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. 2022 Jan;12(1):229-243.
doi: 10.21037/qims-21-215.

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Joël Greffier  1 , Djamel Dabli  1 , Aymeric Hamard  1 , Asmaa Belaouni  1 , Philippe Akessoul  1 , Julien Frandon  1 , Jean-Paul Beregi  1
Affiliations

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Joël Greffier et al. Quant Imaging Med Surg. 2022 Jan.

Abstract

Background: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.

Methods: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).

Results: NPS peaks were lower with AiCE than with AIDR 3D (-41%±6% for all levels) or FIRST (-15%±6% for Strong level and -41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (-9%±2% using Mild and -3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (-6%±6% and -13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (-19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; -21%±14%). Using the Standard level, dose could be reduced by -79% for the small calcification and -57% for the large mass using AICE compared to AIDR 3D.

Conclusions: The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

Keywords: Task-based image quality assessment; computed tomography scan (CT scan); deep learning image reconstruction algorithm; iterative reconstruction algorithm.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-215). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Phantom used in this study and regions of interest (ROI) placed inside the phantom to compute the noise power spectrum (NPS) and the task-based transfer function (TTF) in the imQuest software. (A) ACR CT 464 phantom; (B) ROIs used for the NPS; (C) ROIs used to compute the TTF with the bone and acrylic inserts.
Figure 2
Figure 2
Values of NPS obtained for all dose levels with the levels Mild, Standard and Strong of AIDR 3D, FIRST and AiCE algorithms. (A) NPS peaks values; (B) average NPS spatial frequency (fav) values. AIDR 3D, Adaptive Iterative Dose Reduction 3D; AiCE, Advanced intelligent Clear-IQ Engine; CTDIvol, volume computed tomography dose index; fav, average noise power spectrum spatial frequency; FIRST, Forward projected model-based Iterative Reconstruction SoluTion; NPS, Noise power spectrum.
Figure 3
Figure 3
TTF50% obtained for both inserts for all dose levels with the levels Mild, Standard and Strong of AIDR 3D, FIRST and AiCE algorithms. (A) TTF50% values for the acrylic insert; (B) TTF50% values bone insert. AIDR 3D, Adaptive Iterative Dose Reduction 3D; AiCE, Advanced intelligent Clear-IQ Engine; CTDIvol, volume computed tomography dose index; FIRST, Forward projected model-based Iterative Reconstruction SoluTion; TTF50%, values of Task-based transfer function at fifty percent.
Figure 4
Figure 4
Detectability index (d') as function of the dose with the levels Mild, Standard and Strong of AIDR 3D, FIRST and AiCE algorithms for detection of both features. (A) Large feature (25 mm in diameter, 120 HU contrast); (B) small feature (1.5 mm in diameter, 500 HU contrast). AIDR 3D, Adaptive Iterative Dose Reduction 3D; AiCE, Advanced intelligent Clear-IQ Engine; CTDIvol, volume CT dose index; FIRST, Forward projected model-based Iterative Reconstruction SoluTion.
Figure 5
Figure 5
A 5×5 cm2 region of interest centered on the acrylic insert with the levels Mild, Standard and Strong of AIDR 3D, FIRST and AiCE algorithms as function of the dose level. All images were displayed with a soft tissue window (window width, 350; window level, 50 HU). AIDR 3D, Adaptive Iterative Dose Reduction 3D; AiCE, Advanced intelligent Clear-IQ Engine; FIRST, Forward projected model-based Iterative Reconstruction SoluTion.
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