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Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study

OBJECTIVE: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. MATERIALS AND METHODS: This prospective study included 42 consecutive patients who had undergone low-dose...

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Autores principales: Park, Chae Jung, Kim, Ki Wook, Lee, Ho-Joon, Kim, Myeong-Jin, Kim, Jinna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6082760/
https://www.ncbi.nlm.nih.gov/pubmed/30174486
http://dx.doi.org/10.3348/kjr.2018.19.5.957
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author Park, Chae Jung
Kim, Ki Wook
Lee, Ho-Joon
Kim, Myeong-Jin
Kim, Jinna
author_facet Park, Chae Jung
Kim, Ki Wook
Lee, Ho-Joon
Kim, Myeong-Jin
Kim, Jinna
author_sort Park, Chae Jung
collection PubMed
description OBJECTIVE: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. MATERIALS AND METHODS: This prospective study included 42 consecutive patients who had undergone low-dose contrast-enhanced CT for the evaluation of suspected parotid gland tumors. Prior or subsequent non-low-dose CT scans within 12 months were available in 10 of the participants. Background noise (BN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between non-low-dose CT images and images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose(4); Philips Healthcare), and knowledge-based IMR. Subjective image quality was rated by two radiologists using five-point grading scales to assess the overall image quality, delineation of lesion contour, image sharpness, and noise. RESULTS: With the IMR algorithm, background noise (IMR, 4.24 ± 3.77; iDose(4), 8.77 ± 3.85; FBP, 11.73 ± 4.06; p = 0.037 [IMR vs. iDose(4)] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, 23.93 ± 7.49; iDose(4), 10.20 ± 3.29; FBP, 7.33 ± 2.03; p = 0.011 [IMR vs. iDose(4)] and p < 0.001 [IMR vs. FBP]) was significantly higher compared with the other two algorithms. The CNR was also significantly higher with the IMR compared with the FBP (25.76 ± 11.88 vs. 9.02 ± 3.18, p < 0.001). There was no significant difference in BN, SNR, and CNR between low-dose CT with the IMR algorithm and non-low-dose CT. Subjective image analysis revealed that IMR-generated low-dose CT images showed significantly better overall image quality and delineation of lesion contour with lesser noise, compared with those generated using FBP by both reviewers 1 and 2 (4 vs. 3; 4 vs. 3; and 3–4 vs. 2; p < 0.05 for all pairs), although there was no significant difference in subjective image quality scores between IMR-generated low-dose CT and non-low-dose CT images. CONCLUSION: Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors.
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spelling pubmed-60827602018-09-01 Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study Park, Chae Jung Kim, Ki Wook Lee, Ho-Joon Kim, Myeong-Jin Kim, Jinna Korean J Radiol Neuroimaging and Head & Neck OBJECTIVE: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. MATERIALS AND METHODS: This prospective study included 42 consecutive patients who had undergone low-dose contrast-enhanced CT for the evaluation of suspected parotid gland tumors. Prior or subsequent non-low-dose CT scans within 12 months were available in 10 of the participants. Background noise (BN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between non-low-dose CT images and images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose(4); Philips Healthcare), and knowledge-based IMR. Subjective image quality was rated by two radiologists using five-point grading scales to assess the overall image quality, delineation of lesion contour, image sharpness, and noise. RESULTS: With the IMR algorithm, background noise (IMR, 4.24 ± 3.77; iDose(4), 8.77 ± 3.85; FBP, 11.73 ± 4.06; p = 0.037 [IMR vs. iDose(4)] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, 23.93 ± 7.49; iDose(4), 10.20 ± 3.29; FBP, 7.33 ± 2.03; p = 0.011 [IMR vs. iDose(4)] and p < 0.001 [IMR vs. FBP]) was significantly higher compared with the other two algorithms. The CNR was also significantly higher with the IMR compared with the FBP (25.76 ± 11.88 vs. 9.02 ± 3.18, p < 0.001). There was no significant difference in BN, SNR, and CNR between low-dose CT with the IMR algorithm and non-low-dose CT. Subjective image analysis revealed that IMR-generated low-dose CT images showed significantly better overall image quality and delineation of lesion contour with lesser noise, compared with those generated using FBP by both reviewers 1 and 2 (4 vs. 3; 4 vs. 3; and 3–4 vs. 2; p < 0.05 for all pairs), although there was no significant difference in subjective image quality scores between IMR-generated low-dose CT and non-low-dose CT images. CONCLUSION: Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors. The Korean Society of Radiology 2018 2018-08-06 /pmc/articles/PMC6082760/ /pubmed/30174486 http://dx.doi.org/10.3348/kjr.2018.19.5.957 Text en Copyright © 2018 The Korean Society of Radiology http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Neuroimaging and Head & Neck
Park, Chae Jung
Kim, Ki Wook
Lee, Ho-Joon
Kim, Myeong-Jin
Kim, Jinna
Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title_full Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title_fullStr Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title_full_unstemmed Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title_short Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
title_sort contrast-enhanced ct with knowledge-based iterative model reconstruction for the evaluation of parotid gland tumors: a feasibility study
topic Neuroimaging and Head & Neck
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6082760/
https://www.ncbi.nlm.nih.gov/pubmed/30174486
http://dx.doi.org/10.3348/kjr.2018.19.5.957
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