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In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

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.

Franck Caro, Zhang Guozhi, Deak Paul, Zanca Federica


Chest, Computed tomography, Contrast-detail evaluation, Deep learning image reconstruction, Dosimetry, Image quality, Iterative reconstruction