Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.

Journal: Scientific reports
Published Date:

Abstract

Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders - 98%, virtual patients - 97%) and quantify materials (mean absolute percentage difference: cylinders - 8-10%, virtual patients - 10-15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.

Authors

  • Jayasai R Rajagopal
    Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA. jayasai.rajagopal@duke.edu.
  • Saikiran Rapaka
    From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (C.T., C.N.D.C., S.B., M.R., T.W.M., T.M.D., R.R.B., U.J.S.), and Division of Cardiology, Department of Medicine (R.R.B., D.H.S., U.J.S.), Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260; Department of Computed Tomography-Research & Development, Siemens Healthcare GmbH, Forchheim, Germany (K.L.G., C.C., C.S., M.S.); Department of Corporate Technology, Siemens SRL, Brasov, Romania (L.M.I.); and Department of Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ (S.R., P.S.).
  • Faraz Farhadi
    Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
  • Ehsan Abadi
  • W Paul Segars
    Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC, 27708, USA.
  • Tristan Nowak
    Siemens Healthineers AG, Siemensstr. 3, 91301, Forchheim, Germany.
  • Puneet Sharma
    Digital Technologies and Innovation, Siemens Healthineers, Princeton, NJ, United States.
  • William F Pritchard
    Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA.
  • Ashkan Malayeri
    Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
  • Elizabeth C Jones
    Clinical Center, National Institutes of Health, Bethesda, MD, United States.
  • Ehsan Samei
    Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.
  • Pooyan Sahbaee
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).