Development of Dynamic Contrast-Enhanced MRI

“Dynamic Contrast-Enhanced MRI”
Introduction
One of the most significant non-invasive imaging modalities applied both in research and clinical diagnostics cis Magnetic Resonance Imaging (MRI). Its widespread use is partially based on its characteristic to visualize tissues with high resolutions in 3D and its ability to provide anatomical, functional and metabolic tissue information in vivo (Strijkers, Mulder, van Tilborg, & Nicolay, 2007). In an MR image, the basic contrast mostly derives from regional differences in the intrinsic T1, T2 relaxation times, except for local water content differences. T1 and T2 relaxation times can be selected independently to have a commanding influence on image contrast. Nevertheless, a sensitive and accurate diagnosis cannot always be feasible, due to the fact that the intrinsic water, T1 and T2 contrast values are modified and become very often limited by tissue pathology. Consequently, the need for enhanced image contrast led to the growing use of intravenously injected MRI contrast agents, whose use although violates partially the non-invasive character of MRI brought about significant benefits. Combining MRI and contrast agents (CA) increases the possibilities to image inflamed tissues in pathologies, such as arthritis, atherosclerotic plaques, and tumor angiogenesis (Strijkers, Mulder, van Tilborg, & Nicolay, 2007).
Definition of DCE-MRI
A technique which combines MRI and contrast agents is Dynamic Contrast-Enhanced MRI (DCE-MRI). According to Gordon et al. (Gordon, et al., 2014), “DCE-MRI analyzes the temporal enhancement pattern of a tissue following the introduction of a paramagnetic contrast agent into the vascular system. This is accomplished by the acquisition of baseline images without contrast enhancement, succeeded by a set of images acquired over time (usually over a few minutes) during and after the arrival of the contrast agent in the tissue of interest”.

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A time intensity curve (TIC) for the tissue is generated by the acquired signal, as it can be seen in Figure 1. In a TIC, the response of the tissue is represented in enhancement values to the arrival of the contrast agent. Specific physiological properties that are in association with the microvascular blood flow, including tissue volume fractions, vessel permeability, and vessel surface area product, can be extracted by analyzing a TIC (Gordon, et al., 2014).

Figure 1: An example of a time intensity curve obtained from a tumor metastasis (Bonekamp & Macura, 2008).
All variations of DCE-MRI studies are relied on a rather plain fundamental principle: the MR signal intensity of a tissue is modified, when a paramagnetic particle (contrast agent) penetrates and spreads over through the tissue, based on its local concentration (Gordon, et al., 2014).
MR images of a chosen region of interest (ROI) are obtained in time intervals of few seconds before, during, and after the intravenous injection of a contrast agent. Each obtained image represents one time point, and each and every pixel in a set of images produces its own intensity curve. After the injection of the CA, the signal intensity varies at every time point (is related to the concentration of the CA in the tissue) based on tissue parameters, including vascularization, vessels’ permeability and surface area product, and in this way parametric maps of particular microvascular biomarkers can be extracted. Furthermore, by using suitable mathematical models absolute values of the aforementioned parameters can be estimated. These parameters usually reflect a compartmental pharmacokinetics model demonstrated by CAs, which are allocated between the intravascular and extravascular spaces as it can be seen in Figure 2 (Gordon, et al., 2014).

Figure 2: Toft’s compartmental model for calculating DCE-MRI quantitative pharmakokinetic parameters (Verma, et al., 2012).
DCE-MRI techniques
Currently, two DCE-MRI techniques are defined based on its registration and the origin of the extracted signal. As MRI is highly sensitive to small concentrations of paramagnetic materials passing through a tissue, there are two different physical-chemical properties (Gordon, et al., 2014).

Relaxation effect

T1, T2 tissue relaxation times are reduced when a diffusible contrast agent is used. Positively enhanced T1-weighted images are generated, when this effect is used and the studies evaluating this effect are characterized asDynamic Contrast Enhanced(DCE)-MRI,T1-W DCE.

