Caro Franck1, Guozhi Zhang2, Paul Deak3, Federica Zanca4. 1. Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium; mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium. Electronic address: caro.franck@uza.be. 2. Department of Radiology, KU Leuven University Hospitals Leuven, Leuven, Belgium. 3. GE Healthcare, Glattbrugg, Switzerland. 4. Palindromo Consulting, Leuven, Belgium.
Abstract
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 TrueFidelity (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 (fpeak = 0.22/0.13 mm-1, RMSD = 0.14/0.38), with respect to FBP (fpeak = 0.30 mm-1). Marginal difference was observed for TrueFidelity: fpeak = 0.33/0.30/0.30 mm-1 and RMSD = 0.03/0.04/0.07 for L/M/H strength. Values of TTF50% 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.
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 TrueFidelity (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 (fpeak = 0.22/0.13 mm-1, RMSD = 0.14/0.38), with respect to FBP (fpeak = 0.30 mm-1). Marginal difference was observed for TrueFidelity: fpeak = 0.33/0.30/0.30 mm-1 and RMSD = 0.03/0.04/0.07 for L/M/H strength. Values of TTF50% 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.