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Original Investigation|Articles in Press

Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study

Published:January 21, 2023DOI:https://doi.org/10.1016/j.acra.2022.12.040
Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study
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      Rationale and Objectives

      Deep-learning-based super-resolution image reconstruction (DLSRR) is a novel image reconstruction technique that is expected to contribute to improvement in spatial resolution as well as noise reduction through learning from high-resolution computed tomography (CT). This study aims to evaluate image quality obtained with DLSRR and assess its clinical potential.

      Materials and Methods

      CT images of a Mercury CT 4.0 phantom were obtained using a 320-row multi-detector scanner at tube currents of 100, 200, and 300 mA. Image data were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), deep-learning-based image reconstruction (DLR), and DLSRR at image reconstruction strength levels of mild, standard, and strong. Noise power spectrum (NPS), task transfer function (TTF), and detectability index were calculated.

      Results

      The magnitude of the noise-reducing effect in comparison with FBP was in the order MBIR <HIR <DLR <DLSRR. The resolution property was in the order HIR <FBP <DLR <MBIR <DLSRR. The detectability index was highest for DLSRR. The maximum and mean of the NPS shifted towards lower frequencies for HIR and MBIR compared with FBP, and similar shifts were observed for DLR and DLSRR. For each image reconstruction technique, NPS decreased with increasing reconstruction strength level, but no change was observed in TTF.

      Conclusion

      The present results suggest that DLSRR can achieve greater noise reduction and improved spatial resolution in the high-contrast region compared with conventional DLR and iterative reconstruction techniques.

      Key Words

      Abbreviations:

      CT (Computed Tomography), IR (Iterative Reconstruction), DLR (Deep Learning-based image Reconstruction), HIR (Hybrid Iterative Reconstruction), MBIR (Model-Based Iterative Reconstruction), DLSRR (Deep-Learning-based Super-Resolution image Reconstruction), CCTA (Coronary Computed Tomographic Angiography), TTF (Task-based Transfer Function), NPS (Noise Power Spectrum)
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      Article info

      Publication history

      Published online: January 21, 2023
      Accepted: December 24, 2022
      Received in revised form: December 17, 2022
      Received: October 14, 2022

      Publication stage

      In Press Corrected Proof

      Identification

      DOI: https://doi.org/10.1016/j.acra.2022.12.040

      Copyright

      © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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