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.
Balda
Michael
M
Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany.
Heismann
Björn
B
Hornegger
Joachim
J
D000465:Algorithms; D016477:Artifacts; D006801:Humans; D011829:Radiation Dosage; D011835:Radiation Protection / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015900:Radiography, Dual-Energy Scanned Projection / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*