Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters

Objectives: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT). Methods: CT images of a 16 cm dosimetry phantom, a head phantom, and the brains of 11 patients were reconstructed using filtered bac...

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Detalhes bibliográficos

Principais autores:	Yusuke Inoue, Hiroyasu Itoh, Hirofumi Hata, Hiroki Miyatake, Kohei Mitsui, Shunichi Uehara, Chisaki Masuda
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2024-12-01
coleção:	Tomography
Assuntos:	computed tomography brain deep learning reconstruction hybrid iterative reconstruction image noise
Acesso em linha:	https://www.mdpi.com/2379-139X/10/12/147
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