AI Medical Image Harmonization for Multi-Vendor MRI Consistency
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Solution Overview
Problem
Medical images acquired from different hardware systems and vendors have varying contrasts, distortions, and formats, posing challenges for radiologists due to increased complexity in processing and analysis, especially with multi-contrast MRI where incomplete or corrupted images are common.
Innovation Solution
A deep learning-based system that automatically detects image quality, adjusts formatting, harmonizes data, and provides a dashboard for insights, using components like a Meta Data Parser, image quality check, and a graphical user interface to standardize and synthesize images, and recommend rescans when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple vendor scanners are used to acquire medical images, then imaging versatility and coverage are improved, but image consistency and processing complexity worsen
Solution Approach 1:
The patent introduces an intermediary processing system that acts as a mediator between multiple vendor scanners and the analysis pipeline. This intermediary layer standardizes images from different vendors by applying consistent processing protocols, harmonizing data formats, and normalizing image characteristics, thereby enabling seamless integration of multi-vendor images without increasing downstream processing complexity
Solution Approach 2:
The system applies parameter changes to harmonize images from different vendors by adjusting imaging parameters, contrast settings, and data formatting to a standardized reference framework. This allows images with varying acquisition parameters to be transformed into a consistent format that can be processed uniformly across all vendor sources
2Loss of information
If multiple contrast-weighted images are acquired, then diagnostic information completeness is improved, but scan time and data management complexity worsen
Solution Approach 1:
The system performs preliminary organization and classification of multiple contrast-weighted images during the acquisition phase, tagging and structuring them according to their contrast type (T1, T2, FLAIR, etc.) and diagnostic relevance. This preliminary action enables efficient retrieval and selective processing of only the necessary contrast images, reducing overall scan time and data management burden while preserving complete diagnostic information
Solution Approach 2:
The patent extracts and separates individual contrast-weighted images into distinct, organized categories, allowing the system to process only the specific contrast images needed for particular diagnostic tasks. This extraction approach prevents unnecessary processing of all available contrasts, thereby reducing time consumption while maintaining access to complete diagnostic information when needed
3Reliability
If image quality checks and standardization processes are implemented, then data consistency is improved, but processing time and computational resources worsen
Solution Approach 1:
The system performs image quality checks, format standardization, and data harmonization as preliminary actions immediately upon image acquisition, before the images enter the main analysis pipeline. By completing these validation and standardization steps upfront, the system ensures data consistency is established early, preventing downstream reprocessing and actually improving overall processing efficiency
Solution Approach 2:
The patent implements self-service mechanisms where the image processing system automatically performs quality assessment, format validation, and standardization without requiring manual intervention. The system self-regulates by detecting image quality issues and applying appropriate corrections or flags, thereby maintaining high data consistency while minimizing the computational overhead associated with manual processing
Data Source
AI summary
Methods and systems are provided for computer-implemented method for providing data-driven insights. The method comprises: receiving input medical images of a subject; utilizing deep learning-based algorithm to determine a quality of the input medical images, standardize a format or name of the input medical images and/or assess a completeness of a protocol associated with acquiring the input medical images; and generating insights based at least in part on the quality of the input medical image pr the completeness of the protocol, and displaying the insights on a graphical user interface (GUI).