Citing#

Citing Larmor#

If you use Larmor in your work, please cite it, and also cite the papers of the methods you used, listed below. There is no paper about Larmor yet, so cite the software itself, with the version you used (lar --version prints it):

Di Meglio A. Larmor: a Python library and viewer for diffusion MRI, version x.y.z. https://larmor.org

In BibTeX:

@software{larmor,
  author  = {Di Meglio, Antonio},
  title   = {Larmor: a Python library and viewer for diffusion MRI},
  version = {x.y.z},
  year    = {YYYY},
  url     = {https://larmor.org},
}

The repository holds the same information in CITATION.cff, which reference managers and GitHub read.

Methods and papers#

Preprocessing#

  • MP-PCA denoising. Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. NeuroImage 142 (2016) 394-406.

  • Gibbs-ringing removal. Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76 (2016) 1574-1581.

Models#

  • DTI. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103 (1994) 247-254.

  • DKI. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53 (2005) 1432-1440. doi:10.1002/mrm.20508

  • Constrained DKI fit and its measures. Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 65 (2011) 823-836.

  • Weighted fits of DTI and DKI. Veraart J, Sijbers J, Sunaert S, Leemans A, Jeurissen B. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. NeuroImage 81 (2013) 335-346.

Spherical deconvolution#

  • GRL. Guo F, Leemans A, Viergever MA, Dell’Acqua F, De Luca A. Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data. NeuroImage 218 (2020) 116948. doi:10.1016/j.neuroimage.2020.116948

  • mFOD, and its solver, non-negative least squares with a ridge penalty (L2NNLS). De Luca A, Guo F, Froeling M, Leemans A. Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs). NeuroImage 222 (2020) 117206. doi:10.1016/j.neuroimage.2020.117206

  • Damped Richardson-Lucy (dRL) solver. Dell’Acqua F, Scifo P, Rizzo G, et al. A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution. NeuroImage 49 (2010) 1446-1458. doi:10.1016/j.neuroimage.2009.09.033

  • LASSO solver. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B 58 (1996) 267-288. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B 67 (2005) 301-320. doi:10.1111/j.1467-9868.2005.00503.x

  • Non-negative least squares (NNLS), used by the solvers and the constrained DKI fit. Lawson CL, Hanson RJ. Solving Least Squares Problems. Prentice-Hall, 1974 (chapter 23).

  • NODDI response function. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61 (2012) 1000-1016. doi:10.1016/j.neuroimage.2012.03.072

Tractography and analysis#

  • Deterministic tracking. Tournier J-D, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23 (2004) 1176-1185. doi:10.1016/j.neuroimage.2004.07.037

  • Probabilistic tracking. Tournier J-D, Calamante F, Connelly A. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22 (2012) 53-66. doi:10.1002/ima.22005

  • Track density images. Calamante F, Tournier JD, Jackson GD, Connelly A. Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage 53 (2010) 1233-1243.

  • Structural connectomes. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biol 6 (2008) e159. For the radial search that assigns endpoints to regions: Smith RE, Tournier JD, Calamante F, Connelly A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage 104 (2015) 253-265.

  • Tract profiles. Yeatman JD, Dougherty RF, Myall NJ, Wandell BA, Feldman HM. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One 7 (2012) e49790. doi:10.1371/journal.pone.0049790. Chandio BQ, Risacher SL, Pestilli F, et al. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10 (2020) 17149. doi:10.1038/s41598-020-74054-4

Registration#

  • Reorienting tensors and peaks. Alexander DC, Pierpaoli C, Basser PJ, Gee JC. Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging 20 (2001) 1131-1139.

Toolboxes#

The spherical deconvolution methods and their solvers (GRL, mFOD, dRL, L2NNLS and LASSO) are reimplementations of those in MRIToolkit, by Alberto De Luca (github.com/delucaal/MRIToolkit). Denoising, Gibbs-ringing removal, tractogram editing, track density images and connectomes follow MRtrix3’s dwidenoise, mrdegibbs, tckedit, tckmap and tck2connectome: Tournier JD et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202 (2019) 116137. doi:10.1016/j.neuroimage.2019.116137