This is the old Camino website, please visit our new page to download the code and get the latest documentation.
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2013-05-06: This is the old Camino website. The latest versions of the code and related information are at http://cmic.cs.ucl.ac.uk/camino
Camino is a free, open-source, object-oriented software package for analysis and reconstruction of Diffusion MRI data, tractography and connectivity mapping.
- 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).
- ActiveAx axon diameter and density mapping
- White matter compartment model fitting
- 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.
The most recent version of Camino is compatible with Java 1.6 and can be found here. Links to supporting tools for Camino can be found via the Related Links page. This link updates nightly if the latest version of the code compiles and tests successfully.
SVN access is back
You can use SVN to checkout the very latest version of Camino with the command:
svn co http://amy.cs.ucl.ac.uk:8090/repos/camino
If you want to get a particular version, do
svn -r $VERSION co http://amy.cs.ucl.ac.uk:8090/repos/camino
Windows users can use TortoiseSVN to access this.
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.
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 cite 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
||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.
|General HARDI/Multi-fibre methods
||Alexander DC and Seunarine KK. Mathematics of Crossing Fibers. Chapter 27 in Diffusion MRI: Theory, Methods, and Applications, ed DK Jones, OUP 2010.
Seunarine KK and Alexander DC. Multiple Fibers: Beyond the Diffusion Tensor. Chapter 4 in Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy, eds Johansen-Berg H and Behrens TEJ, Academic Press, 2009.
Alexander DC. Multiple-fibre reconstruction algorithms for diffusion MRI. Ann N Y Acad Sci 1046:113–133, 2005.
||Alexander DC A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features., Magnetic Resonance in Medicine, Vol. 60, pp. 439-448, 2008.
D.C. Alexander, P.L. Hubbard, M.G. Hall, E.A. Moore, M. Ptito, G.J.M. Parker and T.D. Dyrby Orientationally invariant indices of axon diameter and density from diffusion MRI., NeuroImage, 52 (4), 1374-1389, 2010.
||Tuch DS, Q-Ball Imaging, Magnetic Resonance in Medicine, 52(6), 1358-1372, 2004.
|Spherical harmonic Q-Ball
||Descoteaux M, Angelino E, Fitzgibbons S, Deriche R (2007). Regularized, fast and robust analytical q-ball imaging. Magn Reson Med 58:497–510.
Anderson AW (2005). Measurement of fiber orientation distributions using high 49 angular resolution diffusion imaging. Magn Reson Med 54:1194–1206.
Hess CP, Mukherjee P, Han ET, Xu D, Vigneron DB (2005). A spherical harmonic approach to q-ball imaging. In Proceedings of the 13th Annual Meeting of the ISMRM, Florida, p. 389.
||Jansons KM and Alexander DC, Persistent angular structure: new insights from diffusion magnetic resonance imaging data, Inverse Problems, 19, 1031-1046, 2003.
|Fast-PAS or Reduced Encoding PAS-MRI
||Sweet A and Alexander DC, Reduced Encoding Persistent Angular Structure, ISMRM, 572, 2010.
|Maximum Entropy Spherical Deconvolution (MESD)
||Alexander DC, Maximum entropy spherical deconvolution for diffusion MRI, Proc. Information Processing in Medical Imaging (IPMI), 2005.
||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.
||Parker GJM and Alexander DC, Probabilistic Monte Carlo Based Mapping of Cerebral Connections Utilising Whole-Brain Crossing Fibre Information, Proc. IPMI 2003.
||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.
||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
||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.
|DWI alignment with mbalign
||Bai Y and Alexander DC, Model-based registration to correct for motion between acquisitions in diffusion MR imaging, IEEE International Symposium on Biomedical Imaging, 947-950, 2008.
||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.
||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, Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI., IEEE Transactions on Medical Imaging, Vol. 28, pp. 1354-1364, 2009.