Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study

By Bennett A. Landman, A. Huang, A. Gifford, D. Vikram, I. Lim, Jonathan A. D. Farrell, J. Bogovic, J. Hua, Min Chen, S. Jarso and 7 more. NeuroImage, 2010

318 citations recorded by the source corpus.

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 minute protocol on a 3T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22–61 y/o). 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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/54ee13eb2641a9d27b850f151667252e779409ac

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Citations in the corpus

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “1) Symmetric multivariate template construction [122] using the data described in [123].”
Multiscale brain MRI super-resolution using deep 3D convolutional networks (2019, unknown): “The Kirby 21 dataset (Landman et al. 2011)”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, unknown): “…all others are from publicly available datasets (i.e. Test-Retest OASIS1 (Marcus et al., 2007), the Multi-Modal Reproducibility Resource (Landman et al., 2011), Nathan Kline Institute Test – Retest and Nathan Kline Institute/Rockland Sample, Human Language Network subjects (Morgan et…”
Statistical Inference on Random Dot Product Graphs: a Survey (2017, methodology): “This is consistent with the reproducibility finding of [53].”