Susceptibility effect

When a paramagnetic contrast agent is located in the intravascular space of a tissue and its magnetic susceptibility is much higher than that of the surrounding tissue water, local magnetic inhomogeneities between the intra and extravascular space emerge, which generate negative enhanced T2 or T2* weighted images during the passage of the CA through the capillaries. Studies depending on this phenomenon are characterized asDynamic Susceptibility Contrast(DSC)-MRI or T2*-W DCE.
Image Acquisition
Gordon et al. (Gordon, et al., 2014) state that the method of quantification to be applied depends on the number of the measurements, which are required in order to obtain the data; thus, the measurements include:
I. Creating a map of pre-contrast native T1 values, which is necessary in order to calculate the CA concentrations.
II. Acquiring heavily T1-weighted images, prior and following the Contrast Agent introduction. In this case, high temporal resolution is needed in order to have the ability to further characterize the kinetics of the contrast agent’s entry and exit of the tissue. Typically, 3D image sets are acquired sequentially for 5–10 minutes every few seconds. The ideal for the acquisitions would be to be obtained approximately every 5 seconds, in order to allow the detection of early enhancement. With longer acquisitions (for instance, > 15 seconds), it becomes harder to detect early enhancement.
III. Acquisition of the arterial input function (AIF), in order to estimate the CA concentration in the blood plasma of a feeding artery as a function of time. Acquiring the AIF is necessary for almost all quantitative analysis methods and is up to now technically the most difficult part in the data acquisition process.
Contrast agents
The most regularly used group of contrast agents in DCE-MRI is the low molecular paramagnetic gadolinium (Gd) chelates (Gribbestad, Gjesdal, Nilsen, Lundgren, Hjelstuen, & Jackson, 2005). Principally, in Dynamic Contrast-Enhanced MRI, any low molecular weight CAs can be used. (Tofts). The use of contrast agents with high molecular weights leads to lower permeability and lower Ktrans values, since these agents remain in the intravascular space. Using macromolecular CAs the measurement of regional blood volume acquiring scans of low temporal resolution is feasible (Gribbestad, Gjesdal, Nilsen, Lundgren, Hjelstuen, & Jackson, 2005). Molecular agent with high molecular weight might be more appropriate for tumor angiogenesis and thus offer better response evaluation to therapy (Turkbey, Thomasson, Pang, Bernardo, & Choyke, 2010).
Analysis Methods
Gordon et al. (Gordon, et al., 2014) state that “the arrival of CA and thus the enhancement pattern of the tissue depend on a wide variety of factors including vascularity, capillary permeability, perfused capillary surface area, volume and composition of extracellular fluid, renal clearance and perfusion. The analysis of DCE data can provide valuable information concerning the vascular status and perfusion”. Data analysis can be performed using either: qualitative, semi-quantitative, and quantitative approach (Verma, et al., 2012).
Qualitative – This kind of analysis can range from visual inspection of the images for fast and extreme enhancement of lesions, to the plotting of kinetic curves of signal intensity against time (Gupta, Kauffman, Polascik, Taneja, & Rosenkrantz, 2013). The qualitative analysis of DCE-MRI depends on the assumption of rapid and intense enhancement and wash-out as indicator of the existence of a tumor. The tumor vessels are generally leakier and more readily enhanced after the injection of the CA than the ordinary vessels. An early rapid high enhancement after injection is expected followed by a relatively rapid decline compared with a slower and continuously increasing signal for normal tissues during the first few minutes after contrast injection. However, the possibility for an overlap between the natural and the malignant tissues, limit the capabilities of this DCE-MRI approach. Finally, the qualitative approach is regarded as a subjective approach and therefore difficult to standardize among institutions, constituting multicenter trials less reliable (Verma, et al., 2012).
Semi-quantitative – The semi-quantitative approach also depends on the same assumption as the qualitative approach. On the other hand, in the semi-quantitative analysis various curve parameters are integrated (Verma, et al., 2012). It must be mentioned that depending on the application area, different perfusion parameters are relevant. Nevertheless, some parameters are of general interest for almost all applications. These parameters are acquired to characterize the shape of the TIC, including the time of first arrival of the CA, peak enhancement ( PE – the maximum value normalized if the baseline is subtracted), time to peak (TTP – the timepoint where peak enhancement takes place), integral (the area between the baseline and the curve, indicating with PE if blood supply is reduced in a ROI), mean transit time (MTT – the timepoint where the integral is bisected), slope (the curve’s steepness during wash-in phase, downslope (the descending curve’s steepness in wash-out phase ) and wash-in and wash-out curve shapes (Figure 1, Figure 3A). (Preim et al., 2009). Three common dynamic curve types exist in the literature after the initial CA uptake: type 1, persistent increase; type 2, plateau; and type 3, wash-out after initial slope, as it can be seen in Figure 3B and Figure 1. Even though the semi-quantitative approach is used widely in the evaluation of DCE-MRI, significant restrictions arise dealing with the factors contributing to the MR signal intensity (e.g. generalization across acquisition protocols, sequences), which have an effect on the curve metrics (Verma, et al., 2012).

