<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Introduction</h3>
<p>Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated the quality of DLIR images and compared it to the quality of images from the latest adaptive statistical iterative reconstruction (ASIR-V) algorithm in terms of noise-power spectrum (NPS) and modulation-transfer function (MTF).</p>
</sec>
<sec>
<h3>Methods</h3>
<p>We scanned a Revolution QA phantom (GE Healthcare, USA) and a 20 cm water phantom (GE Healthcare, USA) with our 512 multi-slice computed tomography (CT) scanner. Images of the tungsten wire within the Revolution QA phantom were reconstructed with a 50 mm field of view (FOV). The images were reconstructed with various ASIR-V strengths (i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%) and DLIRs (i.e. low, medium, and high) to assess the MTF. The images from the 20 cm water phantom were reconstructed with the same configuration to assess the NPS.</p>
</sec>
<sec>
<h3>Results</h3>
<p>The MTF was similar for both reconstruction algorithms of DLIR and ASiR-V. The peak frequency (fp) of the DLIR low was comparable to that from ASIR-V at 50, 60, 70%; the DLIR medium was comparable to ASIR-V at 80%; and the DLIR high was comparable to ASIR-V at 100%. The average frequency (fA) of the DLIR low was comparable to that from ASIR-V at 40%; the DLIR medium was comparable to ASIR-V at 50%; and the DLIR high was comparable to ASIR-V at 70%. Both the DLIR and ASIR-V were able to reduce noise, but they had a different texture.</p>
</sec>
<sec>
<h3>Conclusions</h3>
<p>The noise in the DLIR images was more homogenous at high and low frequencies, while in the ASIR-V images, the noise was more concentrated at high frequencies. The MTF was similar for both reconstruction algorithms. The DLIR method showed a better noise reduction than the ASIR-V reconstruction.</p>
</sec>
</abstract>

Introduction
Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated the quality of DLIR images and compared it to the quality of images from the latest adaptive statistical iterative reconstruction (ASIR-V) algorithm in terms of noise-power spectrum (NPS) and modulation-transfer function (MTF).


Methods
We scanned a Revolution QA phantom (GE Healthcare, USA) and a 20 cm water phantom (GE Healthcare, USA) with our 512 multi-slice computed tomography (CT) scanner. Images of the tungsten wire within the Revolution QA phantom were reconstructed with a 50 mm field of view (FOV). The images were reconstructed with various ASIR-V strengths (i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%) and DLIRs (i.e. low, medium, and high) to assess the MTF. The images from the 20 cm water phantom were reconstructed with the same configuration to assess the NPS.


Results
The MTF was similar for both reconstruction algorithms of DLIR and ASiR-V. The peak frequency (fp) of the DLIR low was comparable to that from ASIR-V at 50, 60, 70%; the DLIR medium was comparable to ASIR-V at 80%; and the DLIR high was comparable to ASIR-V at 100%. The average frequency (fA) of the DLIR low was comparable to that from ASIR-V at 40%; the DLIR medium was comparable to ASIR-V at 50%; and the DLIR high was comparable to ASIR-V at 70%. Both the DLIR and ASIR-V were able to reduce noise, but they had a different texture.


Conclusions
The noise in the DLIR images was more homogenous at high and low frequencies, while in the ASIR-V images, the noise was more concentrated at high frequencies. The MTF was similar for both reconstruction algorithms. The DLIR method showed a better noise reduction than the ASIR-V reconstruction.

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Introduction
Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated...

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Article Category: Technical Note

Online veröffentlicht: 05. Juni 2023

Seitenbereich: 104 - 112

Eingereicht: 20. März 2023

Akzeptiert: 04. Mai 2023

DOI: https://doi.org/10.2478/pjmpe-2023-0012

Schlüsselwörter
ASIR-V, DLIR, noise power spectrum, modulation transfer function

© 2023 Adiwasono M. B. Setiawan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Article Category: Technical Note

Online veröffentlicht: 05. Juni 2023

Seitenbereich: 104 - 112

Eingereicht: 20. März 2023

Akzeptiert: 04. Mai 2023

DOI: https://doi.org/10.2478/pjmpe-2023-0012

SchlüsselwörterASIR-V, DLIR, noise power spectrum, modulation transfer function

© 2023 Adiwasono M. B. Setiawan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
ASIR-V, DLIR, noise power spectrum, modulation transfer function