Description of toolboxes

On this page you can find a small description of the functionality of the toolboxes included in QMRITools. For a full overview of all functionality see the documentation or the function listing notebook. For toolboxes that have the demo.nb tag most of the most of the functionality demo notebook.

CardiacTools up

A collection of tools to analyze cardiac data. The main features are cardiac shape analysis which allows defining the hard in a local myocardial coordinate system which allows quantifying and analyzing data. When the cardiac geometry is known there are functions to analyze qMRI parameters in the AH17 model (Cerqueira et al. 2002) or perform transmural sampling of qMRI parameters.

Cardiac segmentation in the AHA-17 model and estimation of the local local myocardial coordinate system

CoilTools up

A collection of tools to evaluate and visualize complex multi-coil data. The functions are specific for analysis of multi-coil magnitude and noise data which allows quantifying per channel SNR. Furthermore, if complex coil sensitivity maps are available it allows performing SENSE g-factor maps simulations.

analysis of coil positions and their sensitivity

DenoiseTools up

The toobox provides two algorithms that allow denoising of DWI data. The first is based on and LMMSE framework (Aja-Fernandez et al. 2008) and the second is based on a random matrix theory and Principal component analysis framework (Veraart et al. 2016). Furthermore, it provides an anisotropic filters for denoising the estimated diffusion tensor which provides more reliable fiber orientation analysis and fiber tractography (Lee et al. 2006; Damon et al. 2021).

denoising of diffusion data using principal component analysis

DixonTools up

An IDEAL based Dixon reconstruction algorithm (Reeder et al. 2005; Yu et al. 2008). The method provides multi-peak fitting B0 field and T2- correction. The toolbox also provides a function for unwrapping phase data in 2D and 3D based on a best path method (Abdul-Rahman et al. 2007; Herraez et al. 2002). It also contains a function that allows simulating gradient echo Dixon data.

IDEAL based Dixon reconstruction output

IDEAL based Dixon reconstruction: fitted fat fractions as a function of the imposed fat fraction, SNR and B0 field offset

ElastixTools up

A wrapper that calls the Elastix registration framework (Klein et al. 2010; Shamonin 2013). The toolbox determines what registration or transformations need to be performed, exports the related data to a temp folder and calls an automatically generated command line script that performs the registration. After registration is completed the data is again loaded into Mathematica.

non rigid registration over time points using Dixon data

non rigid registration of mulit slice 2D cardiac diffusion data

GeneralTools up

This toolbox provides core functions used in many other functions and features. The functions comprise amongst others: data cropping, mathematical and statistical operators that ignore zero values, and data rescaling, transformation and padding.

GradientTools up

The main feature is an algorithm that uses static repulsion (Jones, Horsfield, and Simmons 1999; Froeling et al. 2017) to generate homogeneously distributed gradient directions for DWI experiments. It also provides functions to convert bval and bvec files to bmatrix and vice versa.

The graphical user interface of the gradient generation tool.

ImportTools up

Allows importing DCM data or DCM header attributes. These functions are rarely used since the toolbox mostly uses the NIfTY data format and provides tools to convert DCM to NIfTI via dcm2niix. Furthermore the default DCM importing capability of Mathematica has improved over the years.

IVIMTools up

The toolbox includes functions to perform IVIM fitting of DWI data. There are two main functions: non linear fitting and Bayesian fitting (Orton et al. 2014). It also contains functions to remove the IVIM bias signal from diffusion weighted data using multiple b-values (de Luca et al. 2017).

effect of inculding ivim into the DTI fit.

Visualization of IVIM fitting.

JcouplingTools up

A toolbox that allows simulation of NMR spectra using Hamiltonians based on methods from FID-A. It allows simulating large spin systems (Castillo et al. 2011) and was initially implemented to investigate fat spectra in TSE acquisitions of muscle (Stokes et al. 2013). However its most prominent application now is to generate basis spectra for fitting acquired MRS data.

Simulated 31p spectra.

