深度学习重建单一材料物体的少视角 X 射线 CT 测量结果，并在增材制造中进行验证

IF 3.2 3区工程技术 Q2 ENGINEERING, INDUSTRIAL

Cirp Annals-Manufacturing Technology Pub Date : 2024-01-01 DOI:10.1016/j.cirp.2024.04.079

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引用次数: 0

摘要

高质量 XCT 测量所需的大量采集时间仍然是高通量检测任务的绊脚石。因此，本文提出了一种深度学习重建算法，以提高快速、少视图 XCT 测量的质量。本文提出的方法在颅颌面加成制造植入物的模拟和实验 XCT 测量中得到了验证。验证结果表明，与少视角采集相关的噪声和条纹伪影大幅减少。因此，在将采集时间缩短一个数量级以上的同时保持高重建质量的潜力得到了证实。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep learning reconstruction of few-view X-ray CT measurements of mono-material objects with validation in additive manufacturing

The large acquisition times needed for high-quality XCT measurements remain a stumbling block for high-throughput inspection tasks. This paper therefore presents a deep learning reconstruction algorithm to improve the quality of fast, few-view XCT measurements. The proposed method is validated on both simulated and experimental XCT measurements of additively manufactured cranio-maxillofacial implants. The validation demonstrates a drastic reduction in noise and streaking artifacts associated with few-view acquisitions. Therefore, the potential to maintain high reconstruction quality while reducing acquisition times by more than one order of magnitude is confirmed.

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来源期刊

Cirp Annals-Manufacturing Technology 工程技术-工程：工业

CiteScore

7.50

自引率

9.80%

发文量

137

审稿时长

13.5 months

期刊介绍： CIRP, The International Academy for Production Engineering, was founded in 1951 to promote, by scientific research, the development of all aspects of manufacturing technology covering the optimization, control and management of processes, machines and systems. This biannual ISI cited journal contains approximately 140 refereed technical and keynote papers. Subject areas covered include: Assembly, Cutting, Design, Electro-Physical and Chemical Processes, Forming, Abrasive processes, Surfaces, Machines, Production Systems and Organizations, Precision Engineering and Metrology, Life-Cycle Engineering, Microsystems Technology (MST), Nanotechnology.