Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 35852557

Abstract

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators.

Authors

  • Hasan Sari
    Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America.
  • Mohammadreza Teimoorisichani
    Siemens Medical Solutions, USA Inc., Knoxville, TN, USA.
  • Clemens Mingels
    Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Ian Alberts
    Department of Nuclear Medicine, University of Bern, Bern, Switzerland.
  • Vladimir Panin
    Siemens Medical Solutions, USA Inc., Knoxville, TN, USA.
  • Deepak Bharkhada
    Siemens Medical Solutions, USA Inc., Knoxville, TN, USA.
  • Song Xue
  • George Prenosil
    Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.
  • Maurizio Conti
    Siemens Medical Solutions, USA Inc., Knoxville, TN, USA.
  • Axel Rominger

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