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Abstract #2827

Laplacian pyramid based data fusion for high resolution dynamic MRI

Liad Pollak Zuckerman1, Lior Weizman2, Yonina C. Eldar2, Dafna Ben Bashat3, Moran Arzi3, and Michal Irani1

1Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel, 2Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel, 3Tel Aviv Medical Center, Tel Aviv University, Tel Aviv, Israel

Dynamic contrast-enhanced (DCE) MRI is useful for tumor diagnosis and treatment. In DCE, there is a tradeoff between the spatial and temporal resolutions. Improving the spatial resolution while preserving the temporal dynamics is essential for better diagnosis/treatment. We present a method (LAPFUD) for enhancing the spatial frequency without compromising on temporal resolution. LAPFUD combines information from a static high-resolution image acquired at baseline, with each low-resolution frame. By making local decisions it preserves details from both inputs without changing the temporal behavior. Experiments show that LAPFUD provides superior performance (spatially and temporally) compared to the commonly used keyhole method.

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