 |
| RESEARCH
> Camino |
Camino is a free, open-source, object-oriented software package for analysis and reconstruction of Diffusion MRI data, tractography and connectivity mapping.
Key Features
- Reconstruction techniques including:
- Fitting the Diffusion Tensor to diffusion-weighted MRI data.
- Standard scalar measures, such as FA and Tr(D).
- Fitting 2 and 3-tensor models.
- Advanced reconstruction algorithms including RESTORE, q-ball, and maximum-entropy spherical deconvolution (including PAS-MRI).
- Data synthesis
- Generate synthetic data from standard diffusion tensors.
- Generate synthetic data from full diffusion tensor images.
- Generate synthetic data from other models of diffusion within restricting media.
- Generate synthetic data by Monte-Carlo simulation of diffusion within restricting geometries.
- Deterministic and probabilistic tractography (PICo), including:
- Tractography and connectivity mapping with single and multiple tensor models.
- Waypoints, exclusion regions, and multiple-ROI processing.
- Output connection probability maps, or save streamlines in raw binary, VTK, or OOGL (GeomView) format.
- PAS-PICo and Q-Ball PICo
- DT image warping
- Preservation of principal directions (PPD)
- Finite strain approximation
- Useful sets of gradient directions for diffusion MRI acquisition protocols.
- Electrostatic point sets
- Ordered point sets for improved realignment and partial acquisition.
- Full documentation via
- Unix man pages
- A variety of tutorials illustrating common
tasks
- Standard javadoc for the source code.
Download
The most recent stable version of Camino is compatible with Java 1.5 and can be found here (3 MB). To decompress and build Camino you will need bzip2, tar, and Java. These and other supporting tools for Camino can be found via the Related Links page.
You can also use SVN to checkout the very latest version of Camino with the command:
svn co http://amy.cs.ucl.ac.uk:8090/repos/camino
Windows users can use TortoiseSVN to access this.
Support
Queries, bug reports, feature requests and other feedback should be directed to camino |at| cs.ucl.ac.uk. You can also sign up to the Camino users list to make sure you hear of new developments, releases and bug fixes.
Referencing
Please use the following general citation for Camino if you use it for your work:
P. A. Cook, Y. Bai, S. Nedjati-Gilani, K. K. Seunarine, M. G. Hall, G. J. Parker, D. C. Alexander, Camino: Open-Source Diffusion-MRI Reconstruction and Processing, 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, p. 2759, May 2006.
Here are some references you should site for specific techniques that Camino implements:
| Linear DT Fitting | Basser PJ, Mattielo J, and Lebihan D, Estimation of the effective self-diffusion tensor from the NMR spin echo, Journal of Magnetic Resonance, 103, 247-54, 1994. |
| Non-Linear and constrained DT Fitting | Jones DK and Basser PJ, Squashing peanuts and smashing pumpkins: How noise distorts diffusion-weighted MR data, Magnetic Resonance in Medicine, 52(5), 979-993, 2004.
Alexander DC and Barker GJ, Optimal imaging parameters for fibre-orientation estimation in diffusion MRI, NeuroImage, 27, 357-367, 2005 |
| RESTORE | Chang L-C, Jones DK and Pierpaoli C, RESTORE: Robust estimation of tensors by outlier rejection, Magnetic Resonance in Medicine, 53(5), 1088-1095, 2005. |
| Spherical Harmonic Fibre-Crossing Detection | Alexander DC, Barker GJ and Arridge SR, Detection and modelling of non-Gaussian apparent diffusion coefficient profiles in human brain data, Magnetic Resonance in Medicine, 48, 331-340, 2002. |
| Q-Ball | Tuch DS, Q-Ball Imaging, Magnetic Resonance in Medicine, 52(6), 1358-1372, 2004. |
| PAS-MRI | Jansons KM and Alexander DC, Persistent angular structure: new insights from diffusion magnetic resonance imaging data, Inverse Problems, 19, 1031-1046, 2003. |
| Maximum Entropy Spherical Deconvolution (MESD) | Alexander DC, Maximum entropy spherical deconvolution for diffusion MRI, Proc. Information Processing in Medical Imaging (IPMI), 2005. |
| DT PICo | Parker GJM, Haroon HA and Wheeler-Kingshott CAM, A Framework for a Streamline-Based Probabilistic Index of Connectivity (PICo) using a Structural Interpretation of MRI Diffusion Measurements, Journal of Magnetic Resonance Imaging, 18, 242-254, 2003.
Cook PA, Alexander DC, Parker GJM, Modelling noise-induced fibre-orientation error in diffusion-tensor MRI, IEEE International Symposium on Biomedical Imaging, 332-335, 2004. |
| Multi-tensor PICo | Parker GJM and Alexander DC, Probabilistic
Monte Carlo Based Mapping of Cerebral Connections Utilising
Whole-Brain Crossing Fibre Information, Proc. IPMI 2003. |
| PAS-PICo | Parker GJM and Alexander DC, Probabilistic anatomic connectivity derived from the microscopic persistent angular structure of cerebral tissue, Philosophical Transactions of the Royal Society B, 360, 893-902, 2005.
Seunarine KK, Cook PA, Hall MG, Embleton K, Parker GJM and Alexander DC, Exploiting peak anisotropy for
tracking through fanning structures, Proc. ISMRM 2007, p. 901. |
| Q-Ball PICo | Seunarine KK, Cook PA, Hall MG, Embleton KV, Parker GJM, Alexander DC, Exploiting peak anisotropy for tracking through complex structures, IEEE ICCV Workshop on MMBIA 2007. |
| Wild boostrap tractography | Whitcher B, Tuch D S, Wisco J J, Sorensen A G, Wang L, Using the wild bootstrap to quantify uncertainty in diffusion tensor imaging, Human Brain Mapping 29(3), 346-362, 2008.
Jones DK, "Tractography gone wild: Probabilistic tracking using the wild bootstrap", Proc ISMRM 2006, p 435
|
| Bayesian tractography | Friman O, Farneback G, Westin C F, A Bayesian Approach for Stochastic White Matter Tractography, IEEE Transactions on Medical Imaging 25(8), 965-978, 2006.
|
| DT-MRI image warping | Alexander DC, Pierpaoli C, Basser PJ and Gee JC, Spatial Transformations of Diffusion Tensor Magnetic Resonance Images, IEEE Trans. Medical Imaging, 20(11), 1131-1139, 2001. |
| Electrostatic pointsets | Jones DK, Horsfield MA and Simmons A, Optimal strategies for measuring diffusion in anisotropic systems by MRI, Magnetic Resonance in Medicine, 42(3), 515-525, 1999.
Jansons KM and Alexander DC, Persistent angular structure: new insights from diffusion magnetic resonance imaging data, Inverse Problems, 19, 1031-1046, 2003. |
| Ordered pointsets | Cook PA, Symms M, Boulby PA and Alexander DC, Optimal acquisition orders of diffusion-weighted MRI measurements, Journal of Magnetic Resonance Imaging, 25(5), 1051-1058, 2007. |
| Model-based data synthesis | Alexander DC and Barker GJ, Optimal imaging parameters for fibre-orientation estimation in diffusion MRI, NeuroImage, 27, 357-367, 2005 |
| Monte-Carlo data synthesis | Hall MG and Alexander DC, Finite pulse width
improve fibre orientation estimates in diffusion tensor MRI, Proc. ISMRM 2006, p 1076 |
Printer-friendly version. |
|
+44 (0)20 7679 7214 - Copyright © 1999-2005
UCL
