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Title
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Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT : a phantom study
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Author
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Abstract
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| Purpose: To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT. Methods: Phantom images were acquired using a clinical chest protocol (7.6 mGy) and two levels of dose reduction (60% and 80%). Images were reconstructed with FBP, ASIR-V (50% and 100% blending) and True-Fidelity (low (DL-L), medium (DL-M) and high (DL-H) strength). Noise (SD), noise power spectrum (NPS) and task-based transfer function (TTF) were calculated. Noise texture was quantitatively compared by computing root-mean-square deviations (RMSD) of NPS with respect to FBP. Four experienced readers performed a contrast-detail evaluation. The dose reducing potential of TrueFidelity compared to ASIR-V was assessed by fitting SD and contrast-detail as a function of dose. Results: DL-M and DL-H reduced noise and NPS area compared to FBP and 50% ASIR-V, at all dose levels. At 7.6 mGy, NPS of ASIR-V 50/100% was shifted towards lower frequencies (f(peak) = 0.22/0.13 mm(-1), RMSD = 0.14/0.38), with respect to FBP (f(peak) = 0.30 mm(-1)). Marginal difference was observed for TrueFidelity: f(peak) = 0.33/0.30/0.30 mm(-1) and RMSD = 0.03/0.04/0.07 for L/M/H strength. Values of ITF50% were independent of DL strength and higher compared to FBP and ASIR-V, at all dose and contrast levels. Contrast-detail was highest for DL-H at all doses. Compared to 50% ASIR-V, DL-H had an estimated dose reducing potential of 50% on average, without impairing noise, texture and detectability. Conclusions: TrueFidelity preserves the image texture of FBP, while outperforming ASIR-V in terms of noise, spatial resolution and detectability at lower doses. |
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Language
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English
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Source (journal)
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Physica medica / Argus specialist publications. - London
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Publication
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London
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2021
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ISSN
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1120-1797
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DOI
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10.1016/J.EJMP.2020.12.005
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Volume/pages
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81
(2021)
, p. 86-93
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ISI
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000621922300011
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Pubmed ID
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33445125
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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