Daducci, Alessandro2; Canales-Rodríguez, Erick J3; Zhang, Hui4; Dyrby, Tim B1; Alexander, Daniel C4; Thiran, Jean-Philippe5
1 Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and research, Amager and Hvidovre Hospital, The Capital Region of Denmark2 University of Lausanne3 FIDMAG Germanes Hospitaláries, Spain; Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain.4 University College of London5 Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland
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.