Tristan K. Kuehn

AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement

Open browser platform embeds a language-model neuroanatomy tutor in an MRI viewer and scores landmark placements against 21 expert-annotated templates using trained-rater error distributions.

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

The paper releases curated anatomical fiducial placements for MRI datasets to improve image registration quality control and registration accuracy.

An automated BIDS-App for brain segmentation of human fetal functional MRI data

The paper introduces funcmasker-flex, a BIDS App using a 3D U-net to automate fetal fMRI brain segmentation, achieving high Dice scores and reducing manual effort.

Enabling complex fibre geometries using 3D printed axon-mimetic phantoms

This paper introduces 3D-printed axon-mimetic phantoms to evaluate diffusion MRI models under complex fibre orientations, finding that Bingham-NODDI best captures planar fibre dispersion.

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

This paper evaluates the reproducibility and fitting quality of high-resolution diffusion kurtosis imaging (DKI) at 3T using varying b-values and phantom data.

Microstructural characterization and validation of a 3D printed axon-mimetic phantom for diffusion MRI

The paper introduces and validates 3D-printed axon-mimetic phantoms for diffusion MRI, showing they replicate axon-like structures and DKI metrics with high reproducibility.

A framework for evaluating correspondence between brain images using anatomical fiducials

This paper introduces anatomical fiducials (AFIDs) to improve spatial correspondence in brain imaging and demonstrates their effectiveness over traditional voxel overlap metrics.