Metal Artifact Reduction by Using Dual-Energy Raw Data Constraint Learning

Kong, Fanning; Cheng, Ming; Wang, Ning; Cao, Huaisheng; Shi, Zaifeng

doi:10.1109/cisp-bmei53629.2021.9624233

Cited by 1 publication

(1 citation statement)

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“…This method is designed to obtain an improved SCT reconstruction if the signal is Poisson noisy, but MAR is not addressed. A few CNN-based SCT methods do address MAR [ 27 , 28 ]. They use a network with a U-Net architecture to take a dual energy signal and convert it (in different ways) into a virtual monochromatic image with significantly reduced metal artifacts.…”

Section: Introductionmentioning

confidence: 99%

Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning

et al. 2022

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Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.

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Section: Introductionmentioning

confidence: 99%