DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data
© Ortuño et al.; licensee BioMed Central Ltd. 2013
Received: 18 July 2013
Accepted: 28 October 2013
Published: 4 November 2013
DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.
Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains.
A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/.
KeywordsDCE-MRI Imaging Levenberg-Marquardt Fitting Preclinical Pharmacokinetics Animal models High field MR IDL
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) involves the acquisition of sequential images in rapid succession during and after the intravenous administration of a, usually, low-molecular weight contrast agent (CA), which includes a paramagnetic component such as gadolinium (Gd3+). This functional imaging modality has proven to be useful in tumor differentiation, being a sensitive marker of antiangiogenic treatment effect [1, 2].
When T1-weighted magnetic resonance (MR) sequences are used, the CA induces a signal enhancement related with the shortening of spin-lattice or longitudinal relaxation time (T1), the time course of which can be related to physiological parameters. The most common CA used in T1-weighted DCE-MRI, Gadolinium-diethylenetriamine penta-acetic acid (Gd-DTPA), is able to transverse the vascular endothelium (except when the blood-brain barrier is intact) and enter the extravascular-extracellular space (EES), but is unable to cross the cellular membrane. Thus, in DCE-MRI the measured signal intensity changes derive mostly from CA that extravasates to the EES [3, 4]. The dynamics of exchange between the capillary bed and the EES can be evaluated and are usually modeled as an open two-compartment model, dependent on the washout rate between EES and plasma (k ep ), and the volume transfer constant between plasma and EES, denoted as K t r a n s .
DCE-MRI has been used to investigate permeability and perfusion in small animal tumor models [6, 7]. A key consideration in rodents is that the concentration of CA in vascular plasma evolves rapidly compared to tissue, and is quite difficult to sample the maximum signal intensity to effectively characterize the tissue pharmacokinetics. Since sampling the blood (the gold standard in humans) is very invasive in small animals, kinetic models that do not rely on arterial input function (AIF) measurements are desirable in preclinical DCE-MRI.
Therefore, the software application presented in this manuscript is aimed at filing this gap and providing a powerful and versatile T1-weighted DCE-MRI processing tool, and at the same time, intuitive and easy-to-use in preclinical studies. It has been implemented in Interactive Data Language (IDL), accessible at http://www.exelisvis.com/idl.
The DCE@urLAB application integrates pixel-wise pharmacokinetic analysis using the following models: Tofts , Hoffmann , Larsson , and a reference region (RR) model . The Tofts pharmacokinetic model has been widely applied to characterize murine tumors [12–14], as well as the Hoffmann pharmacokinetic model [15, 16]. The Larsson model has not been extensively applied to small animal DCE-MRI, but is the third model typically used in theoretical studies and reviews [5, 17]. Finally, the RR model has been proposed as an alternative when AIF cannot be precisely estimated.
Model-based and semi-quantitative analysis of T1-weighted DCE-MRI can be performed with general purpose pharmacokinetic compartmental analysis packages, either non-commercial, like WinSAAM , JPKD , or commercial, like SAAM II . These are complex tools that require specific training and need to be adjusted to the particular problem of DCE-MRI. Pixel-wise analysis and ROI selection of images are also not included in these platforms.
BioMap is built in IDL, and supports compartmental analysis over ROIs through the perfusion tool. Two ROIs must be defined, one describing the CA tissue-concentration and the other the concentration of the CA in blood plasma (C p ). When C p cannot be measured in an ROI, either because the image does not contain a large blood vessel, or the signal from the blood vessel is corrupted by pulsation, movement or saturation effects, a theoretical bi-exponential decay function can be used as C p . Published results with DCE-MRI using BioMap include small animal studies [12, 26, 27]. Although BioMap can generate pixel maps, it does not work with coarse resolutions and is limited to the Tofts model, with a bi-exponential model of C p .
PermGUI and PCT  are freeware applications oriented to extract the permeability coefficient of the blood brain barrier (BBB) in human patients. The tools analyze DCE-MRI images using the Patlak model . This model is also used in the package Toppcat, which runs as a plugin of ImageJ . Toppcat is also free of charge for educational and research purposes.
DcemriS4  is a collection of shell scripts to help automate the quantitative analysis of DCE-MRI and diffusion weighted imaging (DWI), and written in the R programming environment . Kinetic parametric estimation is performed with the Tofts model and non-linear regression, Bayesian estimation or deconvolution algorithms. AIF is parameterized with a tri-exponential function  to obtain an analytical solution of the convolution integral and increase computational efficiency.
