Convolutional Neural Network based Metal Artifact Reduction in X-ray Computed Tomography

By Yanbo Zhang, Hengyong Yu. IEEE Transactions on Medical Imaging, 2017

446 citations recorded by the source corpus.

In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists of two phases. In the CNN training phase, we build a database consisting of metal-free, metal-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of metal implants.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/833ec94c0b95a961accf93f624e8d17e09f381e3

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “In [120], Zhang et al. demonstrate this eﬀectively for metal artifacts.”
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, unknown): “struction [122] and metal artifact reduction [123, 124].”