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

Abstract

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. In the absence of other quantitative approaches, a point-based set of anatomical fiducials (AFIDs) was recently developed and 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, specifically focussing on structural magnetic resonance images (MRI) obtained from patients with Parkinson’s Disease (PD). We first confirmed that AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. With localization error established, we evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow for MRI scans. We found that registration errors from this workflow as 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 AFID points as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs around the left temporal horn, brainstem and pineal gland in the clinical group, structures 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 accurate aggregation of imaging datasets and comparisons between various neurological conditions.

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

“…After a brief tutorial, AFIDs have been shown to have high reproducibility even when performed by individuals with no prior knowledge of medical images, neuroanatomy, or neuroimaging software. This was shown in separate studies where placements were performed on publicly available templates and datasets 2 and a clinical neuroimaging dataset 3 . The AFIDs protocol provides a metric that is independent of the registration itself while offering sensitivity to registration errors at the scale of millimeters (mm).…”

“…The most common metrics employed for the purpose of examining the quality of image registration, including the Jaccard similarity and Dice kappa coefficients, compute the voxel overlap between regions of interest (ROIs), which have been shown to be insufficiently sensitive when used in isolation or in combination for validating image registration strategies 1 . The ROIs used in voxel overlap are often larger subcortical structures that are readily visible on MRI scans (i.e., the thalamus, globus pallidus, and striatum), and thus lack the ability to detect subtle misregistration between images which may be crucial to detecting erroneous significant differences and variability [1][2][3][4][5] . Inspired by classic stereotactic methods, our group created, curated, and validated a protocol for the placement of anatomical fiducials (AFIDs) on T1 weighted (T1w) structural magnetic resonance imaging (MRI) scans of the human brain 2 .…”

“…For instance, AFIDs have been used to evaluate the process of iterative deformable template creation 6,7 , showing that error metrics generated from AFIDs converged differently as a function of template iterations and registration method (i.e., linear vs non-linear). Sharing the AFID placements and their associated images in the Brain Imaging Data Structure (BIDS) format aids in the convenience we strive to provide for the end-user and neuroimaging application developer 2,3,6,7 . Education: New raters can compare their AFID placements to the curated normative distribution placements we release here.…”

“…To improve user accessibility and navigation of our released AFIDs annotations and framework, we also release the AFIDs validator (https://validator.afids.io). This tool provides: 1) detailed documentation of the AFIDs placement protocol, 2) an interactive way for users to upload placements to a regulated database, and 3) interactive ways to view uploaded placements relative to curated placements, which helps guide user to improve neuroanatomical understanding and placement accuracy 2,3 . Brain structure and volumetric analyses: The 32 AFIDs (and associated images) in our pathologic dataset relative to the control can allow for insight on brain morphology and putative biomarkers of neurodegenerative diseases 3 .…”

“…Additionally, quality and version control of the AFIDs framework will be introduced as more collaborations and initiatives begin incorporating it into their workflows and releases. New templates and brain images can be added to future versions of the data descriptor once they have met standards for validation set by prior related studies 2,3 .…”