DATforDCEMRI  is an R package tool which allows performing kinetic deconvolution analysis  and visualizing the resulting pixel-wise parametric maps. Like DcemriS4, this software package requires an end-user training in R programming environment.
These software packages are primarily designed for human studies and thus are not well suited for some typical requirements of preclinical DCE-MRI, e.g., the difficulty in accurately measuring the AIF in small animals makes that typical models in human studies cannot be used and ultimately requires the use of the Hoffmann or RR models. These models are not implemented in available software packages. Other important functionalities such as the difficulty in reading the imaging format produced by preclinical studies prevent from the use of those packages by the preclinical research community. Thus, in-house solutions are commonly used in DCE-MRI small animal studies, using Matlab programming environment [33, 34], LabView  or IDL [36–39], but they are mostly designed for a specific study and with limited availability.
In this section, the compartmental models implemented in the DCE@urLAB analysis tool are described. Additional information and technical details can be found in the “DCEurLAB Methods.pdf” document included in the software package, accessible at http://www.die.upm.es/im/archives/DCEurLAB/ and in the Additional file 1. This section also includes a brief description of the graphical user interface (GUI) usage.
DCE-MRI pharmacokinetic modeling
where C e is the CA concentration in EES, C t is the total CA concentration in the tissue, v p is the fractional volume of blood plasma, and v e =K t r a n s /k ep is the fractional volume of EES. The physiological meaning of K t r a n s depends on the biological mechanism of CA exchange (i.e., blood flow, permeability, or a mixed case). If no prior information about the tissue is available, then is prudent to leave the interpretation open.
The Tofts model produces reliable results if the tissue is weakly vascularized, while the extended Tofts model can also be applied to highly perfused tumors . It is important to note that the quantification of Tofts parameters requires the estimation C p (t) from the acquired MR signal. Thus, an additional MRI model has been included in the DCE@urLAB application and is discussed later.
where S(t) is the MR signal course from tissue and S0 is the MR signal before CA injection. The fitting parameters are: k ep ; A H , which approximately corresponds to the size of the EES; and k el , the renal elimination constant.
where is the initial slope of the MR signal and S0 the MR signal value prior to CA injection. C p is approximated as a sum of N exponentials with amplitudes a i and time constant m i .
where Ct,r is the concentration of CA in the RR tissue and Kt r a n s,r and ve,r are the quantitative parameters for the RR.
and CA concentration in tissue is calculated from Equation 7. Note that r1 and T10 must be known to quantify the tissue concentration from the MR signal. T10 may be estimated using the ratio of two spin-echo images collected with different TR. The estimation error can be reduced with a higher number of images with a least-squares minimization algorithm.
Estimation of model parameters
Curve fitting routines have been implemented using internal IDL functions and the freely available MPFIT IDL library . MPFIT contains a set of non-linear regression algorithms for robust least-squares minimization, based on the freely available MINPACK package (Univ. of Chicago, http://www.netlib.org/minpack/) a library of FORTRAN subroutines for solving nonlinear equation systems.
DCE@urLAB uses the Levenberg-Marquardt algorithm (LMA)  to perform the non-linear least squares regression in each pixel of the analyzed ROI. LMA has demonstrated robustness in the pharmacokinetic modeling of DCE-MRI . LMA is used to estimate appropriate parameters in several models: Tofts (with bi-exponential C p of Equation 2 or solving the discrete convolution Equation 3); the equivalent extended Tofts model; Hoffmann (Equation 4); Larsson (Equation 5); and also the RR model (Equation 6).
Pixel-based processing of dynamic MRI data can be demanding in terms of memory and CPU, and hardware requirements will vary depending on the size of data sets, as well as the number of pixels selected. In any case, it is recommended to run the program in systems with at least 2 GB of RAM memory. In addition to pharmacokinetic modeling, model-free semi-quantitative analysis can be performed, including IAUC (initial area under curve), RCE (relative contrast enhancement) and TTM (time to max enhancement) .
Description and use of the GUI
The software tool accepts DCE-MRI sequences and auxiliary inputs: T10 maps, AIF data and pre-calculated ROIs. Interface functionality is disabled until a 4-dimensional DCE-MRI study is open. The tool considers the sequence set to be a 4D stack of images in X-Y-Z-time order. Data can be imported from DICOM format, Bruker Biospin MRI data format (http://www.bruker.com/products/mr/mri.html), as well as from binary unformatted data. If the dynamic MR sequence is loaded properly, the interface will show a single 2D slice of the whole 4D data set in the left display tab, and a relative contrast enhancement (RCE) image in the right display tab (Figure 2).