MaskingTools up

Tools for masking and homogenization of data. It provides functions for smoothing cutting and merging masks and functions for the evaluation of data within masks.

Visualization of manual segmented muscles.

NiftiTools up

Import and export of the NIfTI file format. Part of the code is based on previously implemented nii-converter. For converting DICOM data to the NIfTI file format the toolbox uses dcm2niix. It also provides some specialized NIfTI import functions for specific experiments which are probably not generalizable.

PhysiologyTools up

Functions for importing and analyzing Philips physiology logging and RespirAct trace files. The functions are rarely used and not well supported.

PlottingTools up

A variety of functions for visualization of various data types. The main functions are ‘PlotData’ and ‘PlotData3D’ which allow viewing 2D, 3D and 4D data.

Data viewer for 2D, 3D and 4D data.

ReconstructionTools up

A variety of function for raw MRI data reconstruction. The main goal was to create a set of functions that allow for the reconstruction of multi coil 3D CSI data and and low SNR 31P imaging data.

ProcessingTools up

The toolbox comprises a variety of functions that allow data manipulation and analysis. The main functions allow joining multiple data sets with overlapping slices into one continuous data set (Froeling et al. 2015) and to automatically split data of two legs into two separate data-sets. Furthermore, it contains a collection of functions for data evaluation and analysis.

Joining data acquired in multiple stacks.

Automatically find the plan where to split data into left and right leg.

RelaxometryTools up

A collection of tools to fit T2, T2*, T1rho and T1 relaxometry data. The main function of this toolbox is an extended phase graph (EPG) (Weigel 2015) method for multi-compartment T2 fitting of multi-echo spin echo data (Marty et al. 2016). Therefore it provides functions to simulate and evaluate EPG (Keene et al. 2020).

Simulated EPG signal over the slice profile for combined water and fat signals.

Demonstration of EPG based T2 fitting: the fitted water T2 relaxation as a function of B1, SNR and fat fraction.

SimulationTools up

The main purpose of this toolbox is to simulate DTI based DWI data and contains some functions to easily perform analysis of the fit results of the simulated signals (Froeling et al. 2013).

SpectroTools up

The main purpose of this toolbox is to process and visualize spectra data and allows to fit spectra using simulated basis spectra. Dynamic spectra and chemical shift data can be denoised using PCA based de-noising (Froeling et al. 2020).

Comparison of fitted and measured 31P spectra of muscle.

Resulting basis spectra of a fit of 31P spectra of muscle.

TensorTools up

The original toolbox where the project started. The main functions in this toolbox are to fit and evaluate the diffusion tensor model. Various fitting methods are implemented (e.g. LLS, NLS, WLLS, and iWLLS). The default method is an iterative weighted linear least squares approach (Veraart et al. 2013). The tensor fitting also includes outlier detections using REKINDLE (Tax et al. 2015) and data preparation includes drift correction (Vos et al. 2017).

Fitted tensor from DTI data of calf muscle.

MD and FA as a function of SNR and fat fraction. Results are from simulated data using an iWLLS algorithm with outlier rejection.

TractographyTools up

This toolbox provides functions to perform fiber tractography and fiber analysis. The toolbox is still under development and currently only the tractography algorithm is implemented in the release.

Fiber tractgraphy of the soleus muscle color coded for fiber direction.

TaggingTools up

Currently under development

VisteTools up

Import and export functions for tensor data which can be used in the vIST/e tractography tool.

References up

  • Abdul-Rahman, Hussein S., Munther A. Gdeisat, David R. Burton, Michael J. Lalor, Francis Lilley, and Christopher J. Moore. 2007. “Fast and robust three-dimensional best path phase unwrapping algorithm.” Applied Optics 46 (26): 6623. link.

  • Aja-Fernandez, Santiago, Marc Niethammer, Marek Kubicki, Martha E. Shenton, and Carl Fredrik Westin. 2008. “Restoration of DWI data using a rician LMMSE estimator.” IEEE Transactions on Medical Imaging 27 (10): 1389–1403. link.

