Introduction

Deep Brain Stimulation (DBS) of the Sub Thalamic Nuclei treats resistant tremor and rigidity in Parkinson disease¹. Its stimulatory effects can be non-invasively monitored with fMRI². Advanced MRI images on patients with active implantable devices contain artifacts that must be identified to provide clean, reliable data, prior to in-depth analysis. The current work consisted in the development of a tool for detecting DBS-induced MRI artifacts.

Methods

We developed a semi-automated pipeline featuring a graphical interface, the “Artifacts Recognition Tool of DBS” (artDBS), implemented in Python v3.11 and tested on 17 patients with DBS. This tool processes T1-weighted images, pre- and post-operative rs-fMRI sequences, and post-operative CT scans, aligned using FSL³ and ANTs⁴. The software employs automatic thresholds along with sequential morphological⁵ and topological operations to detect artifacts caused by electrode leads and peripheral wires. The latter can be manually adjusted via thresholding GUI button, as their size varies among patients due to variation in external positioning.

Results

Using the “artDBS” GUI, areas with signal alterations caused by magnetic susceptibility artifacts from DBS implants were successfully identified and extracted automatically. Manual GUI-based segmentation threshold adjustment allows even greater precision in peripheral artifact delineation. The segmentations were reviewed and evaluated as optimal in 85% of the sample.

Conclusions

The preventative segmentation of artifacts present in DBS patient MRI images is crucial, to circumscribe susceptibility and geometric distortion artifacts that can be propagated through the analysis chain. The artDBS pipeline offers a method to achieve this.

References

1. Steinhardt, Julia, et al. “Mechanisms and consequences of weight gain after deep brain stimulation of the subthalamic nucleus in patients with Parkinson’s disease.” Scientific Reports 13.1 (2023): 14202.
2. Miao, Jingya, et al. “Use of functional MRI in deep brain stimulation in Parkinson’s diseases: a systematic review.” Frontiers in Neurology 13 (2022): 849918.
3. Jenkinson, Mark, et al. “FSL.” Neuroimage 62.2 (2012): 782-790.
4. Avants, Brian B., et al. “A reproducible evaluation of ANTs similarity metric performance in brain image registration.” Neuroimage 54.3 (2011): 2033-2044.
5. Boutet, Alexandre, et al. “Functional MRI safety and artifacts during deep brain stimulation: experience in 102 patients.” Radiology 293.1 (2019): 174-183.