Rotating projection-based localizer radiograph: enable sparse sampling for dose-saving and scan speed enhancement with transformer combined CNN

Kairui Feng; Yougu Yang; Yi Tian

doi:10.1117/12.3006877

1 April 2024 Rotating projection-based localizer radiograph: enable sparse sampling for dose-saving and scan speed enhancement with transformer combined CNN

Kairui Feng, Yougu Yang, Yi Tian

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252E (2024) https://doi.org/10.1117/12.3006877
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

The technique of Localizer Radiography (LR) can realize patient oriented automatic exposure control based on attenuation information. Rotating Projection based Localizer Radiography (RPLR), as a dynamic tube positioned scanning, aims to improve the whole clinical workflow. However, topogram (topo) reconstruction in RPLR is affected by sparse sampling. This paper proposed a deep learning model which contains transformers (power in modeling long-term relationship) and CNNs (high texture modeling capacity) to implement projection context restoration for topo reconstruction. With a coarse topo prior generated by the transformers based on sparse sampling data, high-fidelity topo texture can be rendered with CNNs, which reveals great potential for topo reconstruction in RPLR.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kairui Feng, Yougu Yang, and Yi Tian "Rotating projection-based localizer radiograph: enable sparse sampling for dose-saving and scan speed enhancement with transformer combined CNN", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252E (1 April 2024); https://doi.org/10.1117/12.3006877

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