CT Slip Ring Data Compression With Packet Error Protection
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Solution Overview
Problem
Current CT imaging systems face challenges in efficiently compressing and transmitting large volumes of data while maintaining data integrity, as existing compression algorithms are sensitive to corruption and may not function effectively during low compression periods, leading to potential data loss and insufficient bandwidth for high-resolution scans.
Innovation Solution
A method and system architecture for compressing and decompressing data using a compressor and decompressor system with a slip ring, employing Golomb-Rice encoding and error protection to ensure data integrity and adapt to varying compression ratios, allowing for efficient data transfer even during low compression periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression algorithms are used to prevent data loss, then data integrity is improved, but compression ratio deteriorates
Solution Approach 1:
The patent segments the data transmission into multiple packets with error correction codes (ECC) added to each packet. This allows the compression algorithm to work on the original data without adding redundant error protection data to the entire compressed stream, thereby maintaining both high compression ratios and data integrity through packet-level error correction.
Solution Approach 2:
The patent applies error correction coding before compression, and incorporates error detection and correction mechanisms into the compression algorithm itself. This preliminary preparation of data with error protection capabilities allows the system to achieve lossless compression while maintaining high compression ratios, as the error protection is integrated rather than added as separate overhead.
2Reliability
If error detection and correction schemes are added to protect against data corruption, then reliability is improved, but data volume increases reducing compression effectiveness
Solution Approach 1:
The patent divides compressed data into packets and applies error correction codes at the packet level rather than adding comprehensive error protection to the entire data stream. This segmentation allows error protection to be applied efficiently without significantly increasing overall data volume, maintaining compression effectiveness while providing robust corruption protection.
Solution Approach 2:
The patent uses Golomb-Rice encoding with variable parameters that can be adjusted based on data characteristics. By changing encoding parameters dynamically, the system optimizes compression ratios while integrating error protection mechanisms that adapt to the data being compressed, preventing significant increases in data volume.
3Loss of substance
If compression algorithms encode pixels based on multiple past pixels, then compression ratio is improved, but sensitivity to data corruption increases
Solution Approach 1:
The patent segments the compressed data stream into packets with embedded error correction codes. When data corruption occurs, the error correction mechanisms can identify and correct errors within individual packets, preventing corruption from propagating through the entire decompressed image sequence. This maintains the benefits of predictive coding while reducing sensitivity to corruption.
Solution Approach 2:
The patent incorporates error detection and correction capabilities into the compression algorithm itself, preparing the data stream with protection mechanisms before transmission. This preliminary error protection allows the system to use aggressive predictive coding for high compression ratios while having built-in safeguards against data corruption affecting multiple pixels.
4Productivity
If high compression ratios are achieved to reduce bandwidth requirements, then productivity is improved, but reliability deteriorates due to increased sensitivity to corruption
Solution Approach 1:
The patent segments compressed data into packets with error correction codes, allowing high compression ratios to be maintained while providing robust error protection at the packet level. This segmentation ensures that bandwidth efficiency is improved through compression while reliability is maintained through localized error correction that prevents corruption from affecting large portions of the reconstructed image.
Data Source
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AI summary
Disclosed herein are methods and systems for transferring data from a rotating data source such as an X-ray scanner to a stationary target such as a central processing unit over a slip ring. Data received from the data source is compressed on the rotor by a compressor attached to a memory buffer for data retention in the event that the slip ring capacity is below the required data rate. On the stationary platform a decompressor resides which may decompress data back into its original form and transmit it to the target over a network. A memory buffer is attached to the decompressor for the event that data needs to be accumulated when the network or target cannot handle the rate of data to be sent. The compressor comprises of a predictor unit, an entropy encoder unit and an error protector unit. The predictor may use stored data as reference from a set of frames and locations within them. The encoding of the locations may be by means of a Golomb- Rice encoded integer. The entropy encoder may encode differences by means of Golomb- Rice encoding with a parameter K which may vary for each group of data units or it may keep data in its original format for purposes of data reduction or error protection. The error protector may protect parameters inserted into the data stream by means of an error protection code while most of the data can remain without protection.