Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson's disease

• Type of Material: Article
• Authors: Mohammad Abbass, Western University, London; Greydon Gilmore, Western University, London; Alaa Taha, Western University, London; Ryan Chevalier, Western University, London; Magdalena Jach, Western University, London; Terry M Peters, Western University, London; Ali R Khan, Western University, London; Jonathan Lau, Emory University
• Language: English
• Date: 2021-10-23
• Publisher: SPRINGER HEIDELBERG
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1007/s00429-021-02408-3
• Title of Journal or Parent Work: BRAIN STRUCTURE & FUNCTION
• Volume: 227
• Issue: 1
• Start Page: 393
• End Page: 405
• Grant/Funding Information: No funding was received to assist with the preparation of this manuscript.
• Keywords: Accuracy; Parkinson's disease; Biomarker; Science & Technology; IMAGE REGISTRATION; Fiducials; Anatomy & Morphology; Life Sciences & Biomedicine; ACCURACY; Neurosciences; ATLASES; Registration; Deep brain stimulation; Neurosciences & Neurology
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson’s disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.