Post-processing methods for dynamic contrast agent acquisitions offer an improved understanding of the underlying tissue. Post-processing methods encompass a number of image processing and pharmaco-kinetic modeling techniques that lead to the estimation of physiologically relevant semi-quantitative and quantitative parameters from the acquired dynamic set of images. Some of the post-processing methods are broadly applicable to several clinical applications that include cardiovascular, tumor, and kidney imaging.
A common first post-processing step in the contrast agent acquisitions is image registration or motion compensation of the dynamic image series. Motion compensation across multiple time frames helps visually identify abnormalities in the washin and washout temporal dynamics of the contrast agent. Motion compensation also helps minimize errors when fitting the tracer-kinetic models to the time curves. A number of motion compensation methods have been proposed for DCE acquisitions, for example, methods in [1-5], and is still an interesting and an active area of research [6]. Motion compensation techniques can also be used to improve image sharpness and overall quality of each time frame. One example of such an improvement is obtained by reducing inconsistencies in k-space data occurring due to motion during data acquisition [7].
Segmentation of images is a common pre-step in order to extract time curves from regions of interest in the tissue and for the arterial input function (AIF). While manual segmentation is a commonly used approach, alternative semi-automatic and fully automatic segmentation methods have been proposed [8-12]. Signal intensity time curves extracted from the segmented regions of interest are converted to contrast agent concentration curves and fit to a kinetic model for estimation of quantitative parameters. In DCE acquisitions care should be taken to correct for T2* effects in AIF [13]. Alternatives ways to obtaining AIF, instead of from a region of interest, include blind estimation [14-17] and population-based methods [18-20].
Model-fitting is the next step that extracts semi-quantitative or quantitative physiological parameters from the contrast agent concentration time curves. Several kinetic models exist, choice of the model [21] and the type of fitting, non-linear or linear [22, 23], can affect the computation time, analyses and interpretation of the extracted parametric maps [24].
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