Medical Image Data Compression via Inversion
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Solution Overview
Problem
The existing methods for processing medical image data, particularly in magnetic resonance tomography, face challenges in efficiently managing increasing data sizes and maintaining high image quality while reducing storage requirements.
Innovation Solution
The method involves using Compressed Sensing (CS) techniques to acquire and store only compressed raw data and processing data, which are used to generate output data, thereby reducing storage space and preserving high image quality by displacing the compression step into the data acquisition phase, and storing these data using various forms of processing data such as loading modules, source code, intermediate code, or platform-independent algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compressed data storage is used to reduce storage space, then storage capacity efficiency is improved, but image quality may deteriorate due to lossy compression
Solution Approach 1:
Instead of compressing the final image data, the patent inverts the approach by compressing the raw acquisition data before image reconstruction. This allows lossless compression at the data acquisition stage while preserving full image quality, as the compression is applied to the unprocessed signal rather than to reconstructed images that would suffer from quality degradation.
Solution Approach 2:
The patent applies compression in advance during the data acquisition phase, before image reconstruction and processing. By performing lossless compression on the raw data immediately after acquisition, the system reduces storage requirements while maintaining the ability to reconstruct high-quality images later without any quality loss from compression artifacts.
2Manufacturing precision
If all processed output data are archived to ensure high image quality, then image quality is maintained, but storage requirements increase significantly
Solution Approach 1:
The patent extracts and stores only the essential compressed raw data and processing data required for image reconstruction, rather than archiving all processed output data. This selective extraction of critical data elements enables significant storage reduction while maintaining the capability to generate high-quality images on demand through reconstruction from the compressed raw data.
Solution Approach 2:
The patent changes the data representation parameters by storing compressed raw data in a compact format with associated processing instructions, rather than storing fully processed image data. This parameter transformation allows the system to maintain image quality through reversible reconstruction while dramatically reducing the storage footprint by storing data in a more efficient compressed state.
3Quantity of substance
If compressed raw data are stored instead of processed data, then storage efficiency is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces processing data as an intermediary element that bridges the compressed raw data and the final image output. These processing data contain the necessary instructions and parameters for reconstruction, acting as a mediator that simplifies the overall process by providing a clear pathway from compressed data to high-quality images without requiring complex ad-hoc processing algorithms.
Data Source
AI summary
A method to process medical image data has the following features. Immediately compressed raw data are acquired by an imaging medical technology apparatus. The compressed raw data are stored. In addition to the compressed raw data, processing data are stored which are provided to generate output data from the compressed raw data, wherein the file size of the compressed raw data and the processing data in total is less than the file size of the output data.