Feasibility of a low-dose orbital CT protocol with a knowledge-based iterative model reconstruction algorithm for evaluating Graves' orbitopathy

Ho Joon Lee, Jinna Kim, Ki Wook Kim, Seung Koo Lee, Jin Sook Yoon

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose: To evaluate the clinical feasibility of low-dose orbital CT with a knowledge-based iterative model reconstruction (IMR) algorithm for evaluating Graves' orbitopathy. Methods: Low-dose orbital CT was performed with a CTDIvol of 4.4 mGy. In 12 patients for whom prior or subsequent non-low-dose orbital CT data obtained within 12 months were available, background noise, SNR, and CNR were compared for images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose4), and IMR and non-low-dose CT images. Comparison of clinically relevant measurements for Graves' orbitopathy, such as rectus muscle thickness and retrobulbar fat area, was performed in a subset of 6 patients who underwent CT for causes other than Graves' orbitopathy, by using the Wilcoxon signed-rank test. Results: The lens dose estimated from skin dosimetry on a phantom was 4.13 mGy, which was on average 59.34% lower than that of the non-low-dose protocols. Image quality in terms of background noise, SNR, and CNR was the best for IMR, followed by non-low-dose CT, iDose4, and FBP, in descending order. A comparison of clinically relevant measurements revealed no significant difference in the retrobulbar fat area and the inferior and medial rectus muscle thicknesses between the low-dose and non-low-dose CT images. Conclusions: Low-dose CT with IMR may be performed without significantly affecting the measurement of prognostic parameters for Graves' orbitopathy while lowering the lens dose and image noise.

Original languageEnglish
Pages (from-to)327-331
Number of pages5
JournalClinical Imaging
Volume51
DOIs
Publication statusPublished - 2018 Sep 1

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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