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iterative reconstruction hir with the fc13 kernel ![]() Iterative Reconstruction Hir With The Fc13 Kernel, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/adir+algorithm+with+fc13+kernel/adir+algorithm+with+fc13+kernel/pmc08292685-72-9-25 Average 90 stars, based on 1 article reviews
iterative reconstruction hir with the fc13 kernel - by Bioz Stars,
2026-11
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Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: CT scanning and reconstruction settings for the four algorithms.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: CT number in each reconstruction algorithm at different radiation doses.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: Image noise in each reconstruction algorithm at different radiation doses.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: The noise power spectrum (NPS) curves of each reconstruction algorithm obtained at 100%, 50%, and 25% radiation doses. (a) Filtered back projection (FBP). (b) Hybrid iterative reconstruction (HIR). (c) Model‐based iterative reconstruction (MBIR). (d) Deep learning‑based reconstruction (DLR). As the radiation dose decreases, the NPS curves move upward in each reconstruction algorithm. The NPS curves in DLR were more similar to those in HIR than in the other reconstruction algorithms.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: The normalized noise power spectrum (NPS) curves of each reconstruction algorithm obtained at 100%, 50%, and 25% radiation doses. (a) Filtered back projection (FBP). (b) Hybrid iterative reconstruction (HIR). (c) Model‐based iterative reconstruction (MBIR). (d) Deep learning‑based reconstruction (DLR). The shapes of the normalized NPS for DLR hardly varied at the different doses, while those of MBIR shifted slightly to a higher frequency as the radiation dose decreased.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: The detectable minimum diameters of the low‐contrast object at different radiation doses.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: Attenuation profile curves (APCs) for all reconstruction algorithms obtained at 100%, 50%, and 25% radiation doses. The APCs of the (a–c) high‐signal object, (d–f) moderate‐signal object, and (g–i) low‐signal object obtained at 100%, 50%, and 25% radiation doses, respectively. The APCs were affected by the applied reconstruction algorithm. With the moderate‐signal object, the APCs had some amplitude and could be identified from the boundary of the background, except for the filtered back projection (FBP) algorithm. FBP, filtered back projection; HIR, hybrid iterative reconstruction; MBIR, model‐based iterative reconstruction; DLR, deep learning‐based reconstruction.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques:
Journal: Journal of Applied Clinical Medical Physics
Article Title: Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
doi: 10.1002/acm2.13318
Figure Lengend Snippet: Spatial frequency at 50% and 10% of MTF at different radiation doses.
Article Snippet: In addition, two types of iterative reconstruction were conducted:
Techniques: