Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

Joël Greffier; Maxime Pastor; Salim Si-Mohamed; Cynthia Goutain-Majorel; Aude Peudon-Balas; Mourad Zoubir Bensalah; Julien Frandon; Jean-Paul Beregi; Djamel Dabli

doi:10.1016/j.diii.2023.10.004

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

Diagn Interv Imaging. 2024 Mar;105(3):110-117. doi: 10.1016/j.diii.2023.10.004. Epub 2023 Nov 8.

Authors

Joël Greffier¹, Maxime Pastor², Salim Si-Mohamed³, Cynthia Goutain-Majorel⁴, Aude Peudon-Balas⁴, Mourad Zoubir Bensalah⁴, Julien Frandon², Jean-Paul Beregi², Djamel Dabli²

Affiliations

¹ IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France. Electronic address: joel.greffier@chu-nimes.fr.
² IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France.
³ University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 69100 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 69500 Bron, France.
⁴ Department of Medical Imaging, Centre Hospitalier de Perpignan, 66000 Perpignan, France.

PMID: 37949769
DOI: 10.1016/j.diii.2023.10.004

Abstract

Purpose: The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.

Materials and methods: Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter).

Results: Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f₅₀) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f₅₀ values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels.

Conclusion: Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

Keywords: Cardiac CT; Deep-learning image reconstruction algorithm; Multidetector computed tomography; Task-based image quality assessment.

MeSH terms

Algorithms
Deep Learning*
Humans
Image Processing, Computer-Assisted
Phantoms, Imaging
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted / methods
Tomography, X-Ray Computed* / methods