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Related Experiment Video

Updated: Jun 23, 2026

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

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Novel lossless FMRI image compression based on motion compensation and customized entropy coding.

Victor Sanchez¹, Panos Nasiopoulos, Rafeef Abugharbieh

¹Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada. victors@ece.ubc.ca

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|May 1, 2009

Summary

This study introduces an advanced method for lossless compression of functional MRI (fMRI) data, achieving a 13% improvement in compression ratio over existing techniques.

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Optogenetic Functional MRI

Optogenetic Functional MRI

Published on: April 19, 2016

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Last Updated: Jun 23, 2026

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

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Optogenetic Functional MRI

Optogenetic Functional MRI

Published on: April 19, 2016

Area of Science:

Medical Imaging
Data Compression
Biomedical Engineering

Background:

Lossless compression of 4-D medical images is crucial for efficient storage and transmission.
Previous work established a foundation using the H.264/AVC standard for medical image compression.

Purpose of the Study:

To enhance lossless compression of functional MRI (fMRI) data.
To introduce novel multiframe motion compensation and adaptive binary arithmetic coding techniques.

Main Methods:

Developed a multiframe motion compensation process with 4-D search, variable-size block matching, and bidirectional prediction.
Implemented a context-based adaptive binary arithmetic coder for residual and motion vector data.
Validated the method on real fMRI sequences and compared it against 4D-JPEG2000 and H.264/AVC.

Main Results:

The proposed method significantly outperforms state-of-the-art techniques for fMRI data compression.
Achieved an average compression ratio improvement of 13% compared to existing methods.
Demonstrated superior performance across various fMRI sequence resolutions.

Conclusions:

The enhanced compression technique offers substantial improvements for handling fMRI data.
The novel methods provide a more efficient solution for lossless compression in medical imaging.
This advancement facilitates better management and analysis of functional MRI datasets.