Sparse-view CT spatial resolution enhancement via a densely connected convolutional neural network with spatial guidance

Nimu Yuan; Jian Zhou; Jinyi Qi

doi:10.1117/12.2611818

4 April 2022 Sparse-view CT spatial resolution enhancement via a densely connected convolutional neural network with spatial guidance

Nimu Yuan, Jian Zhou, Jinyi Qi

Author Affiliations +

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120312F (2022) https://doi.org/10.1117/12.2611818
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

The temporal resolution of x-ray computed tomography (CT) is limited by the scanner rotation speed and detector readout time. One way to reduce the detector readout time is to acquire fewer number of projections. However, reconstruction using sparse-view data could result in spatial resolution loss and reconstruction artifacts that may negatively affect the clinical diagnoses. Therefore, improving the spatial resolution of sparse-view CT (SVCT) is of great practical value. In this study, we proposed a deep learning-based approach for SVCT spatial resolution enhancement. The proposed method utilizes a densely connected convolutional neural network (CNN) that is further aided by a radial location map to recover the radially dependent blurring caused by the continuous rotation of an x-ray source. The proposed method was evaluated using sparse-view data synthesized from full-view projection data of real patients. The results showed that the proposed CNN was able to recover the resolution loss and improve the image quality. Compared with the network using the same main structure but without a radial location map, the proposed method achieved better image quality in terms of the mean absolute error and structure similarity.

Citation Download Citation

Nimu Yuan, Jian Zhou, and Jinyi Qi "Sparse-view CT spatial resolution enhancement via a densely connected convolutional neural network with spatial guidance", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120312F (4 April 2022); https://doi.org/10.1117/12.2611818

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