Parallel High Angular Resolution Diffusion Imaging (pHARDI) is a toolkit for the GPU/CPU-accelerated reconstruction of intra-voxel reconstruction methods from diffusion Magnetic Resonance Imaging (dMRI) data. It was designed to support multiple linear algebra accelerators in a wide range of devices, such as multi-core GPU devices (both CUDA and OpenCL) or even co-processors, like Intel Xeon Phi. For platforms that do not support any GPU-based accelerator, our solution can also run on multi-core processors (CPU) using highly-tuned linear algebra libraries. We use Armadillo on top of the linear algebra accelerators for providing a common interface and ArrayFire for supporting GPU devices.
List of reconstruction methods:
- Diffusion Tensor Imaging (order-2 or higher) with Symmetric Positive-Definite Constraints (DTI-SPD) (Barmpoutis, 2010)
- Q-Ball Imaging (QBI) (Tuch, 2004; Descoteaux, 2007)
- Q-Ball Imaging in Constant Solid Angle (QBI-CSA) (Aganj, 2010)
- Revisited version of the Diffusion Orientation Transform (DOT-R2) (Canales-Rodríguez, 2010a)
- Generalized Q-sampling Imaging (GQI) (Fang-Cheng, 2010)
- Diffusion Spectrum Imaging (DSI) (Wedeen, 2005; Canales-Rodríguez, 2010b)
- Robust and Unbiased Model-Based Spherical Deconvolution (RUMBA-SD) (Canales-Rodríguez, 2015; Garcia-Blas, 2016)
In the near future we plan to include additional 'state-of-the-art' intra-voxel methods, as well as fiber tracking algorithms.
The following libraries and compiler are required for compilation:
- GCC (>= 4.9)
- Armadillo (>= 6.7)
- ITK (>= 4.9)
- ArrayFire (>= 3.4.0)
- Cmake (>= 3.0.0)
Mandatory packages:
sudo apt install libfontconfig1-dev build-essential git cmake libfreeimage-dev cmake-curses-gui freeglut3-dev libinsighttoolkit4.5
Optional packages:
sudo apt install libatlas-dev liblapack-dev libblas-dev libopenblas-dev libarpack2-dev liblapacke-dev libatlas3gf-base libatlas3-base opencl-headersInstalling OpenCL support and libraries:
- Open source approach:
sudo apt install ocl-icd-opencl-dev beignet-opencl-icd opencl-headers mesa-opencl-icd- AMD
sudo apt install ocl-icd-opencl-dev http:https://support.amd.com/en-us/kb-articles/Pages/OpenCL2-Driver.aspx
Downloading and Installing ArrayFire:
git clone https://github.com/arrayfire/arrayfire.git
cd arrayfire/
git submodule init
git submodule update
mkdir build
cd build/
cmake ..
make -j8
sudo make installThis method will download and cross-compile all dependencies from author's repositories. During this process, all dependencies will be downloaded and compiled under deps folder. This method obtains bynaries for: Armadillo (7.950), ITK (4.12.0), Boost (1.64), FFTW (3.3.2), OpenBlas (0.2.19), Lapack (3.7.0), ArrayFire (3.4.2).
cmake .. -DUSE_SUPERBUILD=ON -DCMAKE_CXX_COMPILER=/usr/bin/g++-5 -DCMAKE_C_COMPILER=/usr/bin/gcc-5 -DCMAKE_BUILD_TYPE=Release
makegit clone https://github.com/arcosuc3m/phardi
cd phardi/
mkdir build
cd build
cmake .. -DCMAKE_CXX_COMPILER=/usr/bin/g++-5 -DCMAKE_BUILD_TYPE=Release
make http:https://doi.org/10.5281/zenodo.258764
USAGE: phardi [options]
Options:
Compulsory arguments (You MUST specify ALL arguments):
--alg, -a Reconstruction method (dti-spd, qbi, qbi-csa, dotr2, gqi-l1, gqi-l2, dsi, rumba)
--data, -k Data file
--mask, -m Binary mask file
--bvecs, -r b-vectors file
--bvals, -b b-values file
--odf, -o Output path
Optional arguments (You may optionally specify one or more of):
--precision, -p Calculation precision (float|double)
--verbose, -v Verbose execution details
--help, -h Print usage and exit
--compress, -z Compress resulting files
--device Hardware backend: cuda, opencl or cpu (default cuda).
--scheme, -s File path to the reconstruction grid (i.e., spherical-mesh)
(default, 362 unit vectors on the hemisphere).
Related to each reconstruction method:
RUMBA:
--rumba-iterations Iterations performed (default 300).
--rumba-lambda1 Longitudinal diffusivity value, in units of mm^2/s (default 0.0017).
--rumba-lambda2 Radial diffusivity value, in units of mm^2/s (default 0.0003).
--rumba-lambda-csf Diffusivity value in CSF, in units of mm^2/s (default 0.0030).
--rumba-lambda-gm Diffusivity value in GM, in units of mm^2/s (default 0.0007).
QBI:
--qbi-lambda Regularization parameter (default 0.006).
GQI (L1/L2)
--gqi-lambda Regularization parameter (default 1.2).
--gqi-meandiffdist Mean diffusion distance ratio (default 1.2).
DOTR2
--dotr2-lambda Regularization parameter (default 0.006).
CSA
--qbi-csa-lambda Regularization parameter (default 0.006).
DSI
--dsi-lmax Maximum spherical harmonic order (default 10).
--dsi-resolution Reconstruction grid resolution to compute the propagator (default 35, i.e, 35x35x35).
--dsi-rmin RMIN parameter (default 1).
--dsi-lreg Regularization parameter (default 0.004).
--dsi-boxhalfwidth Box half width parameter (default 5).
DTI
--dti-spd-torder Tensor order (default 2).
Examples:
phardi -a rumba -k /data/data.nii.gz -m /data/nodif_brain_mask.nii.gz -r /data/bvecs -b /data/bvals --odf /result/This work has been partially supported through the EU project ICT 644235 RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications and TIN2016-79637-P Scalable Data Management Techniques for High-End Computing Systems from the Ministerio de Economía y Competitividad, Spain, as well as the research project grant PI15/00277 by Instituto de Salud Carlos III (Co-funded by European Regional Development Fund/European Social Fund) "Investing in your future").
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Retrieved from http:https://arma.sourceforge.net/