We describe a standard set of quantity names and symbols related to the estimation of kinetic parameters from dynamic contrast-enhanced T 1 -weighted magnetic resonance imaging data, using diffusable agents such as gadopentetate dimeglumine (Gd-DTPA). These include a) the volume transfer constant K trans (min ؊1 ); b) the volume of extravascular extracellular space (EES) per unit volume of tissue v e (0 F v e F 1); and c) the flux rate constant between EES and plasma k ep (min ؊1 ). The rate constant is the ratio of the transfer constant to the EES (k ep ‫؍‬ K trans / v e ). Under flow-limited conditions K trans equals the blood plasma flow per unit volume of tissue; under permeability-limited conditions K trans equals the permeability surface area product per unit volume of tissue. We relate these quantities to previously published work from our groups; our future publications will refer to these standardized terms, and we propose that these be adopted as international standards.

Rapid T 1 -weighted 3D spoiled gradient-echo (GRE) data sets were acquired in the abdomen of 23 cancer patients during a total of 113 separate visits to allow dynamic contrast-enhanced MRI (DCE-MRI) analysis of tumor microvasculature. The arterial input function (AIF) was measured in each patient at each visit using an automated AIF extraction method following a standardized bolus administration of gadodiamide. The AIFs for each patient were combined to obtain a mean AIF that is representative for any individual. T 1 -weighted dynamic contrast-enhanced (DCE)-MRI is an established method for assessing microvascular changes associated with disease in tissues. It is most commonly used in cancer imaging (1-15), but has also been applied in a range of inflammatory conditions (16,17,41) and in cerebral (18) and cardiac (19) ischemia. Quantitative DCE-MRI has the potential to provide physiological information related to the functional status of tissue microvasculature. This information is available via the application of a tracer kinetic model-usually a compartmental model that describes the rate of transfer of contrast agent between the blood pool and the extracellular extravascular space (EES) (20).All models of contrast agent kinetics require the concentration of contrast agent in the blood pool (the arterial input function (AIF)) to be determined. Simple models assume a simplified functional form for the AIF, and additionally assume that the same functional form is valid for all individuals (16,21). However, it has been shown that using a simplified standard AIF leads to large systematic errors in model output parameters such as the volume transfer constant K trans and blood volume v b (22,42). Additionally, it is generally assumed that interpatient variability in factors such as heart rate and kidney function will lead to differences in the true form of AIF between individuals. An AIF that is accurately measured in each patient studied is therefore the accepted aim for kinetic modeling using contrast agents, even if it one that is met in only a minority of studies (6,13,23).In many settings it is not possible to perform an AIF measurement reliably, due either to data acquisition constraints or the lack of a suitable artery within the imaging field of view (FOV) from which to obtain an AIF. In such cases it would be desirable to utilize an assumed form of AIF that provides sufficient information to allow an accurate estimation of model parameters. Here we present a functional form of AIF that meets this requirement. We obtained this AIF from a population of 67 individually measured AIFs from the abdomens of 23 patients. We also show that the variability associated with the population of AIFs is low. Finally, we show that the use of the new functional form of the population AIF improves the reproducibility of tracer kinetic model parameters, and conclude that it is valid to use an assumed form of AIF if it is not possible to acquire AIFs from individual patients. MATERIALS AND METHODS PatientsTwenty-three patients (...

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing ‘translational gaps’ through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored ‘roadmap’. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

No abstract

In recent years a number of physiological models have gained prominence in the analysis of dynamic contrast-enhanced T 1 -weighted MRI data. However, there remains little evidence to support their use in estimating the absolute values of tissue physiological parameters such as perfusion, capillary permeability, and blood volume. In an attempt to address this issue, data were simulated using a distributed pathway model of tracer kinetics, and three published models were fitted to the resultant concentration-time curves. Parameter estimates obtained from these fits were compared with the parameters used for the simulations. The results indicate that the use of commonly accepted models leads to systematic overestimation of the transfer constant, K trans , and potentially large underestimates of the blood plasma volume fraction, V p . In summary, proposals for a practical approach to physiological modeling using MRI data are outlined. The last decade has seen a rapid development in the use of dynamic contrast-enhanced T 1 -weighted MRI in medicine. In tandem with the technological advances that have enabled improved data acquisition, a number of investigators have employed physiological models to facilitate data interpretation. While the use of these models has found numerous applications (e.g., in studies of tumor physiology (1) and myocardial perfusion (2)), and a number of groups have assessed the potential of model parameters as surrogate markers (3,4), little has been published that addresses the direct interpretation of these results. Specifically, how do the estimates obtained using the various models compare with the physiological parameters they purport to measure? This is not a simple question to address since it is often difficult to identify a reliable "gold standard." Many investigators compare their results with those obtained with positron emission tomography (PET). However, PET shares many of the basic models employed in MRI (5). Similarly, data simulation exercises using Monte-Carlo techniques designed to assess accuracy and precision in parameter estimation often utilize the same model to both generate and analyze the data (6,7). In this way, the sensitivity of the estimates to experimental variables, such as noise and sampling frequency, is assessed but little is revealed about the physiological significance of the resultant parameter estimates.A physiological model incorporating multiple parallel pathways and heterogeneous flow was used to simulate data of a realistic nature to which simplified models were fitted. The experiment was designed to assess the accuracy of the models themselves, not the quality of the data to which they are fitted (in terms of noise, sampling frequency, etc.), since data quality is essentially an experimental variable. Furthermore, this study was restricted to those models dealing with a contrast agent that diffuses out of the vascular space (thereby incorporating capillary permeability as a model parameter). Many of the issues associated with the analysis of data from...

These findings show considerable promise for isolating vascular characteristics of prostate cancer.