Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

Journal: Medical physics

Published Date: Jan 10, 2024

Abstract

BACKGROUND: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging.

Authors

Reza Jahangir

Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
Alireza Kamali-Asl
Hossein Arabi

Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
Habib Zaidi

Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

Keywords

Brain Deep Learning Fluorodeoxyglucose F18 Image Processing, Computer-Assisted Magnetic Resonance Imaging Positron Emission Tomography Computed Tomography Positron-Emission Tomography

External Resources

View on PubMed Access via DOI PubMed (38197492)

Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

Abstract

Authors

Keywords

External Resources

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