Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN
| Autor: | Jong Chul Ye, Junghyun Lee, Jawook Gu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Radiological and Ultrasound Technology
Computer science business.industry Feature vector Deep learning Supervised learning Pattern recognition Iterative reconstruction Computer Science Applications Reduction (complexity) Metal Artifact Metals Feature (computer vision) Image Processing Computer-Assisted Unsupervised learning Attention Artificial intelligence Electrical and Electronic Engineering Artifacts Tomography X-Ray Computed business Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 40:3932-3944 |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2021.3101363 |
| Popis: | Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning approaches for image reconstruction, various deep learning methods have been suggested for metal artifact reduction, among which supervised learning methods are most popular. However, matched metal-artifact-free and metal artifact corrupted image pairs are difficult to obtain in real CT acquisition. Recently, a promising unsupervised learning for MAR was proposed using feature disentanglement, but the resulting network architecture is so complicated that it is difficult to handle large size clinical images. To address this, here we propose a simple and effective unsupervised learning method for MAR. The proposed method is based on a novel β -cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Moreover, by adding the convolutional block attention module (CBAM) layers in the generator, we show that the metal artifacts can be more focused so that it can be effectively removed. Experimental results confirm that we can achieve improved metal artifact reduction that preserves the detailed texture of the original image. |
| Databáze: | OpenAIRE |
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