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Dual-energy subtraction angiography (DESA) in the interventional setting promises to reduce motion-induced DSA artifacts and support iodine quantitation. A prototype dual-energy C-arm system with 30 Hz kV-switching capability is currently under development. The purpose of this work was to investigate methods of noise reduction in 2D iodine/boneonly images generated on this platform. The methods investigated were anti-correlated noise reduction (ACNR), which exploits noise correlations in iodine/bone and tissue-only images, and a hybrid algorithm (ACNR-ML) that employs machine learning to achieve further reduction in low-frequency noise compared to ACNR. An anthropomorphic chest phantom and a porcine angiographic study were used to evaluate iodine signal-difference-to-noise-ratio, vessel profile and noise texture in DESA images with and without denoising. ACNR iodine images were formed by subtracting a weighted high-pass filtered tissue image from the iodine image, where in practice the high-pass tissue image was the original minus a low-pass-filtered version. The ACNR-ML algorithm replaced the low-pass-filtered tissue image with a denoised tissue image produced by a convolutional neural network (U-Net). When compared to standard weighted log subtraction, the ACNR algorithm yielded an iodine SDNR improvement factor ranging from 2.58 to 2.91 and 3.66 to 4.08 in the phantom and porcine studies, respectively. The ACNR-ML algorithm yielded SDNR improvement from 2.82 to 3.44 in the phantom study and 5.41 to 6.08 in the porcine study. Both noise reduction techniques resulted in vessel full-width half-maximum measurements that were within 3% of the measurements from non-denoised images.
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