Reduced-Reference Learning for Target Localization in Deep Brain Stimulation
preprintposted on 2022-05-17, 02:27 authored by Li WengLi Weng, Zhoule Zhu, Hemmings Wu, Junming Zhu
This work proposes a supervised machine learning algorithm for target localization in deep brain stimulation (DBS). DBS is a recognized treatment for movement disorders, such as essential tremor. The effects of DBS significantly depends on the precise choice of target location. Recent research on diffusion tensor imaging (DTI) shows that the optimal target is related to the dentato-rubro-thalamic tract (DRTT), thus DRTT analysis has become a promising approach. This technique is more accurate than conventional ones, but still too complicated for clinical scenarios, where only magnetic resonance imaging (MRI) data is available. In order to improve efficiency and utility, we consider target localization as a non-linear regression problem in a reduced-reference learning framework, and solve it with convolutional neural networks. The proposed method is light-weight, and consists of two image-based networks: one for classification and the other for localization. We model the basic workflow as an image retrieval process and define relevant performance metrics. Using DRTT analysis as groundtruths, we show that DTI-based optimal targets can be inferred from MRI data with high accuracy. For 280x220 (0.7 mm slice thickness) MRI input, our model achieves an average posterior localization error of 2.3 mm, and a median of 1.7 mm. The proposed framework is the first in the DBS domain. It is a successful application of reduced-reference learning, and may serve as a baseline for general target localization problems in DBS.