Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study
Yoshinori Funama
1
,
Yasunori Nagayama
2
,
Daisuke Sakabe
3
,
Yuya Ito
4
,
Yutaka Chiba
4
,
Takeshi Nakaura
2
,
Seitaro Oda
2
,
Masafumi Kidoh
2
,
Toshinori Hirai
2
2
4
Canon Medical Systems Corporation, Otawara, Japan.
|
Publication type: Journal Article
Publication date: 2025-03-01
scimago Q1
wos Q1
SJR: 0.992
CiteScore: 6.2
Impact factor: 3.9
ISSN: 10766332, 18784046
PubMed ID:
39304377
Abstract
This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths.
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Total citations:
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Citations from 2024:
3
(100%)
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GOST
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Funama Y. et al. Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study // Academic Radiology. 2025. Vol. 32. No. 3. pp. 1517-1524.
GOST all authors (up to 50)
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Funama Y., Nagayama Y., Sakabe D., Ito Y., Chiba Y., Nakaura T., Oda S., Kidoh M., Hirai T. Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study // Academic Radiology. 2025. Vol. 32. No. 3. pp. 1517-1524.
Cite this
RIS
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TY - JOUR
DO - 10.1016/j.acra.2024.09.012
UR - https://linkinghub.elsevier.com/retrieve/pii/S1076633224006615
TI - Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study
T2 - Academic Radiology
AU - Funama, Yoshinori
AU - Nagayama, Yasunori
AU - Sakabe, Daisuke
AU - Ito, Yuya
AU - Chiba, Yutaka
AU - Nakaura, Takeshi
AU - Oda, Seitaro
AU - Kidoh, Masafumi
AU - Hirai, Toshinori
PY - 2025
DA - 2025/03/01
PB - Elsevier
SP - 1517-1524
IS - 3
VL - 32
PMID - 39304377
SN - 1076-6332
SN - 1878-4046
ER -
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BibTex (up to 50 authors)
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@article{2025_Funama,
author = {Yoshinori Funama and Yasunori Nagayama and Daisuke Sakabe and Yuya Ito and Yutaka Chiba and Takeshi Nakaura and Seitaro Oda and Masafumi Kidoh and Toshinori Hirai},
title = {Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study},
journal = {Academic Radiology},
year = {2025},
volume = {32},
publisher = {Elsevier},
month = {mar},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1076633224006615},
number = {3},
pages = {1517--1524},
doi = {10.1016/j.acra.2024.09.012}
}
Cite this
MLA
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Funama, Yoshinori, et al. “Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study.” Academic Radiology, vol. 32, no. 3, Mar. 2025, pp. 1517-1524. https://linkinghub.elsevier.com/retrieve/pii/S1076633224006615.