Implementation of the linear framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) described here:

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data
Alessandro Daducci, Erick Canales-Rodriguez, Hui Zhang, Tim Dyrby, Daniel Alexander, Jean-Philippe Thiran
NeuroImage 105, pp. 32-44 (2015)

• NODDI MATLAB toolbox. Download the software and follow the instructions provided here to install it.

• CAMINO toolkit. Download the software and follow the instructions provided here to install it.

• SPArse Modeling Software (SPAMS). Download the software and follow the instructions provided here to install it.

Add the folder containing the source code of AMICO to your MATLAB PATH.

Copy the file AMICO_Setup.txt and rename it to AMICO_Setup.m. Modify its content to set the paths to your specific needs, as follows:

• AMICO_code_path : path to the folder containing the source code of AMICO (this repository). E.g. /home/user/AMICO/code.

• NODDI_path : path to the folder containing the source code of the NODDI toolbox (in case you want to use NODDI, not needed for ActiveAx). E.g. /home/user/NODDI_toolbox_v0.9.

• CAMINO_path : path to the bin folder containing the executables of the Camino toolkit (in case you want to use ActiveAx, not needed for NODDI). E.g. /home/user/camino/bin.

• SPAMS_path : path to the folder containing the source code of the SPAMS Library. E.g. /home/user/spams.

• AMICO_data_path : path to the folder where you store all your datasets. E.g. /home/user/AMICO/data. Then, the software assumes the folder structure is the following:

  ├── data
      ├── Study_01                 --> all subjects acquired with protocol "Study_01"
          ├── Subject_01
          ├── Subject_02
          ├── ...
      ├── Study_02                 --> all subjects acquired with protocol "Study_02"
          ├── Subject_01
          ├── Subject_02
          ├── ...
      ├── ...
  

  This way, the kernels need to be computed only once per each study, i.e. same protocol (number of shells, b-values etc), and subsequently adapted to each subject (specific gradient directions) very efficiently.

Tutorials/demos are provided in the folder doc/demos to help you get started with the AMICO framework.