Deep learning reconstruction enhances tophus detection in a dual-energy CT phantom study.

Journal: Scientific reports
Published Date: May 28, 2025

Abstract

This study aimed to compare two deep learning reconstruction (DLR) techniques (AiCE mild; AiCE strong) with two established methods-iterative reconstruction (IR) and filtered back projection (FBP)-for the detection of monosodium urate (MSU) in dual-energy computed tomography (DECT). An ex vivo bio-phantom and a raster phantom were prepared by inserting syringes containing different MSU concentrations and scanned in a 320-rows volume DECT scanner at different tube currents. The scans were reconstructed in a soft tissue kernel using the four reconstruction techniques mentioned above, followed by quantitative assessment of MSU volumes and image quality parameters, i.e., signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Both DLR techniques outperformed conventional IR and FBP in terms of volume detection and image quality. Notably, unlike IR and FBP, the two DLR methods showed no positive correlation of the MSU detection rate with the CT dose index (CTDIvol) in the bio-phantom. Our study highlights the potential of DLR for DECT imaging in gout, where it offers enhanced detection sensitivity, improved image contrast, reduced image noise, and lower radiation exposure. Further research is needed to assess the clinical reliability of this approach.

Authors

  • Sydney Alexandra Schmolke
    Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charitéplatz 1, 10117, Berlin, Germany.
  • Torsten Diekhoff
    From the Institute for Radiology (K.K.B., L.C.A., K.G.A.H., T.D., S.M.N., B.H., J.L.V.) and Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine) (F.P., L.S., M.P., V.R.R., H.H., J.R., M.T., D.P.), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany (K.K.B., L.C.A., J.R.); Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Munich, Germany (M.R.M.); Department of Medicine, University of Alberta, Edmonton, Alberta, Canada (R.G.L., W.P.M.); Rheumazentrum Ruhrgebiet Herne, Ruhr University Bochum, Germany (X.B.); and Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany (D.P.).
  • Jürgen Mews
    Canon Medical Systems Europe, Amstelveen, The Netherlands.
  • Karim Khayata
    Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charitéplatz 1, 10117, Berlin, Germany.
  • Maximilian Kotlyarov
    Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charitéplatz 1, 10117, Berlin, Germany.

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