title: Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.
abstract: 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.
authors.affiliation: Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; University Hospital Center (CHUV) and University of Lausanne (UNIL), Switzerland. Electronic address: alessandro.daducci@epfl.ch.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Daducci
authors.affiliation: FIDMAG Germanes Hospitaláries, Spain; Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain.
authors.forename: Erick J
authors.initials: EJ
authors.lastname: Canales-Rodríguez
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark.
authors.forename: Tim B
authors.initials: TB
authors.lastname: Dyrby
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
authors.affiliation: Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; University Hospital Center (CHUV) and University of Lausanne (UNIL), Switzerland.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*
keywords: Convex optimization; Diffusion MRI; Microstructure imaging