Value-Based Noise Reduction for Low-Dose Dual-Energy Computed Tomography
We introduce a value-based noise reduction method for Dual-Energy CT applications. It is based on joint intensity statistics estimated from high-and low-energy CT scans of the identical anatomy in order to reduce the noise level in both scans. For a given pair of measurement values, a local gradient ascension algorithm in the probability space is used to provide a noise reduced estimate. As a consequence, two noise reduced images are obtained. It was evaluated with synthetic data in terms of quantitative accuracy and contrast to noise ratio (CNR)-gain. The introduced method allows for reducing patient dose by at least 30％ while maintaining the original CNR level. Additionally, the dose reduction potential was shown with a radiological evaluation on real patient data. The method can be combined with state-of-the-art filter-based noise reduction techniques, and makes low-dose Dual-Energy CT possible for the full spectrum of quantitative CT applications
Michael Balda Bjorn Heismann Joachim Hornegger
Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, GermanySiemens Healthcare, Erlang
国际会议
北京
英文
547-554
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）