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.
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 37 decibel or higher. Study design: Prospective observational study. Study population: Twenty Parkinson's patients considered eligible for DBS surgery at Radboud University Medical Centre will be included. Main study parameters/endpoints: Peak signal-to-noise ratio measured in decibels.
Study Type
OBSERVATIONAL
Enrollment
20
Radboud University Medical Center
Nijmegen, Gelderland, Netherlands
Peak signal-to-noise ratio
The change in image quality as determined by the peak signal-to-noise ratio.
Time frame: Six months after study completion of the last subject.
Structural similarity index measure
The change in image quality as determined by the structural similarity index measure.
Time frame: Six months after study completion of the last subject.
Normalized root mean squared error
The change in image quality as determined by the normalized root mean squared error.
Time frame: Six months after study completion of the last subject.
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