Figure 3: A) A typical TIC curve (Preim et al., 2009). B) Differentiation of three patterns of washout phase: type 1 (blue), progressive; type 2 (green), plateau ; type 3 (red), wash-out (Verma, et al., 2012).
Factors like the injection rate and the temporal resolution can easily alter the shape of a wash-in/washout curve, creating difficulties in comparison and quantitation. High inter-patient variability is also a factor that can make the definition of threshold values more complex for every parameter that could standardize semi-quantitative approach. However, this approach is relatively simple which makes it even more appealing (Verma, et al., 2012).
Quantitative – The quantitative approach depends on modeling the concentration change of the CA by integrating pharmacokinetic modeling techniques (Gordon, et al., 2014). Several pharmacokinetic models were proposed, such as by Tofts (Tofts), Brix et al. (Brix et al., 1991). Most of them depend on estimating the exchange rate between extracellular space and blood plasma using some transfer rate constants, like Ktrans(forward volume transfer constant) andkep(reverse reflux rate constant between extracellular space and plasma). “The transfer constant,Ktrans, is equal to the permeability surface area product per unit volume of tissue.Moreover, Ktransdetermines the flux from the intravascular space to the extracellular space; it may principally represent the vascular permeability in a permeability-limited situation (high flow in relation to permeability), or it may represent the blood flow into the tissue in a flow-limited situation (high permeability in relation to flow). Theveis the extracellular extravascular volume fraction, andkep=Ktrans/ veexpresses the rate constant, describing the efflux of contrast media from the extracellular space back to plasma. Thevpis the fraction of plasma per unit volume of tissue”, according to Verma et al. (Verma, et al., 2012).
In quantitative DCE-MRI analysis, a four compartment model is used for “tissue”: plasma, extracellular space, intracellular space, and renal excretory pathway (Figure 2). This pharmacokinetic model is applied to the CA concentration changes in the artery (AIF) supplying the tissue of interest, and the CA concentration of the tissue. It must also be noted that due to the fact that pharmacokinetic models require concentration values, signal intensity must be converted to T1 values, because MRI signal intensity is not linear with the CA concentration (Verma, et al., 2012).
Clinical Applications of DCE-MRI
DCE-MRI has been used for the detection and characterization of tumors in the clinical setting. It also makes the monitoring of tumor treatment and the response to conventional chemotherapy and angiogenic therapies feasible by acting as biomarker (Figure 4). Early tumor detection and treatment affects significantly the survival of patients. DCE-MRI is applied increasingly in a wider range of patients with different kind of cancer, including breast, head and prostate cancer. The method’s quantification ability of characteristics of the lesion microvasculature has stimulated the scientists to use the technique for “in-vivo staging” of tumors. According to early studies in the field, an evident relationship was demonstrated between large and rapid increases in malignant behavior and signal enhancement in tumors located in prostate, breast, and head. Additionally, important overlapping of contrast enhancement patterns has been noticed between malignant and benign tumors. Growing accuracy and specificity in the recognition of microvascular characterization parameters is expected to further ameliorate lesion characterization (Gribbestad, Gjesdal, Nilsen, Lundgren, Hjelstuen, & Jackson, 2005).
More specifically regarding prostate cancer detection and localization, DCE-MRI contributes to prostate MRI, succeeding higher specificity and sensitivity than T2-weighted MR imaging, and sextant u ltrasound guided biopsy, methods being used widely for the pre-treatment work up and screening of prostate cancer respectively (Choi, Kim, & Kim, 2007; Bonekamp & Macura, 2008). It has been proven that the multi-parametric approach has improved significantly the accuracy of prostate MRI and has a great future.
In a cancerous tissue, the number of vessels and their permeability are increased in comparison with normal tissues. Moreover, the interstitial space is greater. These factors cause significant increase of contrast enhancement parameters, such as MTT, blood flow, interstitial volume. The aforementioned observations are applicable in prostate cancer, too. As it can be seen in Figure 3B, the red curve could represent a prostate cancer with faster and steeper enhancement and faster wash-out than in normal tissues.

Figure 4 a-c (Turkbey, Thomasson, Pang, Bernardo, & Choyke, 2010): a) A patient with prostate cancer. The arrow indicates a low signal intensity focus on axial T2W MR image B) Increased enhancement shown by the lesion on axial T1W DCE-MR image C) fusion of color-coded Ktrans
Conclusion
The determination of functional microvascular parameters by using DCE-MRI might be instrumental in evaluating many vascular diseases. The potential of the technique to assess the severity of illnesses, to non-invasively and in parallel measure multiple relevant parameters, to study the pathophysiology of diseases, seems to be extremely promising.
Even though, the method is known for over 20 years it is still considered immature. This has mainly to do with the significant variations in data analysis and acquisition protocols from study to study. Furthermore, the analysis of the pharmacokinetic parameters is a complex task and computationally expensive, due to the existence of plethora of analysis algorithms (Gordon, et al., 2014). DCE-MRI is restricted in organs with physiologic motion, including lungs and liver, and may not be applicable in some specific group of patients, especially those with renal failure and claustrophobia (Turkbey, Thomasson, Pang, Bernardo, & Choyke, 2010). However, although the extraction of quantitative pharmacokinetic parameters is more difficult, compartmental model based methods are more robust than the semi-quantitative approaches, and offer deeper understanding of physiology. Finally, they are not potentially based on the scanning technique, the type of scanner, and individual patient variations (Gordon, et al., 2014).