Imaging Data Compression by Event Reordering and Ring Pair Encoding
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Solution Overview
Problem
Multi-modality imaging systems, such as PET scanners, generate vast amounts of data that increase in size due to advancements in technology, leading to higher processing demands and storage requirements, necessitating an efficient compression method to manage this data effectively.
Innovation Solution
The method involves acquiring a stream of imaging data, dividing it into interstices, classifying detected events based on probability of occurrence, and reordering them using ring pair identification numbers and derived transaxial angles to compress the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technological advances increase detector resolution and data collection capability, then measurement precision and data quality are improved, but data size and storage requirements increase
Solution Approach 1:
The patent segments the raw imaging data into multiple data structures (e.g., separating patient identification data, scan parameter data, and imaging data). This segmentation allows selective compression and efficient storage of different data types, reducing overall data size while preserving measurement precision.
Solution Approach 2:
The patent applies parameter changes by transforming data representation formats, such as converting raw detector signals into compressed data structures with optimized bit allocations. This allows the system to maintain high measurement precision while significantly reducing the quantity of stored data.
2Measurement precision
If data word size increases to accommodate higher resolution detectors and TOF timing precision, then measurement precision is improved, but processing demand and storage cost increase
Solution Approach 1:
The patent segments high-precision data into structured fields with optimized bit allocations. By dividing TOF timing data, detector position data, and event type data into separate structured fields, the system maintains measurement precision while reducing overall processing complexity through organized data access patterns.
Solution Approach 2:
The patent transforms raw high-precision data into compressed representations by changing data parameters and formats. This includes optimizing bit allocations for different data fields and using efficient encoding schemes that maintain timing precision while reducing processing demand.
3Adaptability or versatility
If list files are used to store all detected events for retrospective analysis, then adaptability and analysis capability are improved, but storage space requirements increase
Solution Approach 1:
The patent segments list file data into structured records with specific fields for different types of information. This segmentation enables selective storage and retrieval of data needed for different retrospective analyses, reducing overall storage requirements while maintaining adaptability for various analysis types.
Solution Approach 2:
The patent applies parameter changes by optimizing the data structure of list files, using compressed representations of events with variable-length fields based on data importance. This allows retrospective analysis capability to be maintained with reduced storage space through efficient parameter encoding.
Data Source
AI summary
A method for compressing imaging data includes acquiring a stream of imaging data, and dividing the stream of imaging data into a plurality of interstices, each interstice including a plurality of detected events. The also includes classifying the detected events within each interstice based on a probability of occurrence of the detected event, and reordering the detected events within each interstice based on the probability of occurrence to compress the image data. The method further includes generating a ring pair identification number for each detected event in the plurality of interstices.