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DSG-GAN:A dual-stage-generator-based GAN for cross-modality synthesis from PET to CT.
Wang, Huabin; Wang, Xiangdong; Liu, Fei; Zhang, Grace; Zhang, Gong; Zhang, Qiang; Lang, Michael L.
Afiliación
  • Wang H; Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China. Electronic address: wanghuabin@ahu.edu.cn.
  • Wang X; Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
  • Liu F; Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
  • Zhang G; Faculty of Engineering, Western University, Canada.
  • Zhang G; Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China; School of Public Health, Anhui University of Science and Technology, HuaiNan, Anhui 232001, China.
  • Zhang Q; Physical Examination Center of The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010010, China.
  • Lang ML; Department of Physics, University of Winnipeg, 515 Portage Ave., Winnipeg, Manitoba, Canada; Sino Canadian Health Research Institute, Winnipeg, Manitoba, Canada.
Comput Biol Med ; 172: 108296, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38493600
ABSTRACT
PET/CT devices typically use CT images for PET attenuation correction, leading to additional radiation exposure. Alternatively, in a standalone PET imaging system, attenuation and scatter correction cannot be performed due to the absence of CT images. Therefore, it is necessary to explore methods for generating pseudo-CT images from PET images. However, traditional PET-to-CT synthesis models encounter conflicts in multi-objective optimization, leading to disparities between synthetic and real images in overall structure and texture. To address this issue, we propose a staged image generation model. Firstly, we construct a dual-stage generator, which synthesizes the overall structure and texture details of images by decomposing optimization objectives and employing multiple loss functions constraints. Additionally, in each generator, we employ improved deep perceptual skip connections, which utilize cross-layer information interaction and deep perceptual selection to effectively and selectively leverage multi-level deep information and avoid interference from redundant information. Finally, we construct a context-aware local discriminator, which integrates context information and extracts local features to generate fine local details of images and reasonably maintain the overall coherence of the images. Experimental results demonstrate that our approach outperforms other methods, with SSIM, PSNR, and FID metrics reaching 0.8993, 29.6108, and 29.7489, respectively, achieving the state-of-the-art. Furthermore, we conduct visual experiments on the synthesized pseudo-CT images in terms of image structure and texture. The results indicate that the pseudo-CT images synthesized in this study are more similar to real CT images, providing accurate structure information for clinical disease analysis and lesion localization.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Exposición a la Radiación / Tomografía Computarizada por Tomografía de Emisión de Positrones Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Exposición a la Radiación / Tomografía Computarizada por Tomografía de Emisión de Positrones Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos