Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

Joël Greffier; Djamel Dabli; Julien Frandon; Aymeric Hamard; Asmaa Belaouni; Philippe Akessoul; Yannick Fuamba; Julien Le Roy; Boris Guiu; Jean-Paul Beregi

doi:10.1002/mp.15180

Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

Med Phys. 2021 Oct;48(10):5743-5755. doi: 10.1002/mp.15180. Epub 2021 Sep 1.

Authors

Affiliations

¹ Department of medical imaging, CHU Nîmes, Nîmes Medical Imaging Group, Univ Montpellier, Nîmes, France.
² Computed Tomography Division, Canon Medical Systems France, Suresnes, France.
³ Medical Physics Department, Montpellier University Hospital, Montpellier, France.
⁴ Saint-Eloi University Hospital, Montpellier, France.

PMID: 34418110
DOI: 10.1002/mp.15180

Abstract

Purpose: To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm.

Material and methods: Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDI_vol : 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered-back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone (high-contrast insert) and acrylic (low-contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (f_av ) of the NPS curve were measured. The detectability index (d') was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries.

Results: The noise magnitude was lower with both AiCE versions than with AIDR 3De. The noise magnitude was lower with AiCE V10 than with AiCE V8 (-4 ± 6% for Mild, -13 ± 3% for Standard, and -48 ± 0% for Strong levels). f_av and TTF_50% values for both inserts shifted towards higher frequencies with AiCE than with AIDR 3De. Compared to AiCE V08, f_av shifted towards higher frequencies with AiCE V10 (45 ± 4%, 36 ± 3%, and 5 ± 4% for all levels, respectively). The TTF_50% values shifted towards higher frequencies with AiCE V10 as compared with AiCE V8 for both inserts, except for the Strong level for the acrylic insert. Whatever the dose and AiCE levels, d' values were higher with AiCE V10 than with AiCE V8 for the small object/calcification and for the large object/lesion.

Conclusion: As compared to AIDR 3De, lower noise magnitude and higher spatial resolution and detectability index were found with both versions of AiCE. As compared to AiCE V8, AiCE V10 reduced noise and improved spatial resolution and detectability without changing the noise texture in a simple geometric phantom, except for the Strong level. AiCE V10 seems to have a greater potential for dose reduction than AiCE V8.

Keywords: deep learning image reconstruction algorithm; multidetector computed tomography; task-based image quality assessment.

MeSH terms

Algorithms
Deep Learning*
Drug Tapering
Image Processing, Computer-Assisted
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted*
Tomography, X-Ray Computed