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Reconstruction of micro CT-like images from clinical CT images using machine learning: a preliminary study
Author(s): Kyohei Takeda; Yutaro Iwamoto; Keisuke Uemura; Masaki Takao; Nobuhiko Sugano; Yoshinobu Sato; Yen-Wei Chen
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Paper Abstract

High-resolution medical images are crucial for medical diagnosis, and for planning and assisting surgery. Micro computed tomography (micro CT) can generate high-resolution 3D images and analyze internal micro-structures. However, micro CT scanners can only scan small objects and cannot be used for in-vivo clinical imaging and diagnosis. In this paper, we propose a super-resolution method to reconstruct micro CT-like images from clinical CT images based on learning a mapping function or relationship between the micro CT and clinical CT. The proposed method consists of following three steps: (1) Pre-processing: This involves the collection of pairs of clinical CT images and micro CT images for training and the registration and normalization of each pair. (2) Training: This involves learning a non-linear mapping function between the micro CT and clinical CT by using training pairs. (3) Processing (testing) step: This involves enhancing a new CT image, which is not included in the training data set, by using the learned mapping function.

Paper Details

Date Published: 9 March 2018
PDF: 7 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057339 (9 March 2018); doi: 10.1117/12.2293443
Show Author Affiliations
Kyohei Takeda, Ritsumeikan Univ. (Japan)
Yutaro Iwamoto, Ritsumeikan Univ. (Japan)
Keisuke Uemura, Osaka Univ. (Japan)
Masaki Takao, Osaka Univ. (Japan)
Nobuhiko Sugano, Osaka Univ. (Japan)
Yoshinobu Sato, Nara Institute of Science and Technology (Japan)
Yen-Wei Chen, Ritsumeikan Univ. (Japan)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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