Hybrid algorithms for SAR matrix compression and the impact of post-processing on SAR calculation complexity.

Area of Science:

• Medical Physics
• Biomedical Engineering
• Electromagnetics

Background:

• Specific Absorption Rate (SAR) is a critical safety parameter in Magnetic Resonance Imaging (MRI).
• Efficient calculation of SAR is essential for real-time monitoring, especially in systems with numerous radiofrequency (RF) coils.
• Virtual Observation Points (VOPs) are used for SAR matrix compression, but computational efficiency remains a challenge.

Purpose of the Study:

• To develop and evaluate faster algorithms for VOP compression and post-processing of SAR matrices.
• To investigate the relationship between the number of RF coil channels and the computational load in VOP-based SAR calculations.
• To enhance the efficiency of online SAR supervision in MRI.

Main Methods:

• Proposed novel algorithms combining two criteria for VOP-based SAR matrix upper boundedness.
• Compared new and existing algorithms using head coil arrays with varying channel counts.
• Applied a new post-processing algorithm to VOP sets and analyzed the number of VOPs for a fixed relative overestimation.

Main Results:

• New algorithms demonstrated a 2x to >10x speed improvement over previous methods.
• Compression efficiency remained identical between old and new algorithms.
• Post-processing showed that the number of VOPs increases logarithmically with RF coil channel count for a fixed median relative overestimation.

Conclusions:

• The developed algorithms offer substantial speed enhancements for SAR calculations.
• Post-processing algorithms are highly advantageous for online SAR supervision in high-channel-count MRI systems.
• These advancements enable lower relative SAR overestimation for a given number of VOPs, improving safety monitoring.