Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

IF 3.2 2区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

EJNMMI Physics Pub Date : 2026-04-19 DOI:10.1186/s40658-026-00880-6

Koki Takamura, Koichi Ishizu, Chio Okuyama, Tomohiro Ueno, Naozo Sugimoto

引用次数: 0

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通过对数据驱动门控PET进行深度学习训练，有效消除呼吸相位严格匹配的PET/CT病例中由呼吸误配引起的衰减校正误差。

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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来源期刊

EJNMMI Physics Physics and Astronomy-Radiation

CiteScore

6.70

自引率

10.00%

发文量

审稿时长

13 weeks

期刊介绍： EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.