An artificial neural net and error backpropagation to reconstruct single photon emission computerized tomography data

Med Phys. 1999 Feb;26(2):244-8. doi: 10.1118/1.598511.

Abstract

At present, algorithms used in nuclear medicine to reconstruct single photon emission computerized tomography (SPECT) data are usually based on one of two principles: filtered backprojection and iterative methods. In this paper a different algorithm, applying an artificial neural network (multilayer perception) and error backpropagation as training method are used to reconstruct transaxial slices from SPECT data. The algorithm was implemented on an Elscint XPERT workstation (i486, 50 MHz), used as a routine digital image processing tool in our departments. Reconstruction time for a 64 x 64 matrix is approximately 45 s/transaxial slice. The algorithm has been validated by a mathematical model and tested on heart and Jaszczak phantoms. Phantom studies and very first clinical results ((111)In octreotide SPECT, 99mTc MDP bone SPECT) show in comparison with filtered backprojection an enhancement in image quality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Carcinoid Tumor / diagnostic imaging
  • Computer Simulation
  • Heart / diagnostic imaging
  • Humans
  • Image Processing, Computer-Assisted*
  • Neural Networks, Computer*
  • Octreotide / analogs & derivatives
  • Phantoms, Imaging
  • Radiopharmaceuticals
  • Spine / diagnostic imaging
  • Stomach Neoplasms / diagnostic imaging
  • Technetium Tc 99m Medronate
  • Tomography, Emission-Computed, Single-Photon*

Substances

  • Radiopharmaceuticals
  • indium-111-octreotide
  • Octreotide
  • Technetium Tc 99m Medronate