Improved Contrast-Enhanced MRA

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Summary Contrast-enhanced magnetic resonance angiography (CEMRA) has become a routine clinical tool for pretreatment mapping of vasculature. Among data acquisition techniques for CEMRA, the time-resolved strategy offers a very useful option for many situations because of the elimination of the cumbersome timing of imaging to the contrast bolus arrival. The time-resolved CEMRA generates time series images in a manner similar to fluoroscopic X-ray angiography, where image postprocessing has been used frequently to improve vasculature display. For example, from the time-series images all mask images and arterial phase images can be summoned into one image of greater vascular detail with high signal-to-noise ratio that is particularly useful for presentation in a surgical operation room where video display may not be available. Linear filtering techniques such as the matched filters can be used to produce a summation image and have been attempted in time-resolved or dynamic CEMRA to generate a summary arteriogram. In practice, the major challenge for summarizing time series images is to identify the contrast bolus arrival and to avoid motion-corrupted mask images and arterial phase images that propagate severe motion artifacts into the final summation image. So far, this avoidance of motion-corrupted images and selection of optimal arterial phase and mask images for summation have been performed through a tedious manual procedure. A team from the Weill Cornell Medical College's Department of Radiology has created an algorithm that can fully automate the linear filtering process, i.e., to select the set of arterial phase images and the set of mask images such that the subtracted image from the former to the latter is of the best quality. The algorithm allows quantification of the notion of image quality, and 2) effectively selects the mask and arterial phase images based on the quantified measure of quality.