Rationale: Better targeting of the subthalamic nucleus (STN) improves the outcome of deep brain stimulation (DBS) for Parkinson's disease. Yet, the accuracy of delineating the STN, and therefore the targeting, is limited by the spatial resolution of the magnetic resonance (MR) imaging. The current study aims to acquire a high resolution (HR) MR dataset, tailored to visualise the STN, to train a super-resolution model to predict HR MR images based on lower resolution MR input. This model will aid delineating the STN and improve segmentation and targeting.~Objective: To develop a deep learned super-resolution model that predicts high-resolution MR images with a peak signal-to-noise ratio of 37dB or higher.~Study design: Prospective observational study.~Study population: Twenty Parkinson's patients considered eligible for DBS surgery at Radboudumc will be included.~Main study parameters/endpoints: Peak signal-to-noise ratio measured in decibels.
The goal of this study is to increase magnetic resonance image quality in patients suffering from Parkinson's disease. The main question it aims to answer is: can super-resolution improve clinical magnetic resonance image quality to benefit deep brain stimulation for Parkinson's disease? Participants will receive an additional high-quality MRI scan.