Combination of model-free and model-based analysis of dynamic contrast enhanced MRI for breast cancer diagnosis

Dynamic contrast enhancement (DCE) is the leading technique in magnetic resonance imaging for cancer detection and diagnosis. However, there are large variations in the reported sensitivity and specificity of this method that result from the wide range of contrast-enhanced MRI sequences and protocols, image processing methods, and interpretation criteria. Analysis methods can be divided to physiological based models that take into account the vascular and tissue specific features that influence tracer perfusion, and to model free algorithms that decompose enhancement patterns in order to segment and classify different tissue types. Inhere we present a general hybrid method for analyzing dynamic contrast enhanced images integrating a mathematical, model-free technique with a model derived approach that characterizes tissue microvasculature function. We demonstrate the application of the method for breast cancer diagnosis. A brief description of this approach was recently presented for the diagnosis of prostate cancer. The model free method employed principal component analysis and yielded eigen-vectors of which two were relevant for characterizing breast malignancy. The physiological relevance of the two eigen-vectors was revealed by a quantitative correlation with the model based three time point technique. Projection maps of the eigen-vector that specifically related to the wash-out rate of the contrast agent depicted with high accuracy breast cancer. Overall, this hybrid method is fast, standardized, and yields parametric images characterizing tissue microvascular function. It can improve breast cancer detection and be potentially extended as a computer-aided tool for the detection and diagnosis of other cancers.