Impact of a new deep-learning image reconstruction algorithm on potential dose reduction and quality of chest CT images: a phantom study.

Journal: 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)
Published Date: Jan 16, 2026

Abstract

PURPOSE: To evaluate the impact of a new deep-learning image reconstruction (DLR) algorithm on image quality and potential dose reduction compared with a hybrid iterative reconstruction (IR) algorithm under chest CT conditions. MATERIALS AND METHODS: Acquisitions were performed on an image quality phantom at 5 CTDIvol (0.4/2.5/5.0/7.5/9.5 mGy). Raw data were reconstructed using soft tissue and lung kernels at Level 5 among the 9 levels available on the IR algorithm (IR-5) and the 5 levels on the DLR algorithm (from D1 to D5). The noise power spectrum (NPS) and task-based transfer function (TTF) were computed. The detectability index (d') was computed to model subsolid pulmonary nodule and high-contrast pulmonary nodule for lung images and low-contrast soft tissue mediastinal nodule for soft tissue images. RESULTS: For both kernels, noise magnitude and average NPS spatial frequencies decreased from D1 to D5 and were lower than those obtained with IR-5. For all inserts studied and both kernels, TTF values at 50% decreased from D1 to D5 and were lower than those obtained with IR-5. For all three simulated lesions and both kernels, d' values increased from D1 to D5 and were higher than those obtained with K5. Compared to IR-5, a dose reduction potential was found for each DLR level. CONCLUSION: Compared to IR-5, noise magnitude and detectability values were better, but noise texture and spatial resolution values were degraded, and this degradation increased as the DLR level increased. The results obtained with this new DLR algorithm open up new possibilities for improving image quality and offer significant potential for radiation dose reduction.

Authors

  • Joël Greffier
    Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, Univ Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France. [email protected].
  • Fabien de Oliveira
    IMAGINE, UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, Nîmes, France.
  • Skander Sammoud
    IMAGINE, University of Montpellier, Department of Medical Imaging, CHU Nîmes, Nîmes, France.
  • Jean Paul Beregi
    Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, Univ Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France.
  • Djamel Dabli
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals