Your browser doesn't support javascript.
loading
A review on AI in PET imaging.
Matsubara, Keisuke; Ibaraki, Masanobu; Nemoto, Mitsutaka; Watabe, Hiroshi; Kimura, Yuichi.
Afiliación
  • Matsubara K; Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan.
  • Ibaraki M; Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan.
  • Nemoto M; Faculty of Biology-Oriented Science and Technology, and Cyber Informatics Research Institute, Kindai University, Wakayama, Japan.
  • Watabe H; Cyclotron and Radioisotope Center (CYRIC), Tohoku University, Miyagi, Japan.
  • Kimura Y; Faculty of Biology-Oriented Science and Technology, and Cyber Informatics Research Institute, Kindai University, Wakayama, Japan. ukimura@ieee.org.
Ann Nucl Med ; 36(2): 133-143, 2022 Feb.
Article en En | MEDLINE | ID: mdl-35029818
Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Tomografía de Emisión de Positrones Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Ann Nucl Med Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Japón
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Tomografía de Emisión de Positrones Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Ann Nucl Med Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Japón