DCE Imaging

How do you evaluate the information obtained from a DCE imaging study?  
DCE imaging data can be evaluated by one of three methods: simple visual assessment, semi-quantitative descriptive indices, or model-based quantification. 
Simple visual assessment of contrast enhancement at various time points is the method most widely used by clinical radiologists. Inspection begins by looking for areas in the dynamic series where contrast appears first. Both patterns of contrast uptake and estimated wash-in/wash-out rates from a lesion can provide clues as to its nature. 
Different portions of a dynamic contrast enhancement curve reflects different anatomic and physiological features. The initial steep upslope of the curve correlates with tissue blood flow and its peak height reflects total blood flow and volume. The next portion of the curve is due to contrast leakage into the tissue interstitium and thus is a function of capillary area and permeability. Late portions of the curve reflect the total tissue extracellular space and plasma interstitial volume.
Picture
Contributions to total contrast enhancement as a function of time
Semi-quantitative descriptive indices from the raw signal intensity data can also be applied to this data, similar to those used for DSC imaging. Such parameters may include arrival time (AT), time to peak (TTP), wash-in rate (WIR), wash-out rate (WOR), area under contrast curve (AUC), maximum enhancement (ME), and percent maximum enhancement (%ME) compared to baseline. Although the absolute values of these indices are meaningless, they may be useful when compared to signal intensity changes in other lesions or to the normal tissue background.
Full quantitative analysis of the DCE data requires three steps: 1) conversion of signal intensity to gadolinium concentration; 2) selection of an appropriate tissue model; and 3) estimation of model parameters from the fitted data. Potentially important quantitative tissue parameters obtainable include vascular permeability, gadolinium exchange rates, blood flow, and volume of the tissue extracellular space. The details of how these parameters are calculated and their physiological significance are the subjects of the next several Q&A's.
References    
     Choi YJ, Kim JK, Kim N, et al. Functional MR imaging of prostate cancer. RadioGraphics 2007; 27:65-77. (use of semi-quantitive DCE parameters to detect prostate cancer.)   
     Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 1999; 211:101-110. (original paper describing Type I, II, and III kinetic patterns of contrast uptake in breast tumors).
     Schnall MD, Blume J, Bluemke DA, et al. Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 2006; 238:42-53. (showed Kuhl classification not so accurate as thought).
     Verma S, Turkbey B, Muradyan N, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol 2012; 198:1277-1288.
Related Questions
     Can myocardial perfusion be accurately quantified?
     What pulse sequences are used to perform a DCE study?  
     How do calculated DCE parameters relate to patterns of enhancement we see on clinical images?