"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 35312837

Abstract

PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve diagnostic accuracy, approximately 3/4ths of clinical MPI worldwide remains non-attenuation-corrected (NAC). In this work, we propose a novel deep learning (DL) algorithm to provide "virtual" DL attenuation-corrected (DLAC) perfusion polar maps solely from NAC data without concurrent computed tomography (CT) imaging or additional scans.

Authors

  • Tomoe Hagio
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA. thagio@inviasolutions.com.
  • Alexis Poitrasson-Rivière
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Jonathan B Moody
  • Jennifer M Renaud
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Liliana Arida-Moody
    Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
  • Ravi V Shah
    Cardiovascular Research Center, Massachusetts General Hospital, Boston.
  • Edward P Ficaro
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Venkatesh L Murthy
    Department of Cardiology, University of Michigan, Ann Arbor.

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