Dynamic contrast-enhanced magnetic resonance (DCE-MRI) is a functional imaging technique. It consists of MRI scans coupled to the injection of a contrast agent. The latter leads to a decrease in relaxation time and provides extremely detailed characteristics of the micro-circulation of blood through tissue.
DCE-MRI assessments typically use the characteristics of signal intensity (SI) and time-intensity curves (TIC) regarding regions of interest (ROI). Early DCE-MRI efforts assumed a linear relationship between signal enhancement and contrast uptake. However, given that signal enhancement depends to a very great degree on intrinsic tissue and acquisition parameters, more complex models have been developed to control the effect of tissue characteristics such as the pre-contrast longitudinal relaxation time and the longitudinal or transverse relaxivities of the contrast agent.
DCE-MRI is a two-phased process. Typically, at first, a T1-weighted MRI scan is conducted. This is followed by injection of the contrast agent, and then repeated acquisition of T1-weighted fast spoiled gradient-echo MRI sequences to obtain measurements of signal enhancement as a function of time.
The contrast agents are usually based on gadolinium and include gadoterate meglumine (Gd-DOTA), gadobutrol (Gd-BT-DO3A) gadoteriol and albumin-labelled Gd-DTPA.
Image acquisition and voxel comparison
Typically, 3D image sets are obtained sequentially every few seconds for up to 5-10 minutes. Shorter intervals allow for detection of early enhancement, although many researchers consider 10 seconds to be good enough. Longer intervals than this typically makes it tougher to identify early enhancement.
At the moment, the debate about the upper limit for intervals continues.
After image acquisition, the comparison of T1 values per voxel in each scan allows identification of permeable blood vessels and tumour tissue. Both spatial and temporal resolution must be adjusted to obtain an adequate sampling of the contrast enhancement over time, for each tissue voxel. The speed with which MRI images must be acquired necessitates larger voxels, so as to maintain adequate signal-to-noise ratios. Thus, DCE-MRI is often not as high in resolution as conventional T2-weighted sequences.
Range of biomarkers
Although DCE-MRI can be performed on conventional scanners (typically 1.5 T), it requires specialist image analysis to analyse the enhanced biomarker information which is to be provided. Such information includes tissue perfusion, vascularity, endothelial permeability, cellularity etc.
The biomarkers can be used to provide measurements of tumour vascular function and to improve the diagnosis and management of diseases in a variety of organs.
DCE-MRI in the brain
Clinical applications of DCE-MRI have principally focused on in-vivo characterization of tumours.
One of its earliest applications was to analyse blood vessels in a brain tumour, since the blood-brain barrier (BBB) blocks the contrast agent in normal brain tissue, but not in vessels generated by a tumour.
The contrast agent’s concentration is measured as it passes between the blood vessels and the extracellular space of tissue, and then as it returns to the vessels. In tissues with healthy cells or high cell density, the re-entry of the contrast agent into vessels is quicker since it cannot pass cell membranes. In tissues which are damaged or have a lower cell density, the agent is present in the extracellular space for a longer duration.
Numerous DCE-MRI studies on the brain have researched the correlation between BBB disruption and diseases such as acute ischemic stroke, pneumococcal meningitis, brain metastases, multiple system atrophy, multiple sclerosis and Type-II diabetes. One of the most exciting areas of research is the difference in signal intensity profiles over time between Alzheimer’s disease patients and controls.
Tumours and DCE-MRI
Elsewhere, researchers have also established the benefits of DCE-MRI for differential diagnosis of tumours in the head and neck region, such as salivary gland tumours and lesions in the jaw bone. DCE-MRI has also been used to demonstrate the nature of a lymphoma and making a differential diagnosis versus other lesions.
Prostate cancer is becoming a major area of application for DCE-MRI. One of the key limitations to standards of care in the past was the need for random prostate biopsies after discovery of elevated PSA values. This often led to discovery of inconsequential tumours. Meanwhile, the very same biopsies sometimes missed out on significant disease. DCE-MRI, in conjunction with PSA, can identify tumours likely to cause death if left untreated.
Assessing response to chemotherapy
DCE-MRI is also being used to assess responses to chemotherapy. One example of an ongoing project in this area is CHERNAC (Characterizing Early Response to Neoadjuvant Chemotherapy with Quantitative Breast MRI), which is funded by the Breast Cancer Campaign in the UK.
Elsewhere, DCE-MRI has shown promise in detecting cancer recurrence. For example, biochemical relapse after radical prostatectomy can occur in as much as 15 to 30percent of prostate cancer patients. Detection of tumour recurrence in such cases can be difficult due to the presence of scar tissue. Determining the precise site of recurrence since patients with isolated recurrence could benefit from less-invasive treatments, such as radiation to the resection bed.
Other areas for DCE-MRI application include cardiac tissue viability – for example, to evaluate sub-clinical fibrosis and micro-vascular dysfunction. Researchers have also shown its utility in measuring renal function and partial/segmental liver function.
A full spectrum of methods
In general, the analysis of DCE-MRI is based on a full spectrum of methods from the qualitative to quantitative, with an intermediary semi-quantitative approach.
