Utilizing Pix2Pix conditional generative adversarial networks to recover missing data in preclinical PET scanner sinogram gaps.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PMID: 40233593

Abstract

BACKGROUND: The presence of a gap between adjacent detector blocks in Positron Emission Tomography (PET) scanners introduces a partial loss of projection data, which can degrade the image quality and quantitative accuracy of reconstructed PET images. This study suggests a novel approach for filling missing data from sinograms generated from preclinical PET scanners using a combination of an inpainting method and the Pix2Pix conditional generative adversarial network (cGAN).

Authors

Zahra Karimi

Faculty of Physics, University of Isfahan, Isfahan, Iran.
Khadijeh Rezaee Ebrahim Saraee

Faculty of Physics, University of Isfahan, Isfahan, Iran. Electronic address: kh.rezaee@ast.ui.ac.ir.
Mohammad Reza Ay

Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Peyman Sheikhzadeh

Nuclear Medicine Department, IKHC, Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.

Keywords

Algorithms Animals Generative Adversarial Networks Image Processing, Computer-Assisted Mice Neural Networks, Computer Phantoms, Imaging Positron-Emission Tomography Signal-To-Noise Ratio

External Resources

View on PubMed Access via DOI PubMed (40233593)

Utilizing Pix2Pix conditional generative adversarial networks to recover missing data in preclinical PET scanner sinogram gaps.

Abstract

Authors

Keywords

External Resources

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