KMRI – experimental chunked MRI compression using ZSTD and ROI-aware encoding
KMRI is an experimental medical imaging compression framework designed to improve the efficiency of volumetric MRI data storage. It utilizes chunked, structure-aware compression techniques and Zstandard instead of traditional gzip methods. The project aims to enhance compression ratios and decoding performance while preserving important data features like segmentation masks.
- ▪KMRI replaces .nii.gz with a system that splits MRI volumes into chunks and applies ROI-aware compression strategies.
- ▪The framework is built using Python and C++, leveraging Zstandard for improved compression performance.
- ▪KMRI aims to provide better compression ratios and faster decoding by understanding the structure of MRI data.
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KMRI Experimental medical imaging compression framework exploring chunked, structure-aware alternatives to .nii.gz (gzip-based NIfTI compression) KMRI is a high-performance medical imaging compression system for volumetric MRI/NIfTI data built with Python + C++ (pybind11 + Zstd). It explores whether structure-aware compression can outperform traditional generic compression methods like gzip. ⚡ TL;DR KMRI is an experimental replacement for .nii.gz that: splits MRI volumes into chunks applies ROI-aware compression strategies uses Zstandard instead of gzip optionally quantizes intensity data preserves segmentation masks losslessly improves compression vs speed trade-offs 🧠 Why this project exists Most medical imaging pipelines still rely on: .nii.gz = raw gzip compression of entire volume…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.