Progressive Lossless Compression for 3D Medical Imaging Data
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Solution Overview
Problem
Current medical imaging data compression techniques often result in loss of diagnostic detail and inefficient processing overhead, failing to significantly reduce the time required for transferring and rendering three-dimensional medical images over networks.
Innovation Solution
A progressive lossless compression technique that uses resolution subsampling, delta encoding, and optimizations to compress and decompress three-dimensional medical imaging data, allowing for faster network transfers and reduced time to first image visualization while maintaining image quality, utilizing SIMD architecture for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression techniques are applied to medical imaging data, then image quality and diagnostic detail are preserved, but processing overhead increases and transfer time is not significantly reduced
Solution Approach 1:
The patent divides the 3D medical imaging data into multiple 2D slices or layers, applying compression techniques to each slice independently. This segmentation allows for more efficient processing compared to treating the entire 3D volume as a single unit, reducing the computational overhead while maintaining lossless compression of the complete dataset.
Solution Approach 2:
The patent applies compression to intermediate processing stages rather than only to the final output. By compressing data at multiple points during the imaging pipeline (including during acquisition and preprocessing), the system achieves cumulative compression effects that significantly reduce transfer times while maintaining diagnostic quality in the final uncompressed images.
2Loss of time
If compression is applied to reduce transfer time, then network bandwidth utilization improves, but diagnostic detail may be lost
Solution Approach 1:
The patent performs compression operations in advance during the image acquisition and preprocessing stages, rather than compressing data immediately before transfer. This preliminary compression allows the system to prepare compressed datasets ahead of time, reducing the actual transfer time without compromising the ability to reconstruct full-quality images when needed for diagnosis.
Solution Approach 2:
The patent creates compressed representations or proxies of the original medical imaging data that can be rapidly transferred and displayed. These compressed copies maintain sufficient fidelity for preliminary assessment and can be quickly decoded, while the original full-resolution data remains available for detailed diagnostic work when required.
3Reliability
If entire 3D image stacks are transferred before viewing, then complete diagnostic information is available, but network bandwidth is consumed inefficiently
Solution Approach 1:
The patent segments the 3D image stack into individual 2D slices or manageable chunks that can be transferred independently. This allows the system to transfer only the specific slices needed for a particular diagnostic task rather than the entire volumetric dataset, reducing network bandwidth consumption while maintaining access to complete diagnostic information when all slices are eventually transferred or cached.
Solution Approach 2:
The patent transfers compressed versions of the 3D image data that occupy less network bandwidth, with the understanding that full decompression can occur at the destination if and when complete diagnostic information is required. The compressed transfer provides a bandwidth-efficient intermediate solution that doesn't permanently lose diagnostic capability.
Data Source
AI summary
Techniques and configurations for compression of image data in a progressive, lossless manner are disclosed. In an example, three-dimensional medical images may be compressed and decompressed with high-speed operations, through a compression technique performed on a cube (chunk) of voxels that includes generating a subsampled or filtered cube of voxels, and generating and optimizing a delta data set between the cube of voxels and the subsampled cube of voxels. This optimized delta data set is operable with a decompression technique to losslessly recreate the cube of voxels. Further, the compression technique may be progressively performed with multiple iterations, to allow multiple lower resolution versions of the images prior to loading or receiving the entire compressed data that is reconstructable in a lossless form. Use of this technique may result in dramatically reduced time to first image when visualizing 3D images and performing image data transfers.