  • Castillo, Andrés M., Luc Patiny, and Julien Wist. 2011. “Fast and accurate algorithm for the simulation of NMR spectra of large spin systems.” Journal of Magnetic Resonance 209 (2). Academic Press: 123–30. link.

  • Cerqueira, Manuel D., Neil J. Weissman, Vasken Dilsizian, Alice K. Jacobs, Sanjiv Kaul, Warren K. Laskey, Dudley J. Pennell, John A. Rumberger, Thomas Ryan, and Mario S. Verani. 2002. “Standardized myocardial sementation and nomenclature for tomographic imaging of the heart: A Statement for Healthcare Professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association.” Circulation 105 (4). Lippincott Williams & Wilkins: 539–42. link.

  • Froeling, Martijn, Aart J. Nederveen, Dennis F. R. Heijtel, Arno Lataster, Clemens Bos, Klaas Nicolay, Mario Maas, Maarten R. Drost, and Gustav J. Strijkers. 2012. “Diffusion-tensor MRI reveals the complex muscle architecture of the human forearm.” Journal of Magnetic Resonance Imaging 36 (1). Wiley Subscription Services, Inc., A Wiley Company: 237–48. link.

  • Froeling, Martijn, Aart J. Nederveen, Klaas Nicolay, and Gustav J. Strijkers. 2013. “DTI of human skeletal muscle: The effects of diffusion encoding parameters, signal-to-noise ratio and T2 on tensor indices and fiber tracts.” NMR in Biomedicine 26 (11): 1339–52. link.

  • Froeling, Martijn, Jos Oudeman, G. J. Gustav J. Strijkers, Mario Maas, M. R. Maarten R. Drost, Klaas Nicolay, and Aart J. A. J. Nederveen. 2015. “Muscle Changes Detected with Diffusion-Tensor Imaging after Long-Distance Running.” Radiology 274 (2): 548–62. link.

  • Froeling, Martijn, Chantal M. W. Tax, Sjoerd B. Vos, Peter R. Luijten, and Alexander Leemans. 2017. “MASSIVE brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation.” Magnetic Resonance in Medicine 77 (5). Milan: 1797–1809. link.

  • Froeling, M., Prompers, J. J., Klomp, D. W. J., & van der Velden, T. A. 2021. “PCA denoising and Wiener deconvolution of 31P 3D CSI data to enhance effective SNR and improve point spread function.” Magnetic Resonance in Medicine 85 (6) , link

  • Herraez, Miguel Arevallilo, David R. Burton, Michael J. Lalor, and Munther A. Gdeisat. 2002. “Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path.” Applied Optics 41 (35): 7437. link.

  • Jones, D. K., M. A. Horsfield, and A. Simmons. 1999. “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging.” Magnetic Resonance in Medicine 42 (3). link.

  • Klein, Stefan, Marius Staring, Keelin Murphy, Max A. Viergever, and Josien P. W. Pluim. 2010. “Elastix: A toolbox for intensity-based medical image registration.” IEEE Transactions on Medical Imaging 29 (1): 196–205. link.

  • Lee, Jee Eun, M. K. Chung, and A. L. Alexander. 2006. “Evaluation of Anisotropic Filters for Diffusion Tensor Imaging.” In IEEE International Symposium on Biomedical Imaging, 77–80. IEEE. link.

  • Damon, B. M., Ding, Z., Hooijmans, M. T., Anderson, A. W., Zhou, X., Coolbaugh, C. L., George, M. K., & Landman, B. A. (2021). “A MATLAB toolbox for muscle diffusion-tensor MRI tractography.” Journal of Biomechanics, 124, 110540. link

  • Marty, Benjamin, Pierre Yves Baudin, Harmen Reyngoudt, Noura Azzabou, Ericky C. A. Araujo, Pierre G. Carlier, and Paulo L. de Sousa. 2016. “Simultaneous muscle water T2and fat fraction mapping using transverse relaxometry with stimulated echo compensation.” NMR in Biomedicine 29 (4): 431–43. link.

