ABSTRACT
Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.
Footnotes
Abbreviations
- 7T3T
- 7T MRI subthalamic nucleus atlas for use with 3T MRI (Milchenko et al., 2018)
- ANOVA
- analysis of variance
- DBS
- deep brain stimulation
- DC
- Dice coefficient
- DISTAL
- DBS intrinsic template atlas (Ewert et al., 2017)
- FDA
- U.S. Food and Drug Administration
- FGATIR
- fast gray matter acquisition T1 inversion recovery
- FLAIR
- fluid attenuated inversion recovery
- MER
- microelectrode recordings
- MNIPD25
- population-averaged atlas that was made with 3T MRI of 25 Parkinson’s disease patients (Xiao et al., 2017)
- PD
- Parkinson’s disease
- QSM
- quantitative susceptibility mapping
- SN
- substantia nigra
- STN
- subthalamic nucleus
- SWI
- susceptibility-weighted image
- T
- Tesla
- T1W
- T1-weighted
- T2W
- T2-weighted
- UHFA
- ultrahigh-field atlas (Wang et al., 2016)
1 The framework and algorithm here described are components of the patented and FDA cleared patientspecific STN visualization tool developed by Surgical Information Sciences, Inc. [Harel and Sapiro, 2016; Sapiro et al., 2017]. A preliminary work was presented at conferences [Kim et al., 2015a; Kim et al., 2015b; Kim et al., 2015c]. The scope of this study is the validation and analysis of the method on a large scale clinical data, and comparison of the proposed method with available state-of-the-art methods.