Qualitative analysis is visual and depends on clinical experience and expertise. It assumed that tumour vessels are leaky and more readily enhance after IV contrast material is expressed. As a result, DCE-MRI patterns for malignant tumours show an early and rapid enhancement of the time-intensity curve (TIC) after injection of the agent, followed by a rapid decline. On the other hand, normal tissue shows a slower and steadily increasing signal after agent injection.
Quantitative analysis is based on the pharmacokinetics of contrast agent exchange. It is complex, but allows for a degree of comparability. The limitation is due to a lack of standards. However, better and wider use of software has led to a growing consensus on approaches to quantitative analysis of DCE-MRI data.
One of the most widely used tools is the Toft and Kermode (TK) model, which is showing considerable promise in predicting and monitoring tumour response to therapy.
TK provides data about the influx forward volume transfer constant, KTrans, from plasma into the extravascular-extracellular space (EES). Ktrans is equal to the permeability surface area product per unit volume of tissue, and represents vascular permeability in a permeability-limited situation (high flow relative to permeability), or blood flow into tissue in a flow-limited situation (high permeability relative to flow). KTrans is known to be elevated in many cancers.
Pharmacokinetic modeling for analysing DCE-MRI dates to the early 1990s, and was followed by a consensus paper at the end of the decade ( Tofts P.S., Brix G., Buckley D.L., Evelhoch J.L., Henderson E., Knopp M.V. Contrast-enhanced T 1 -Weighted MRI of a diffusible tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging. 1999′).
Over the years, improvement of imaging techniques (e.g. higher temporal resolution and contrast-to-noise ratio) and greater knowledge of the underlying physiology have catalysed development of more complex pharmacokinetic models.
The TK model, for example, had been developed for measuring BBB (blood-brain barrier) permeability, and overlooked the contribution of the plasma to total tissue concentration. However, as the model gained popularity in assessing tumours throughout the body, vascular contributions to signal intensity were also included.
The semi-quantitative model seeks to fit a curve to data. Like the visual/qualitative, this approach also assumes early and intense enhancement and washout as a predictor of malignancy. However, semi-quantitative analysis also calculates a variety of dynamic curve parameters types after initial uptake, such as the shape of the time-intensity curve (TIC), the time of first contrast uptake, time to peak, maximum slope, peak enhancement, and wash-in and washout curve shapes.
Broadly speaking, there are three types of curve: Type 1 (persistent increase), Type 2 (plateau) and Type 3 (decline after initial upslope). One of the most attractive features of the semi-quantitative model is its relative simplicity in using parameters to differentiate malignant from pathologic but benign tissue.
For example, in the head-and-neck region, a rapid increase in TIC (fast wash-out pattern) indicates a strong possibility of Warthin’s tumour – a benign, sharply demarcated tumour. A persistent increase suggests the possibility of pleomorphic adenoma. A plateau pattern with a slow washout is characteristic of both a malignant tumour and adenoma.
In spite of enthusiasm about the semi-quantitative approach, it cannot be generalized across acquisition protocols and sequences as well as several other factors which impact on MR signal intensity. In turn, these affect curve metrics, such as maximum enhancement and washout percentage. Differences in temporal resolution and injection rates can also change the shape of wash-in/washout curves, making comparison difficult. Finally, such descriptive parameters provide no physiologic insights into the behaviour of the tumour vessels.
The limitations of DCE-MRI
DEC-MRI itself faces some major limitations. Firstly, there is a lack of standardization in DCE-MRI sequences and analysis methodology, making it difficult to compare published studies. In general, shorter acquisition times lend themselves to more comparability.
One frequent problem is movement by the patient and organ motion (e.g. in the gut, the kidney, bladder etc.). Since a DCE-MRI study procedure is over 5 minutes, there can be considerable misregistration between consecutive imaging slices, leading to noise in the wash-in and washout curves, and problems fitting pharmacokinetic models to the curve.
New DCE-MRI postprocessing software seeks to correct this by automatically repositioning sequential images for better alignment. However, these too do not use common algorithms to process the data and generate parametric maps and can show differences – e.g. in tumour vascularity. To enable further investigation of the value of DCE-MRI of the prostate, the technique of DCE-MRI and the pharmacokinetic model used to analyse it must become more standardized.
One of the most serious problems with DCE-MRI, however, is its non-specificity which can lead to to both false negatives and false positives.
Other sources of uncertainty in DCE-MRI studies include a lack of data. For example, one typical assumption is fast water exchange between compartments in spite of suspicions about the influence of restricted water exchange. Indeed, many quantitative models disregard intracellular space since it is assumed that there is no contrast media exchange. However, others have pointed out that water itself can exchange between the cell and the extracellular space, thereby influencing signal changes in the extracellular space. This is clearly an areas which calls for more study.
Further research is also required in areas such as relaxivity values for a contrast agent, field strength and tissue/pathology. Currently, relaxivity across tissues and compartments is generally assumed to be uniform.
To conclude, DCE-MRI is a significant and promising diagnostic modality. However, for most clinical applications, it cannot be used on a standalone basis, regardless of curve shape or intensity of enhancement. DCE-MRI needs to be viewed in the context of other MRI parameters such as diffusion-weighted MRI and MR spectroscopic imaging as well as T2-weighted MRI.