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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