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Erschienen in: Abdominal Radiology 3/2016

04.01.2016

Liver detection algorithm: its efficacy for CT noise reduction in the liver

verfasst von: Ashwini Devapalasundaram, Kenneth K. Lau, Eldho Paul, Larry Mak, Nicholas Ardley, Kevin Buchan, Ahilan Kuganesan

Erschienen in: Abdominal Radiology | Ausgabe 3/2016

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Abstract

Objectives

This study was performed to evaluate the efficacy of a novel computed tomography (CT) liver detection algorithm (LDA), which allows for targeted increase of radiation dose to the upper abdomen, on image quality of the liver.

Methods

We retrospectively evaluated the LDA by comparing 40 consecutive patients who had portal venous CT abdomen performed without use of the algorithm, to 40 patients in whom the algorithm was used. Image quality was assessed objectively by comparing the standard deviation (SD) of attenuation values in Hounsfield units (HU) of the abdominal organs. Qualitative analysis was performed by two blinded radiologists who independently graded the image quality of abdominal organs

Results

There was significant noise reduction in the liver (P < 0.001) and spleen (P < 0.001) in the LDA group compared to the conventional group. There was also a significant improvement in image quality of the liver (P < 0.001), kidney (P < 0.001), spleen (P < 0.001), pancreas (P < 0.001), and psoas (P = 0.005) in the LDA group compared to the conventional group. Overall dose between the two groups was similar.

Conclusions

This liver detection algorithm improves the subjective image quality of upper abdominal organs, in particular the liver, without increasing overall radiation dose.
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Metadaten
Titel
Liver detection algorithm: its efficacy for CT noise reduction in the liver
verfasst von
Ashwini Devapalasundaram
Kenneth K. Lau
Eldho Paul
Larry Mak
Nicholas Ardley
Kevin Buchan
Ahilan Kuganesan
Publikationsdatum
04.01.2016
Verlag
Springer US
Erschienen in
Abdominal Radiology / Ausgabe 3/2016
Print ISSN: 2366-004X
Elektronische ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-015-0617-3

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