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 7 T MR technology facilitate the ability to clearly visualize the STN.
The vast majority of centers, however, still do not have 7 T 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 7 T MRI and its clinical MRI pairs. We first model
in the 7 T 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 7 T 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.