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Quantitative analysis of dopamine transporter imaging using generating MR image from low dose CT image and segmentation by deep learning
Author(s): Shogo Yokoi; Takeshi Hara; Tetsuro Katafuchi; Masaki Matsusako; Xiangrong Zhou; Hiroshi Fujita
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Paper Abstract

In the dopamine nerves of the nigrostriatal body in the brain, 123I-FP-CIT binds to dopamine transporter (DAT), the distribution of which can be visualized on a single photon-emission computed tomography (SPECT) image. The Tossici-Bolt method is generally used to analyze SPECT images. However, since the Tossici-Bolt method uses a fixed region of interest, it is susceptible to the influence of non-accumulated parts. Magnetic resonance (MR) images are effective for recognizing the shape of the striatal region. Here we used MR images generated by deep learning from low-dose CT images taken with SPECT/CT devices. The purpose of this study was to perform a quantitative analysis with high repeatability using the striatal region extracted from automatically generated MR images. First, an MR image was generated from a CT image by pix2pix. After that, a striatal region was extracted from the generated MR image by PSPNet[3]. A quantitative analysis using specific binding ratio was performed using this region. For the experiments, 60 clinical cases of SPECT/CT and MR images were used. The specific binding ratios calculated by this method and the Tossici-Bolt method were compared. As a result, better results than with the Tossici-Bolt method were calculated in 12 cases. Therefore, generating MR images from low-dose CT images and segmentation by deep learning may contribute to quantitative analysis with high reproducibility of DAT imaging.

Paper Details

Date Published: 27 March 2019
PDF: 6 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105012 (27 March 2019); doi: 10.1117/12.2521690
Show Author Affiliations
Shogo Yokoi, Gifu Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Tetsuro Katafuchi, Gifu Univ. of Medical Science (Japan)
Masaki Matsusako, St. Luke's International Hospital (Japan)
Xiangrong Zhou, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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