bonndit

The bonndit package contains computational tools for diffusion MRI processing developed at the University of Bonn.

bonndit implements constrained single and multi tissue deconvolution with higher-order tensor fODFs [Ankele17], and the extraction of principal fiber directions with low-rank tensor approximation [Schultz08]. It also includes code for fiber tractography based on higher-order tensor fODFs, and for filtering the resulting set of streamlines. In particular, bonndit implements spatially regularized tracking using joint tensor decomposition or an Unscented Kalman Filter [Gruen23]. It also contains code from a study in which we compared the strategy of selecting the most suitable number of fiber compartments per voxel to an adaptive model averaging which reduced the model uncertainty [Gruen22].

Finally, the package includes code for suitably constrained fitting of the Diffusional Kurtosis (DKI) model, and computation of corresponding invariants [Groeschel16].

Free software: GNU General Public License v3
Documentation: https://bonndit.readthedocs.io.

Installation

To install bonndit via pip, run the following command

$ pip install bonndit

To install bonndit via conda, run

$ conda install bonndit -c xderes -c conda-forge

Features

An overview of the scripts and functionality included in bonndit is given in our documentation. It also includes a tutorial for performing fiber tracking with our code.

Reference

If you use our software as part of a scientific project, please cite the corresponding publications. The method implemented in stdeconv and mtdeconv was first introduced in

[Ankele16]

Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Fast and Accurate Multi-Tissue Deconvolution Using SHORE and H-psd Tensors. In: Proc. Medical Image Analysis and Computer-Aided Intervention (MICCAI) Part III, pp. 502-510, vol. 9902 of LNCS, Springer, 2016

It was refined and extended in

[Ankele17]

Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Versatile, Robust, and Efficient Tractography With Constrained Higher-Order Tensor fODFs. In: Int'l J. of Computer Assisted Radiology and Surgery, 12(8):1257-1270, 2017

The methods implemented in low-rank-k-approx was first introduced in

[Schultz08]

Thomas Schultz, Hans-Peter Seidel: Estimating Crossing Fibers: A Tensor Decomposition Approach. In: IEEE Transactions on Visualization and Computer Graphics, 14(6):1635-42, 2008

The methods implemented in peak-modelling was first introduced in

[Gruen21]

Johannes Grün, Gemma van der Voort, Thomas Schultz: Reducing Model Uncertainty in Crossing Fiber Tractography. In proceedings of EG Workshop on Visual Computing for Biology and Medicine, pages 55-64, 2021

Extended in:

[Gruen22]

Johannes Grün, Gemma van der Voort, Thomas Schultz: Model Averaging and Bootstrap Consensus Based Uncertainty Reduction in Diffusion MRI Tractography. In: Computer Graphics Forum 42(1):217-230, 2023

The regularized tractography methods (joint low-rank and low-rank UKF) were first implemented in prob-tracking and introduced in

[Gruen23]

Johannes Grün, Samuel Gröschel, Thomas Schultz: Spatially Regularized Low-Rank Tensor Approximation for Accurate and Fast Tractography. In NeuroImage 271:120004, 2023

The use of quadratic cone programming to make the kurtosis fit more stable which is implemented in kurtosis has been explained in the methods section of

[Groeschel16]

Samuel Groeschel, G. E. Hagberg, T. Schultz, D. Z. Balla, U. Klose, T.-K. Hauser, T. Nägele, O. Bieri, T. Prasloski, A. MacKay, I. Krägeloh-Mann, K. Scheffler: Assessing white matter microstructure in brain regions with different myelin architecture using MRI. In: PLOS ONE 11(11):e0167274, 2016

PDFs can be obtained from the respective publisher, or the academic homepage of Thomas Schultz: https://cg.cs.uni-bonn.de/person/prof-dr-thomas-schultz

Authors

Michael Ankele - Constrained spherical deconvolution with tensor fODFs - [momentarylapse] (https://github.com/momentarylapse)
Johannes Grün - Fiber tracking with spatial regularization or model averaging - [JoGruen] (https://github.com/JoGruen)
Olivier Morelle - Code curation, documentation and testing [Oli4] (https://github.com/Oli4)
Thomas Schultz - DKI fitting, supervision and contributions throughout - [ThomasSchultz] (https://github.com/ThomasSchultz)