Real-Time Optimal Synthetic Inversion Recovery Image Selection (RT-OSIRIS) for Deep Brain Stimulation Targeting

Area of Science:

• Medical Imaging
• Neuroscience
• Computational Biology

Background:

• Deep brain stimulation (DBS) requires precise electrode placement for treating neurological and psychiatric disorders.
• Accurate visualization of deep brain targets using MRI is crucial for DBS electrode trajectory planning.
• Synthetic inversion recovery (SIR) images offer promising contrast modification for target visualization.

Purpose of the Study:

• To develop and evaluate methods for real-time selection of optimal inversion time (TI) parameters for SIR imaging.
• To create a user-friendly application for dynamic adjustment of TI and visualization of SIR images.
• To improve the efficiency and accuracy of DBS target localization.

Main Methods:

• Examined three computational approaches: full volume, on-screen slice, and lookup table (LUT) methods for generating SIR images.
• Evaluated methods based on real-time display update speed during TI modification and image scrolling.
• Developed a publicly available, cross-platform application implementing the most efficient method.

Main Results:

• The lookup table (LUT) method demonstrated the fastest display updates for both TI adjustments and image scrolling.
• The developed application enables real-time visualization and selection of optimal SIR images.
• The LUT-based approach significantly enhances the dynamic adjustment of inversion time for improved target visualization.

Conclusions:

• A lookup table-based implementation is the most efficient for real-time SIR image generation and selection.
• The developed application provides an accessible tool for optimizing SIR imaging parameters in DBS planning.
• This work facilitates more precise electrode placement in deep brain stimulation procedures.