Multi-parametric neuroimaging reproducibility: a 3-T resource study

Abstract

Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60-min protocol on a 3-T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22-61 years old). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3.5%. We tabulate the mean intensity, variability, and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1-5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imaging data and analysis routines have been made publicly and freely available. This effort provides the neuroimaging community with a resource for optimization of algorithms that exploit the diversity of modern MRI modalities. Additionally, it establishes a baseline for continuing development and optimization of multi-parametric imaging protocols.

Figure 1

A single registered slice from the baseline images in the multi-parametric study of a representative subject. Structural scans (a–c) reveal high resolution information on local tissue classification. Diffusion (d–e), quantitative T2 (f–g), magnetization transfer (h–i), and quantitative T1 (j–k) provide assessments of local micro-structure. Field maps (l–n) enable improved calibration of quantitative findings. Assessment of perfusion (o–p) allows inference on functional characteristics.

Figure 2

Representative whole brain segmentation for a single subject. Topologically correct segmentation yields volumetric segmentation of cortical gray matter (purple), cerebral white matter (cyan), thalamus (pink), putamen (slate), caudate (yellow), cerebellar white matter (lavender), brainstem (blue), cerebellar gray matter (marigold), and ventricular CSF (black). Sulcal CSF and dura are labeled separately (tan).

Figure 3

Illustrative contrasts derived from multi-parametric imaging. Anatomical information, such as tissue classification, cortical parcellation, local tissue diffusion characteristics, and white matter connectivity (a–d) can be inferred from structural and diffusion imaging. Voxel-wise MR properties, such as T1, T2, or MT ratio (e–g), can be inferred from quantitative imaging. Perfusion imaging reveals blood flow characteristics (e.g., perfusion, vascular density, or functional activity, h–j). Resting state fMRI data show the default mode network extracted using precuneus seed (green, MNI coordinates: −2 −51 27) correlation. B0 and B1 field mapping techniques (k–l) can be used to correct quantitative results and/or spatial distortions.