Medical Data Evaluation Using Concurrent Preprocessing and Postprocessing
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Solution Overview
Problem
The evaluation of medical data with temporal resolution in medical imaging is complex and time-intensive, requiring multiple steps, including preprocessing and postprocessing, which can be automated but also necessitate user interaction and parameter specification, especially for resting-state functional MRI analysis.
Innovation Solution
A method that separates preprocessing and postprocessing phases, allowing for automated preprocessing of medical data into preprocessed datasets, which are then used for interactive postprocessing, enabling efficient time management and user-guided evaluation with reduced need for real-time user input during data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preprocessing and postprocessing are performed sequentially with full user interaction required, then evaluation accuracy is maintained, but evaluation time increases significantly
Solution Approach 1:
The patent applies preliminary action by performing preprocessing operations (such as motion correction, slice timing correction, and spatial normalization) on medical data datasets before the main postprocessing evaluation. This allows the system to prepare data in advance, so that when postprocessing is needed, the data is already optimized and ready for analysis, thereby reducing the overall evaluation time while maintaining accuracy
Solution Approach 2:
The patent segments the evaluation process into distinct phases: preprocessing (including motion correction, slice timing correction, spatial normalization) and postprocessing (statistical analysis and result generation). This segmentation allows different parts of the process to be optimized independently and enables parallel processing of multiple datasets during preprocessing, reducing total evaluation time while preserving accuracy through systematic processing steps
2Reliability
If complex preprocessing algorithms are applied to all datasets, then data quality is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by selectively applying preprocessing algorithms to datasets based on their specific characteristics and quality metrics. Instead of uniformly processing all datasets with the same level of preprocessing, the system identifies which datasets require motion correction, slice timing correction, or spatial normalization, and applies only the necessary corrections to maintain data quality while reducing overall processing time
Solution Approach 2:
The system performs preliminary quality assessment and metadata extraction from datasets before applying complex preprocessing algorithms. This preliminary action allows the system to identify datasets that already meet quality thresholds and may not require extensive preprocessing, thereby improving processing efficiency while maintaining data quality standards
3Adaptability or versatility
If user parameter specification is required for each processing step, then processing flexibility is maintained, but operational complexity increases
Solution Approach 1:
The patent implements self-service by enabling the preprocessing system to automatically extract metadata from datasets and determine appropriate processing parameters without requiring explicit user input for each step. The system autonomously identifies dataset characteristics, selects appropriate preprocessing algorithms, and configures processing parameters based on the data itself, thereby simplifying operation while maintaining flexibility through adaptive processing
Solution Approach 2:
The patent creates a universal preprocessing framework that can handle multiple types of medical data datasets (fMRI, PET, SPECT) with a single integrated system. The preprocessing module is designed to work with various dataset formats and modalities, automatically adapting its processing approach based on the input data type, thereby providing both flexibility and ease of operation across different applications
Data Source
AI summary
In a method and an evaluation computer for evaluating medical data having a temporal resolution, a preprocessing phase and a postprocessing phase, are executed. The medical data include a first dataset acquired at a first time point of the temporal resolution and a second dataset acquired at a second time point of the temporal resolution. The preprocessing of the first dataset is performed in a first time period and the preprocessed first dataset is provided as an output. The preprocessing of the second dataset is performed in a second time period and the preprocessed second dataset is provided as an output, and an interactive preparation of the postprocessing is carried out in the second time period based on the preprocessed first dataset. The medical data are evaluated by postprocessing using the preprocessed first dataset and/or the preprocessed second dataset.