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Tensor-conductance model for reducing the computational artifact in target tissue for low-frequency dosimetry.
Diao, Yinliang; Liu, Li; Deng, Nuo; Lyu, Shilei; Hirata, Akimasa.
Affiliation
  • Diao Y; College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, People's Republic of China.
  • Liu L; Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
  • Deng N; College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, People's Republic of China.
  • Lyu S; College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, People's Republic of China.
  • Hirata A; College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, People's Republic of China.
Phys Med Biol ; 68(20)2023 Oct 06.
Article in En | MEDLINE | ID: mdl-37722382
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radiometry / Artifacts Type of study: Prognostic_studies Limits: Humans Language: En Journal: Phys Med Biol Year: 2023 Document type: Article Country of publication: Reino Unido
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radiometry / Artifacts Type of study: Prognostic_studies Limits: Humans Language: En Journal: Phys Med Biol Year: 2023 Document type: Article Country of publication: Reino Unido