GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent...

Area of Science:

• Medical Imaging
• Computational Science
• Neuroscience

Background:

• Medical imaging, particularly magnetic resonance imaging (MRI), is crucial for diagnosing brain conditions.
• Complex image analysis tasks in MRI demand significant computational resources.
• Graphic processing unit (GPU)-based parallel processing offers a solution for accelerating these computations.

Purpose of the Study:

• To provide a comprehensive literature review on the application of GPU-based parallel processing in brain MRI analysis.
• To emphasize the role of GPU acceleration in enhancing the efficiency of various medical imaging techniques.
• To identify advancements and remaining challenges in GPU-accelerated brain MRI analysis.

Main Methods:

• Systematic review of literature published between 2019 and 2023.
• Analysis of articles focusing on GPU-based parallel processing for brain MRI tasks.
• Categorization of studies based on tasks, techniques, MRI sequences, and processing outcomes.

Main Results:

• GPU-based parallel processing significantly minimizes computing runtime for brain MRI analysis.
• Demonstrated advancements in image classification, object detection, segmentation, registration, and retrieval.
• Identified key areas of progress in accelerating medical imaging computations.

Conclusions:

• GPU-based parallel processing is vital for time-efficient computation in medical imaging, especially for brain MRI.
• The reviewed methods show substantial progress in reducing computational time for real-time medical feedback.
• Further research is needed to address existing obstacles and optimize GPU utilization in future medical analyses.