The platform is specially designed to perform ROI or pixel-wise analysis over the selected ROI belonging to a single slice in the Z dimension (Z-slice for short). These ROIs can be exported in a custom format and subsequently imported in another work session. When required for a specific MRI model, T10 maps can be loaded from the menu file tab. AIF data can also be imported from previously saved sessions or external acquisitions.
Displaying data sets
After loading a valid DCE-MRI sequence, main processing options and menus will become activated. The user is now able to select ROIs, change parameters, as well as configure visualization options. Nevertheless, other options will not be activated until a valid ROI is drawn or imported.
The user can navigate through dynamic frames or Z-slices to select an active ROI for the pharmacokinetic analysis. The color palette of both MRI and RCE displays can be changed by selecting this option on the menu bar (options drop down menu). The user can additionally change the brightness, contrast, alpha channel, etc. Pressing mouse buttons on the display images produces different actions depending on the ROI selection mode. When the ROI selection mode is not activated, the actions allowed are:
Pressing the right mouse button on any image will plot the dynamic MR signal course of the pointed pixel.
If the left mouse button is pressed over the MRI window, the value of the current pixel appears in the information label located at the bottom of the MRI window tab.
When the left mouse button is pressed on the RCE image, the RCE value (%) of the current pixel will be shown in the associated information label.
Selecting and defining ROIs
If the ROI selection mode is activated, right and left mouse buttons are used to manually place ROIs in the selected slice. The ROI types can be Box, Full or Free-drawing type. The ROI definition depends on the type of ROI selected. If a Box-type is selected, the upper left and bottom right corners of the ROI are defined by pressing the left mouse button over the image, or alternatively, typing their X and Y coordinates in editable text fields. If the Full ROI type is selected, the current Z-slice is then defined as a ROI. In the Free-drawing ROI type, the user moves the pointer while pressing down the left mouse button over the image to manually delineate the contour of the ROI. The ROI can be deleted in every moment using the New ROI button and starting again. Finally, the user must also choose the resolution in the Z-slice, i.e., select the pixel size for processing options. The finest resolution corresponds to the intrinsic resolution of the image, but the user can also select coarser resolutions from 2 ×2 to 10 ×10 pixels in the Z-slice (x-y plane). This option allows a direct comparison with other applications using low-resolution maps. The selected ROIs are currently limited to a single Z-slice.
Processing input parameters should be checked before each ROI analysis to obtain accurate results. Input parameters are organized in tabs (located on the lower-right of the main window interface). Each tab groups a set of related parameters. The MR signal tab contains MRI data related constants (e.g., frame period, repetition time, etc.). The AIF tab groups the parameters used in the bi-exponential model for the CA concentration in blood plasma proposed by Tofts. The CA tab must be completed with information concerning the injected contrast (e.g., injection frame, relaxivity, injected dose, etc.). Finally, the RR tab contains additional data used in the reference region model. These input parameters will be used or not depending on the pharmacokinetic study selected, e.g., the AIF tab is only read when the Tofts model is applied.
Pharmacokinetic processing and analysis
Complementary results and data can be accessed from the menu bar, e.g., in the Export/import drop-down menu, several options can be selected to export images shown on the screen, ROI kinetics, or the set of parametric values of the selected ROI. Single column, multiple column, and matrix format are available.
Validation using simulated data
Results with Tofts model applied to QIBA test data
Results with Extended Tofts model applied to QIBA test data
Example with mouse brain tumor
The platform has been tested over real acquisitions of T1-weighted DCE-MRI small animal data. Two different C57BL/6 mouse models have been used in this study. First, a genetically engineered mouse (GEM, S100-v-ErbB; Ink4a-Arf(+/-)), female, age 40 weeks, bearing a Schwannoma (confirmed by histopathological studies carried out by Dr. Martí Pumarola, Murine Pathology Unit, Centre de Biotecnologia Animal i Teràpia Gènica, UAB). Animals from this colony generally develop oligodendrogliomas , although a small percentage of animals can develop other tumour types . The second model studied was a mouse bearing a stereotactically-induced GL261 glioblastoma, described elsewhere [55, 56], age 20 weeks.
A bolus of CA (Gd-DTPA –Magnevist, Bayer Schering Pharma AG, Berlin, Germany–, 50 mM in saline, 0.2 mmol/kg, 10 s duration) was manually injected after acquiring five pre-contrast images. A series of 41 dynamic spin-echo images was acquired with temporal resolution of 51.2 s per frame and the following parameters: TR/TE, 200/5 ms; field of view, 17.6 ×17.6 mm2; slice thickness, 1 mm; in-plane resolution, 138 ×138 μm/pixel. The studies were carried out at the joint NMR facility of the Universitat Autònoma de Barcelona and CIBER-BBN (Cerdanyola del Vallès, Spain), using a 7 T horizontal magnet (BioSpec 70/30; Bruker BioSpin, Ettlingen, Germany).
