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    Biomedical Imaging and Instrumentation

    Comparison between two image analysis softwares for magnetic resonance imaging and computed tomography registration in deep brain stimulation

    (E-194) Comparison between two image analysis softwares for magnetic resonance imaging and computed tomography registration in deep brain stimulation

    Saturday, October 14, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row E - Poster # 194
    Introduction::

    Deep brain stimulation (DBS) is used to treat the motor symptoms of patients with Parkinson disease (PD), essential tremor (ET), and dystonia. The treatment involves surgically implanted electrodes that stimulate structures deep in the brain with pulses of electricity controlled by a pacemaker [1] . DBS has many clinical benefits including: tremor, bradykinesia, and rigidity control. However, DBS can also have side effects, which includes: facial pulling, paresthesia, and dysarthria. Therefore, in order to reduce the side effects of the DBS treatment, clinical precision of electrode localization is necessary. Identifying the target region to treat this neurodegenerative condition is guided by developing an anatomical map using preoperative magnetic resonance imaging (MRI) and computerized tomography (CT) scans [2]. We are able to confirm that electrodes are implanted in the targeted region by assessing a merged postoperative CT scan and a preoperative MRI image [3]. Electrode localization from postoperative CT and preoperative MRI image has been identified using biomedical image visualization software Analyze. However, the latest release of the medical imaging toolbox from MATLAB is considered to provide a similar function to what Analyze offers. For this project, the overarching goal is to compare the localization of DBS electrodes within the subthalamic nucleus (STN) between Analyze and MATLAB’s medical imaging toolbox.



    Materials and Methods:: One preoperative full MRI scan taken axially and one postoperative CT scan were used to reconstruct the DBS electrode localization. Such a process was performed in both Analyze and MATLAB software, following the same procedure to measure the location of an electrode: resampling, transformation, and co-registration. Due to the fact that CT scan has a higher number of voxels in all dimensions compared to full MRI, full MRI voxels have been upsampled to match with that of the CT scan. Once resampled, the full MRI image has been oriented using the Talairach coordinate system. The CT scan was registered with the talairach-oriented full MRI to normalize the coordinate system of two images. After registration was complete, the electrode coordinates were then calculated and compared between the two softwares.

    Results, Conclusions, and Discussions:: Qualitatively, it was found that the final registration between the MRI and the CT align well for both Analyze and MATLAB softwares. However, when measuring the electrode contact locations in each software, the positions of the electrodes differed in the x, y, and z directions, particularly in the z direction. Analyze and MATLAB have placed their origins in different locations. This is because MATLAB uses right anterior superior (RAS) orientation while Analyze uses left posterior superior (LPS) orientation. These differences make it more challenging to compare the two softwares. There are some noticeable differences between the electrode contact locations for the MATLAB and Analyze softwares. Despite these differences, they are both comparative programs for registering and assessing MRI and CT scans. In the future, we will further enhance our analysis by comparing the Analyze and MATLAB images after each processing step through image subtraction. In addition, we will be replicating our processes with multiple MRIs and CTs in order to obtain more universal results,

    Acknowledgements (Optional): :

    References (Optional): : [1] Mikos, A., et al. “Patient-Specific Analysis of the Relationship between the Volume of Tissue Activated during DBS and Verbal Fluency.” NeuroImage, vol. 54, 2011, doi:10.1016/j.neuroimage.2010.03.068, [2] Sadeghi A, Rizi MP, Shahidi G, Vaidyanathan J, Bajelani K, Aboulhasani M, et al. Various targeting methods for subthalamic nucleus in deep brain stimulation. Brain Stimul. (2019) 12:390. doi: 10.1016/j.brs.2018.12.250, [3] Husch A, Petersen MV, Gemmar P, Goncalves J, Sunde N, Hertel F. Post-operative deep brain stimulation assessment: automatic data integration and report generation. Brain Stimul. (2018) 11:863–6. doi: 10.1016/j.brs.2018.01.031