannot be utilized
Solution Approach 1:
The patent merges multiple processing datasets with different characteristics into a unified evaluation framework. By combining the strengths of different data representations (e.g., combining material decomposition data with monoenergetic image data), the system enables reliable diagnostic assessment while simultaneously utilizing the full scope of spectral X-ray imaging capabilities for enhanced productivity.
Data Source
AI summary
A computer-implemented method for a medical imaging device is disclosed. In an embodiment, the computer-implemented method includes receiving at least one item of user information describing a desired visualization format of the image dataset; providing at least one item of request information; describing required input data of at least one evaluation algorithm to be used; determining at least one first processing dataset corresponding to the visualization format according to the user information and at least one second processing dataset usable as input data for the respective evaluation algorithm according to the request information; applying the at least one evaluation algorithm to the respective second processing dataset to determine evaluation information and outputting the first processing dataset and the evaluation information.