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Usefulness of Model-Based Iterative Reconstruction in Brain CT as Compared With Hybrid Iterative Reconstruction.
Sekino, Hirofumi; Ishii, Shiro; Kuroiwa, Daichi; Fujimaki, Hideki; Sugawara, Shigeyasu; Suenaga, Hiroki; Hakozaki, Motoharu; Yamakuni, Ryo; Watanabe, Hirotake; Hasegawa, Osamu; Ito, Hiroshi.
Afiliação
  • Sekino H; From the Department of Radiology and Nuclear Medicine, Fukushima Medical University, Fukushima, Japan.
J Comput Assist Tomogr ; 45(4): 600-605, 2021.
Article em En | MEDLINE | ID: mdl-34176874
ABSTRACT

OBJECTIVE:

The aim of this study was to compare the contrast of gray to white matter between forward-projected model-based iterative reconstruction solution (FIRST) and hybrid iterative reconstruction (IR) by measuring computed tomography value of brain parenchyma.

METHODS:

Computed tomography values of the gray and white matter in 15 areas of 21 patients (7 males, 14 females; average age, 49.5 ± 10.7 years) were measured and compared between FIRST and hybrid IR with filtered back projection (FBP) using 2 different reconstruction kernels FC21 and FC26.

RESULTS:

The ratio of gray to white matter obtained using FIRST (1.25 ± 0.08) was significantly higher than that obtained using FBP with both kernel FC21 (1.13 ± 0.03) and kernel FC26 (1.22 ± 0.06).

CONCLUSIONS:

FIRST increases the contrast between the gray and white matter, and decreases noise in brain computed tomography compared with FBP with hybrid IR.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encefalopatias / Interpretação de Imagem Radiográfica Assistida por Computador / Tomografia Computadorizada por Raios X Tipo de estudo: Prognostic_studies Limite: Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encefalopatias / Interpretação de Imagem Radiográfica Assistida por Computador / Tomografia Computadorizada por Raios X Tipo de estudo: Prognostic_studies Limite: Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article