A Machine Learning Strategy to Improve the Fidelity of Imaging Time-Varying Signals to Improve Clinical Imaging

This technology includes a new technique to improve the fidelity of time-varying signals acquired in the dynamic contrast enhanced (DCE) imaging. This technique enhances the time-varying signals in a given DCE image series through deep convolutional neural networks (CNN) to learn the relationship of signal versus contrast concentration from other series of different contrast doses. The method includes an automatic framework to detect target regions of interest (ROI), match key signal-time frames, and register anatomical structures in different DCE series to train the CNN and produce an image series with improved signal fidelity. Depending on specific ROI in the DCE images, signal fidelity may include the linearity of the signal versus contrast concentration, or measurements of image quality. The method automatically selects key signature frames in two calibrated DCE image series and matches only those frames in the training. Once properly trained, the inference network can be applied to the entire DCE image series to generate a synthetic DCE image series that has improved fidelity.