Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data

By Alessandro Daducci, Erick Jorge Canales-Rodríguez, Hui Zhang, Tim Bjørn Dyrby, Daniel C. Alexander, Jean-Philippe Thiran. NeuroImage, 2015

571 citations recorded by the source corpus.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Subfields: Orthopedics and Sports Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/04a51e0d0c0accb55149161f36514d83af7db544

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In Orthopedics and Sports Medicine

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Citations in the corpus

Imaging brain microstructure with diffusion MRI: practicality and applications (2019, methodology): “The AMICO framework [223], LEMONADE [224], WMTI [142], and [225] are all examples.”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “This will include surface generation via Freesurfer (Dale et al., 1999), adding morphometric measures such as cortical thickness or cortical surface area, mapping resting fMRI and dMRI onto the cortical surface, and using Multimodal Surface Matching (Robinson et al., 2014) for surface-based…”
Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants (2017, background): “Since OD is a relatively new measure of white matter microstructure [31], further work should aim to clarify its behavioural correlates.”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, unknown): “All software used in these pipelines is freely available51, 52 and full pipeline processing scripts will shortly be publicly available.”