DCE-MRI studies have shown great promise in several aspects of head and neck cancer management, including differential diagnosis and assessment of treatment response [3
]. To date, the general methods of analysis of the data have ranged from qualitative to quantitative analysis [3
]. Quantitative modeling requires accurate AIF measurements [7
]. Although it would be ideal to obtain AIFs from individual patients, which is often feasible, in many settings it is not possible to perform an AIF measurement reliably either due to data acquisition constraints or lack of a suitable artery within the imaging FOV from which to obtain an AIF [14
]. Parker et al suggested that in cases where a reliable AIF was not measured, a high-resolution population-averaged AIF can improve the reproducibility of parameters obtained using kinetic-modelling of DCE-MRI data and that in general only small changes in accuracy can be expected [14
]. This study was performed on twenty three patients with cancer in the abdomen [14
]. The authors commented that use of the Avg-AIF approach could be a useful alternative to the use of Ind-AIFs, especially for quantitative treatment studies where changes in microvascular properties are more important than the absolute values [14
]. Our group has recently published the use of limited population based Avg-AIF for DCE-MRI data analysis in osteosarcomas [15
]. In the present study we focus the use of the same principle on a different anatomic site with cancer i.e. head and neck. These preliminary studies can provide basis for large, validation studies and future application in clinical trials which use DCE-MRI parameters as non-invasive MR biomarkers. There is a need for widespread use of quantitative analysis of DCE-MRI data in order to compare and evaluate studies performed at different field strengths which would be independent of instrument platform and acquisition parameters.
Different groups have proposed various methods for analyzing DCE-MRI data for scenarios in which Ind- or Avg-AIFs cannot be obtained easily. These techniques may be an option for analysis of DCE-MRI data after appropriate validation or comparisons with standard methods. Riabkov et al [23
] estimated the kinetic parameters without input functions using multichannel blind identification methods and iterative quadratic maximum-likelihood (IQML) gave the most accurate estimates. Yankeelov et al [24
] and Walker-Samuel et al [25
] have proposed a method that compares the tissue of interest (TOI) curve shape to that of a reference region (RR), thereby eliminating the need for direct AIF measurements when a reliable AIF is not obtainable. Yang et al [27
] proposed the double-reference-tissue method, which assumes that the AIFs of the two reference tissues have the same shape. The elegant simulations used in their study need to be tested in more complicated tissues, such as tumor tissue.
Clinical requirements for diagnosis often dictate large imaging spatial coverage and high image resolution, which result in poor temporal resolution for DCE-MRI acquisition. Roberts et al [28
] showed that AIFs sampled at low temporal resolution introduced an unpredictable degree of error in the quantitative analysis. In such cases the use of Avg-AIF obtained from acquisitions with higher temporal resolution would be the method of choice. In the present study, the Avg-AIF was obtained from DCE-MR images of sufficient temporal resolution, and therefore it may be used to analyze DCE-MRI data that were acquired with poorer temporal resolution but otherwise with the same contrast injection set up, including dose, injection site and injection rate. In the present study ROIs were drawn on all arteries visible on the MR images but reliable AIF measurement was obtained only from the section that contained the central portion of the artery. This was because other sections showed small vessels containing only a small number of pixels, leading to a partial volume effect, or because other sections showed the bifurcation of the common carotid artery. Care was taken by the physicist during the selection process of Ind-AIFs so as to not pre-determine the outcome of the Avg-AIF, but rather to remove patient data that largely deviated from the expected contrast enhancement pattern.
A bi-exponential function was used for data analysis. Consistent use of this function for AIF curve fitting will not introduce systematic errors in longitudinal comparisons of changes in pharmacokinetic parameters caused by treatment. The use of an Avg-AIF implies that resulting parameter estimates will be higher than Ind-AIF estimates for some cases and lower for others. However, there is added benefit in the implicit gain in precision of the pooled or Avg-AIF and the possibility of parameter estimation when Ind-AIFs are not available. Although the sample size was small, other studies of cancers such as sarcoma have reported similar findings for Avg-AIF from small population [15
]. Further validation of the Avg-AIF approach with a larger population and in multiple regions is desirable.