CT Reconstruction Image Compression via Voxel Density Thresholding
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Solution Overview
Problem
Conventional volumetric density files generated by CT scanners are extremely large, limiting storage capacity, portability, and processing efficiency due to their massive size.
Innovation Solution
A computer-implemented method and system for compressing CT reconstruction images by receiving a volumetric density file, replacing voxel density values based on air and material density thresholds, determining voxels of interest, quantizing voxels, and compressing the reduced volume image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional volumetric density files are stored with full precision (16-bit per voxel), then image data integrity is maintained, but storage space requirements become extremely large (16 Gb for 2000×2000×2000 voxels)
Solution Approach 1:
The patent applies parameter changes by modifying the precision of voxel density values from full 16-bit precision to reduced precision (e.g., 8-bit or lower). This is achieved through quantization techniques that map the full range of density values to a reduced set of discrete levels, thereby reducing storage requirements while maintaining sufficient image quality for diagnostic purposes.
Solution Approach 2:
The patent implements local quality by applying different precision levels to different regions of the volumetric data. Critical regions containing anatomical structures of interest are retained at higher precision, while less critical regions (such as uniform background areas or regions with minimal diagnostic value) are compressed to lower precision. This selective approach maintains diagnostic integrity where needed while reducing overall storage requirements.
2Adaptability or versatility
If large volumetric density files are transferred to another machine or location, then processing can be performed on different systems, but transfer time and data transmission bandwidth requirements increase significantly
Solution Approach 1:
The patent reduces the data transmission burden by changing the precision parameter of the volumetric data before transfer. By quantizing the density values to fewer bits per voxel, the total data volume is reduced, enabling faster transfer across networks and storage media while still preserving the essential diagnostic information needed for processing on remote systems.
3Productivity
If full precision volumetric data is processed locally, then processing speed can be maintained, but storage and memory requirements limit the number of files that can be processed simultaneously
Solution Approach 1:
The patent applies parameter changes by reducing the precision of stored volumetric data while maintaining full precision during active processing. This allows multiple compressed files to be loaded into memory simultaneously, increasing the number of files that can be processed in parallel. When processing occurs, the data can be temporarily expanded to full precision to maintain processing speed, then compressed again for storage.
Data Source
AI summary
A computer-implemented method and system compresses CT reconstruction images and can include: receiving a volumetric density file including one or more voxels; replacing one or more voxel density values below an air density value with the air density value; replacing one or more voxel density values above a material density value with the material density value; determining one or more voxels of interest; replacing one or more non-interesting voxel density values below a material surface density with the air density value; replacing one or more non-interesting voxel density values above the material surface density with the material density value; quantizing all voxels to provide a reduced volume image; and compressing the reduced volume image to provide a compressed volume image.