  • Orton, Matthew R., David J. Collins, Dow-Mu Koh, and Martin O. Leach. 2014. “Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling.” Magnetic Resonance in Medicine 71 (1): 411–20. link.

  • De Luca, A., Bertoldo, A., & Froeling, M. (2017). “Effects of perfusion on DTI and DKI estimates in the skeletal muscle. Magnetic Resonance in Medicine, 78(1), 233–246. link

  • Reeder, Scott B., Angel R. Pineda, Zhifei Wen, Ann Shimakawa, Huanzhou Yu, Jean H. Brittain, Garry E. Gold, Christopher H. Beaulieu, and Norbert T. Pelc. 2005. “Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): Application with fast spin-echo imaging.” Magnetic Resonance in Medicine 54 (3): 636–44. link.

  • Shamonin, Denis. 2013. “Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease.” Frontiers in Neuroinformatics 7 (January): 50. link.

  • Stokes, Ashley M., Yesu Feng, Tanya Mitropoulos, and Warren S. Warren. 2013. “Enhanced refocusing of fat signals using optimized multipulse echo sequences.” Magnetic Resonance in Medicine 69 (4). Wiley-Blackwell: 1044–55. link.

  • Tax, Chantal M.W., Willem M. Otte, Max A. Viergever, Rick M. Dijkhuizen, and Alexander Leemans. 2015. “REKINDLE: Robust Extraction of Kurtosis INDices with Linear Estimation.” Magnetic Resonance in Medicine 73 (2): 794–808. link.

  • Veraart, Jelle, Els Fieremans, and Dmitry S. Novikov. 2016. “Diffusion MRI noise mapping using random matrix theory.” Magnetic Resonance in Medicine 76 (5): 1582–93. link.

  • Veraart, Jelle, Dmitry S. Novikov, Daan Christiaens, Benjamin Ades-aron, Jan Sijbers, and Els Fieremans. 2016. “Denoising of diffusion MRI using random matrix theory.” NeuroImage 142 (November). Elsevier Inc.: 394–406. link.

  • Veraart, Jelle, Jan Sijbers, Stefan Sunaert, Alexander Leemans, and Ben Jeurissen. 2013. “Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls.” NeuroImage 81 (November). Elsevier Inc.: 335–46. link.

  • Vos, Sjoerd B., Chantal M. W. Tax, Peter R. Luijten, Sebastien Ourselin, Alexander Leemans, and Martijn Froeling. 2017. “The importance of correcting for signal drift in diffusion MRI.” Magnetic Resonance in Medicine 77 (1): 285–99. link.

  • Weigel, Matthias. 2015. “Extended phase graphs: Dephasing, RF pulses, and echoes - pure and simple.” Journal of Magnetic Resonance Imaging 41 (2). Wiley-Blackwell: 266–95. link.

  • Keene, K. R., Beenakker, J. W. M., Hooijmans, M. T., Naarding, K. J., Niks, E. H., Otto, L. A. M., van der Pol, W. L., Tannemaat, M. R., Kan, H. E., and Froeling, M. “T2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms.” Magnetic Resonance in Medicine, 84(5), 2656–2670. link

  • Keene, K. R., Beenakker, J. W. M., Hooijmans, M. T., Naarding, K. J., Niks, E. H., Otto, L. A. M., van der Pol, W. L., Tannemaat, M. R., Kan, H. E., and Froeling, M. “T2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms.” Magnetic Resonance in Medicine, mrm.28290. link

  • Yu, Huanzhou, Ann Shimakawa, Charles A. McKenzie, Ethan Brodsky, Jean H. Brittain, and Scott B. Reeder. 2008. “Multiecho water-fat separation and simultaneous R*2 estimation with multifrequency fat spectrum modeling.” Magnetic Resonance in Medicine 60 (5): 1122–34. link.