Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject‐to‐template and template‐to‐template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point‐based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.

Background Diffusion kurtosis imaging (DKI) quantifies the microstructure non-Gaussian diffusion properties. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes. Purpose To assess reproducibility in whole-brain high resolution DKI at varying b-values. Study Type Prospective. Subjects and Phantoms Forty-four individuals from the test-retest Human Connectome Project (HCP) database and twelve 3D-printed tissue mimicking phantoms. Field Strength/Sequence Multiband echo-planar imaging for in vivo and phantom diffusion-weighted imaging at 3T and 9.4T respectively. MPRAGE at 3T for in vivo structural data. Assessment From HCP data with b-value =1000,2000,3000 s/mm2 (dataset A), two additional datasets with b-values=1000, 3000 s/mm2 (dataset B) and b-values=1000, 2000 s/mm2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in whole-brain white matter (WM), region of interest (ROI) and gray matter (GM). Statistical Tests DKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate presence of systematic biases. Results Compared to dataset C, the CoV from DKI parameters from datasets A and B were comparable, with WM and GM CoVs <20%, while differences between datasets were smaller for the DKI-derived DTI parameters. Slightly higher fitting residuals were observed in dataset C compared to A and B, but lower residuals in dataset B were detected for the WM ROIs. A similar trend was observed for the phantom data with comparable CoVs at varying fiber orientations for datasets A and B. In addition, dataset C was characterized by higher residuals across the different fiber crossings. Data Conclusion The comparable reproducibility of DKI maps between datasets A and B observed in the in vivo and phantom data indicates that high reproducibility can still be achieved within a reasonable scan time, supporting DKI for clinical purposes.

HIGHLIGHTS: I. Reproducibility and fitting accuracy of high resolution DKI were evaluated as function of available b-values. II. A DKI dataset with b-values of 1000 and 3000 s/mm 2 performs equally well as the original HCP three-shell dataset, while a dataset with b-values of 1000 and 2000 s/mm 2 has lower reproducibility and fitting quality. III. In vivo results were verified using phantoms capable of mimicking different white matter configurations. IV. These results suggest that DKI data can be obtained within less time, without sacrificing data quality. .

Purpose To introduce and characterize inexpensive and easily produced 3D‐printed axon‐mimetic diffusion MRI phantoms in terms of pore geometry and diffusion kurtosis imaging metrics. Methods Phantoms were 3D‐printed with a composite printing material that, after the dissolution of the polyvinyl alcohol, exhibits microscopic fibrous pores. Confocal microscopy and synchrotron phase‐contrast micro‐CT imaging were performed to visualize and assess the pore sizes. Diffusion MRI scans of four identical phantoms and phantoms with varying print parameters in water were performed at 9.4 T. Diffusion kurtosis imaging was fit to both data sets and used to assess the reproducibility between phantoms and effects of print parameters on diffusion kurtosis imaging metrics. Identical scans were performed 25 and 76 days later, to test their stability. Results Segmentation of pores in three microscopy images yielded a mean, median, and SD of equivalent pore diameters of 7.57 μm, 3.51 μm, and 12.13 μm, respectively. Phantoms had T1/T2 = 2 seconds/180 ms, and those with identical parameters showed a low coefficient of variation (~10%) in mean diffusivity (1.38 × 10−3 mm2/s) and kurtosis (0.52) metrics and radial diffusivity (1.01 × 10−3 mm2/s) and kurtosis (1.13) metrics. Printing temperature and speed had a small effect on diffusion kurtosis imaging metrics (< 16%), whereas infill density had a larger and more variable effect (> 16%). The stability analysis showed small changes over 2.5 months (< 7%). Conclusion Three‐dimension‐printed axon‐mimetic phantoms can mimic the fibrous structure of axon bundles on a microscopic scale, serving as complex, anisotropic diffusion MRI phantoms.