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

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Abstract

Deep brain stimulation (DBS) is a method of electrical neuromodulation used to treat a variety of neuropsychiatric conditions including essential tremor, Parkinson’s disease, epilepsy, and obsessive–compulsive disorder. The procedure requires precise placement of electrodes such that the electrical contacts lie within or in close proximity to specific target nuclei and tracts located deep within the brain. DBS electrode trajectory planning has become increasingly dependent on direct targeting with the need for precise visualization of targets. MRI is the primary tool for direct visualization, and this has led to the development of numerous sequences to aid in visualization of different targets. Synthetic inversion recovery images, specified by an inversion time parameter, can be generated from T1 relaxation maps, and this represents a promising method for modifying the contrast of deep brain structures to accentuate target areas using a single acquisition. However, there is currently no accessible method for dynamically adjusting the inversion time parameter and observing the effects in real-time in order to choose the optimal value. In this work, we examine three different approaches to implementing an application for real-time optimal synthetic inversion recovery image selection and evaluate them based on their ability to display continually-updated synthetic inversion recovery images as the user modifies the inversion time parameter. These methods include continuously computing the inversion recovery equation at each voxel in the image volume, limiting the computation only to the voxels of the orthogonal slices currently displayed on screen, or using a series of lookup tables with precomputed solutions to the inversion recovery equation. We find the latter implementation provides for the quickest display updates both when modifying the inversion time and when scrolling through the image. We introduce a publicly available cross-platform application built around this conclusion. We also briefly discuss other details of the implementations and considerations for extensions to other use cases.

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Data Availability

An implementation of the update-LUT method described in this report is available through the NeuroImaging Tools and Resources Collaboratory at the following URL: https://www.nitrc.org/projects/rt-osiris/.

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Acknowledgements

Figures 1 and 3 were created with Biorender.com.

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Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, Jacksonville, FL, USA

    Vishal Patel, Shengzhen Tao, Xiangzhi Zhou, Chen Lin, Erin Westerhold & Erik H. Middlebrooks

  2. Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA

    Sanjeet Grewal & Erik H. Middlebrooks

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  1. Vishal Patel
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  2. Shengzhen Tao
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  3. Xiangzhi Zhou
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Correspondence to Vishal Patel.

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Patel, V., Tao, S., Zhou, X. et al. Real-Time Optimal Synthetic Inversion Recovery Image Selection (RT-OSIRIS) for Deep Brain Stimulation Targeting. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01117-7

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