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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Apr 15, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

GPU accelerated dynamic functional connectivity analysis for functional MRI data.

Devrim Akgün¹, Ünal Sakoğlu², Johnny Esquivel²

¹Department of Computer Engineering, Sakarya University, Sakarya, Turkey.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|March 26, 2015

Summary

This summary is machine-generated.

Parallel computing accelerates dynamic functional connectivity (DFC) analysis in neuroimaging. GPU implementations using CUDA offer significant speed-ups, making complex brain network analysis more practical for large studies.

Keywords:

CUDA Dynamic functional connectivity Functional magnetic resonance imaging GPU computing OpenMP fMRI

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Area of Science:

Neuroimaging and Computational Neuroscience
High-Performance Computing

Background:

Dynamic functional connectivity (DFC) analysis of functional magnetic resonance imaging (fMRI) data is computationally intensive.
Advances in parallel computing, including multi-core processors and Graphics Processing Units (GPUs), offer potential for accelerating such analyses.

Purpose of the Study:

To implement and analyze parallel algorithms for DFC analysis.
To evaluate the performance of thread-based and block-based parallel approaches using Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA).

Main Methods:

Developed a thread-based parallel DFC algorithm using OpenMP for multi-core processors.
Implemented a block-based parallel DFC algorithm using CUDA on GPUs, optimizing for CUDA architecture.
Combined thread-based and block-based approaches for enhanced GPU parallelization.

Main Results:

OpenMP implementation on an 8-core processor achieved up to 7.7x speed-up.
CUDA implementation using combined approaches yielded substantial accelerations, ranging from 18.5x to 157x speed-up.
Parallel GPU solutions significantly reduced computation time for DFC analysis.

Conclusions:

Multi-core and GPU-based parallel implementations significantly accelerate DFC analyses.
The developed algorithms enhance the practicality of DFC analysis for multi-subject and dynamic studies.