Enhancing transcranial ultrasound stimulation planning with MRI-derived skull masks: a comparative analysis with CT-based processing.

Abstract

Objective.Transcranial ultrasound stimulation (TUS) presents challenges in ultrasound wave transmission through the skull, affecting study outcomes due to aberration and attenuation. While planning strategies incorporating 3D computed tomography (CT) scans help mitigate these issues, they expose participants to radiation, which can raise ethical concerns. A solution involves generating skull masks from participants' anatomical magnetic resonance imaging (MRI). This study aims to compare ultrasound field predictions between CT-derived and MRI-derived skull masks in TUS planning.Approach.Five participants with a range of skull density ratios (SDRs: 0.31, 0.42, 0.55, 0.67, and 0.79) were selected, each having both CT and T1/T2-weighted MRI scans. Ultrasound simulations were performed using BabelBrain software with a single-element transducer (diameter = 50 mm,F# = 1) at 250, 500, and 750 kHz frequencies. CT scans were used to generate maps of the skull's acoustic properties. The MRI scans were processed using the Charm segmentation tool from the SimNIBS tool suite using default and custom settings adapted for better skull segmentation. Ultrasound was adjusted to target 30 mm below the skull's surface at 54 electroencephalogram (EEG) locations.Main Results.The custom setting in Charm significantly improved the Dice coefficient between MRI- and CT-derived masks when compared to the default setting (p< 0.001). The maximum pressure error significantly decreased in the custom setting compared to the default setting (p< 0.001). Additionally, the focus location error median across different SDRs averaged 2.32, 1.45, and 1.57 mm in default and 2.08, 1.38, and 1.44 mm in custom conditions for 250 kHz, 500 kHz, and 750 kHz respectively.Significance.MRI-derived skull masks offer satisfactory accuracy at many EEG sites, and using custom settings can further enhance this accuracy. However, significant errors at specific locations highlight the importance of carefully considering stimulation location when choosing between CT- and MRI-derived skull modeling.