Computational implementation and requirements
DCE@urLAB has been implemented in a flexible and modular way, so that the addition of new analysis models is straightforward. The different models can also be used as inline functions to allow flexibility of use and batch programming of multiple studies for advanced users.
Regarding complexity, the optimization (LMA) performed in each pixel has a global algorithmic complexity bound dependent on stopping criterion and number of maximum iterations. The algorithmic complexity by iteration is determined by the cost function (i.e., the pharmacokinetic model) through the calculation of its jacobian matrix. It has been experimentally verified that the computing time needed to perform a pharmacokinetic analysis depends linearly on the number of pixels contained in the ROI and the number of dynamic frames of DCE dataset. This behaviour is expected since the average number of iterations of the LMA does not substantially change for large number of pixels. For example, in a 2.8 GHz Intel Quad Core CPU with 8 GB RAM personal computer, it took 20 seconds to fit a ROI of 1024 pixels and 40 dynamic frames to the Tofts model. Although unrealistic, because tumor ROIs are smaller, the complete analysis using the Tofts model of the whole DCE dynamic slice (128×128=16384 pixels) and 40 dynamic frames, took about 5 minutes in the personal computer formerly described. A maximum of 1.5 GB RAM was required in this case. Should more computer power be required (e.g., with higher resolution images), the program could be easily parallelized and several cores used.
DCE@urLAB is designed to run under Microsoft Windows XP/Vista/7 (both 32 and 64 bits). In order to use the application tool, IDL (version 6.4 or posterior) must have been installed. Another possibility is to install the IDL virtual machine (version 6.4 or posterior), which can be downloaded freely and does not require a license.
Up to date there is no friendly software application for pixel-wise and ROI analysis of DCE-MRI data that can apply different pharmacokinetic models in a preclinical environment. DCE@urLAB is a user-friendly software designed to fulfill the potential needs of the preclinical DCE-MRI community. It has been focused on the analysis of T1-weighted DCE-MRI studies, and tested and optimized according to the requirements of preclinical data analysis. The proposed tool has also been specially designed for easy selection of multi-pixel ROIs. The platform incorporates the compartmental pharmacokinetic models of Tofts, Hoffmann, Larsson, and RR, complemented with non-parametric analysis. Pixel-wise and ROI options allow the user to choose from a variety of forms and pixel sizes (i.e., resolutions). If required by the model, AIF and T10 maps can also be estimated from the acquired data. DCE@urLAB reads multi-slice DCE-MRI data from proprietary and binary raw formats. Results can be exported as color maps superimposed to the DCE image, or as text files that can easily be read with other statistical software packages. Individual pixel and ROI dynamic curves can also be visualized, for easy expert interpretation and pharmacokinetics validation. The most relevant and used models in literature (Tofts models) have been validated with publicly available simulated data. Preliminary experiments have been conducted using T1-weighted DCE-MRI dynamic data from tumor-bearing mouse brains. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/ and in Additional files 1 and 2.
Availability and requirements
Project name: DCE@urLAB 1.0Project home page:http://www.die.upm.es/im/archives/DCEurLAB/Operating system(s): Microsoft Windows 7/Vista/XPProgramming language: IDLOther requirements: IDL 6.4 or higher, IDL Virtual Machine 6.4 or higherLicense: BSD license
Arterial input function
Bulk magnetic susceptibility
Central processing unit
Diffusion weighted imaging
Extracellular extravascular space
Gadolinium-diethylene-triamine penta-acetic acid
Genetically engineered mouse
Graphical user interface
Initial area under curve
Interactive Data Language
Magnetic resonance imaging
Random access memory
Relative contrast enhancement
Region of Interest
Time to max enhancement.
This work was partially supported by Spain’s Ministry of Science & Innovation through CDTI-CENIT (AMIT project), INNPACTO (PRECISION & XIORT projects), PHENOIMA (SAF 2008-0332), MARESCAN (SAF 2011-23870), projects TEC2010-21619-C04-03 & TEC2011-28972-C02-02; Comunidad de Madrid (ARTEMIS S2009/DPI-1802), and IMAFEN (2008–2009), PROGLIO (2010–2011) and PROGLIO2 (2012–2013), intramural projects of CIBER-BBN, with contribution from European Regional Development Funds (FEDER). CIBER-BBN is an initiative funded by the VI National R&D&i Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.
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