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

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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Bibliographic Details
Main Authors: Yusuke Inoue, Hiroyasu Itoh, Hirofumi Hata, Hiroki Miyatake, Kohei Mitsui, Shunichi Uehara, Chisaki Masuda
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Tomography
Subjects:
computed tomography
brain
deep learning reconstruction
hybrid iterative reconstruction
image noise
Online Access:https://www.mdpi.com/2379-139X/10/12/147
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