Multi-frame-based Cross-domain Image Denoising for Low-dose Computed Tomography

要約

タイトル: 低線量コンピュータ断層撮影における多フレームベースのクロスドメイン画像除去法

要約:
– 線量低減の必要性から、コンピュータ断層撮影（CT）においてノイズとアーティファクトを取り除くことが研究されている
– これまでの研究は、Radon変換に基づく模擬データによるものが多かったが、現実世界のCT画像とシミュレーションドメインの画像はかなり異なるため、現実のCT画像に対して適用することは難しい
– 本論文では、商用化された第三世代多スライススパイラルCTスキャナに対し、クロスドメインでの低線量CT画像除去のための二段階法を提案
– マルチスライス投影とボリューム再構築の高い冗長性を生かし、情報の崩壊を避けることができる
– 多くの評価により、他の最新の技術に比べ優れた性能を示すことができた

要約(オリジナル)

Computed tomography (CT) has been used worldwide for decades as one of the most important non-invasive tests in assisting diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation dose has driven researchers to improve the reconstruction quality, especially by removing noise and artifacts. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the potential of learning-based methods, most of them were developed on the simulated data collected using Radon transform. However, the real-world scenario significantly differs from the simulation domain, and the joint optimization of denoising with the modern CT image reconstruction pipeline is still missing. In this paper, for the commercially available third-generation multi-slice spiral CT scanners, we propose a two-stage method that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our method makes good use of the high redundancy of both the multi-slice projections and the volumetric reconstructions while avoiding the collapse of information in conventional cascaded frameworks. The dedicated design also provides a clearer interpretation of the workflow. Through extensive evaluations, we demonstrate its superior performance against state-of-the-art methods.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI