Software, Image Data and Phantoms

Phantom Generator

PhantomGenerator is an extensible software phantom generator for MR image series emulation. It is written in C++ for linux and runs via the command line. Current capabilities include spoiled gradient echo and inversion recovery for MR signal production; diffusion weighted MR image signal production; and the extended Kety tracer kinetic model, Materne dual input tracer kinetic model, and two compartment exchange tracer kinetic model for physiological modelling. We have provided the source code to allow extension of the PhantomGenerator to other imaging scenarios. We have also provided a 64 bit release package for Linux to allow the current capabilities to be accessed without compilation.

Software resources:

Background publications:

A Banerji, A Caunce, Y Watson, CJ Rose, GA Buonaccorsi, GJ Parker. “A flexible software phantom for generating realistic dynamic contrast-enhanced MR images of abdominal tumours,” Proc. ISMRM, 493, 2008.

A Banerji, A Morgan, Y Watson, GA Buonaccorsi, GJ Parker. “Robust assessment of the sensitivity of DCE-MRI parameterisation to breathing motion,” Proc ISMRM, 1964, 2012.

Licensing

  • For academic, non-commercial use, PhantomGenerator is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3.

  • For commercial or non-academic use please contact Geoff Parker (see below).

Usage

The PhantomGenerator is available for research purposes only. Currently we do not have the resources to support the use of the software, which is made available 'as is'. There is a command line help option available, but no manual. We have also provided example inputs and outputs for guidance on it's use. 

Data sets available for download

  • A synthetic DCE-MRI data set of a liver tumour which includes motion emulation, and with ground truth physiology and motion parameters from acquired data. Zero mean Gaussian noise with a signal to noise ratio of 9 in the pre-contrast dynamic images in liver tissue was added to the data.

Synthetic DCE-MRI data


Please cite the following references if using this liver data set results in any publications:

KK Brock, LA Dawson, MB Sharpe, DJ Moseley, DA Jaffray. “Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue,” Int J Radiat Oncol Biol Phys 2006;64:1245-54.

KK Brock, MB Sharpe, LA Dawson, SM Kim, DA Jaffray. “Accuracy of finite element model based multi-organ deformable image registration,” Med Phys 2005;32:1647-59.

A Banerji, A Morgan, Y Watson, GA Buonaccorsi, GJ Parker. “Robust assessment of the sensitivity of DCE-MRI parameterisation to breathing motion,” Proc ISMRM, 1964, 2012.


  • synthetic DCE-MRI data set generated from masks with cubes for each tissue type and without motion emulation. Zero mean Gaussian noise with a signal to noise ratio of 10 for the mean signal in the pre-contrast dynamic images was added to the data. Input masks, config file and command line for the PhantomGenerator are also provided.

  


  • synthetic DCE-MRI data set generated from maps where the three free parameters of the tracer kinetic model are varied along the three axes of the image volume (no motion emulation or noise). Input masks, config file and command line for the PhantomGenerator are also provided. 

 


Contact information

Please contact us to find out more.  


Sparse Parametric Imaging

Sparse Parametric Imaging (SPI) allows quantification of tissue features from vastly undersampled data.

Software resources:

We will shortly be making available software that was used to enable the findings in the publications listed below.

Background publications:

R Little, GJ Parker, CJ Rose. "Sparse parametric imaging for direct parameter measurement: theory and phantom experiments," Proc. ISMRM, 4284, 2014.

HA Haroon, R Little, K Babalola, C Miller, N Sherratt, B Whitnall, T Cootes, C Taylor, GJ Parker, C Rose. "Measurement of morphological biomarkers using highly under-sampled k-space data without image reconstruction: application in left-ventricular end-diastolic volume assessment," Proc. ISMRM 4315, 2014.

RA Little, HA Haroon, KO Babalola, CA Miller, TF Cootes, CJ Taylor, GJM Parker, CJ Rose. "Direct estimation of morphological characteristics of biological structures from highly undersampled k-space measurements," Submitted.

Licensing

  • For academic, non-commercial use, our SPI software is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. Please note however, that you may require licenses to additional software packages and libraries in order to use the SPI software in its current form.

  • For commercial or non-academic use please contact Geoff Parker (see below).

Usage

The SPI resource set is available for research purposes only.

Data sets available

The data used in the generation of the above publications is available for research use. As the imaging data are large we are unable to make these directly available via this page. However, we are happy to share these data on receipt of a direct request via the contact methods below. Please cite the references listed above if you make use of any of these SPI data.

Funding

This work has been funded by the Wellcome Trust 091369/Z/10/Z and UMI3.

Contact information

Please contact us to find out more.


Tissue Mimetic Phantoms for Diffusion MRI

We have developed tissue mimetic phantoms for use with diffusion MRI. The novel materials involved mimic the important features of cellular microstructure with a high degree to geometrical accuracy and therefore allow testing of models of tissue microstructure and for calibration of ADC and diffusionanisotropy measurements.

P.L. Hubbard, F.-L. Zhou, S.J. Eichhorn, G.J.M. Parker. Biomimetic Phantom for the Validation of Diffusion Magnetic Resonance Imaging. Magn. Reson. Med. 73(1), 299-305, (2015). doi: 10.1002/mrm.25107.

FL Zhou, PL Hubbard, SJ Eichhorn, GJ Parker. "Coaxially electrospun axon-mimicking fibers for diffusion magnetic resonance imaging." ACS Applied Materials and Interfaces 4(11): 6311–16, 2012. doi: 10.1021/am301919s

F-L Zhou, PL Hubbard, SJ Eichhorn, GJM Parker. Jet deposition in near-field electrospinning of patterned single component and core-shell fibres. Polymer 52(16), 3603-3610,(2011).


Obtaining Phantom Materials

We are open to collaborations regarding the use of our materials fro diffusion MRI purposes or other tissue mimicking applications.

Funding

This work has been funded:

Contact information

Please contact us to find out more.

Co-electrospinning


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