Quantitative analysis of dopamine transporter imaging using generating MR image from low dose CT image and segmentation by deep learning

Shogo Yokoi; Takeshi Hara; Tetsuro Katafuchi; Masaki Matsusako; Xiangrong Zhou; Hiroshi Fujita

doi:10.1117/12.2521690

27 March 2019 Quantitative analysis of dopamine transporter imaging using generating MR image from low dose CT image and segmentation by deep learning

Shogo Yokoi, Takeshi Hara, Tetsuro Katafuchi, Masaki Matsusako, Xiangrong Zhou, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 1105012 (2019) https://doi.org/10.1117/12.2521690
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

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Shogo Yokoi, Takeshi Hara, Tetsuro Katafuchi, Masaki Matsusako, Xiangrong Zhou, and Hiroshi Fujita "Quantitative analysis of dopamine transporter imaging using generating MR image from low dose CT image and segmentation by deep learning", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105012 (27 March 2019); https://doi.org/10.1117/12.2521690

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KEYWORDS

Magnetic resonance imaging

Computed tomography

Image segmentation

Quantitative analysis

Single photon emission computed tomography

3D image reconstruction

Image analysis

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