29 March 2016 Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging
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Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.
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Yuzuho Yamaguchi, Yuzuho Yamaguchi, Yuta Takeda, Yuta Takeda, Takeshi Hara, Takeshi Hara, Xiangrong Zhou, Xiangrong Zhou, Masaki Matsusako, Masaki Matsusako, Yuki Tanaka, Yuki Tanaka, Kazuhiko Hosoya, Kazuhiko Hosoya, Tsutomu Nihei, Tsutomu Nihei, Tetsuro Katafuchi, Tetsuro Katafuchi, Hiroshi Fujita, Hiroshi Fujita, } "Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881V (29 March 2016); doi: 10.1117/12.2217384; https://doi.org/10.1117/12.2217384

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