Importance

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Abstract

Embodiments disclosed herein provide systems and methods for automating the acquisition and analysis of contrast enhancement images. Model-free discrimination methods are provided in which discrimination between well-perfused pixels (e.g. normal tissue) and perfusion-deficient pixels (e.g. ablation lesions) is automated based on histogram shape. For example, in selected embodiments, discrimination is made based on pixels corresponding to abnormal perfusion (e.g. ablation lesions) and normal perfusion (e.g. normal tissue) form distinctive lobes separated by a minimum formed due to the presence of border pixels. Segmentation of cumulative dynamic contrast enhancement maps by thresholds identified on such histograms is employed to separate abnormally-perfused tissue from the normally-perfused tissue without any user interactions, freeing the user from the necessity to analyze and interpret original dynamic contrast enhancement images or maps derived from them. The histogram properties can also be used for automatic termination of the image acquisition and analysis processes.

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First Claim