Optimizing Attenuation Correction in <sup>68</sup>Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement

Optimizing Attenuation Correction in <sup>68</sup>Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement

<b>Background/Objectives:</b> Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In <sup>68</sup>Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise i...

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Main Authors: Masoumeh Dorri Giv, Guluzar Ozbolat, Hossein Arabi, Somayeh Malmir, Shahrokh Naseri, Vahid Roshan Ravan, Hossein Akbari-Lalimi, Raheleh Tabari Juybari, Ghasem Ali Divband, Nasrin Raeisi, Vahid Reza Dabbagh Kakhki, Emran Askari, Sara Harsini
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Diagnostics
Subjects:
attenuation correction
positron emission tomography computed tomography
deep learning
image artifacts
neural networks
Online Access:https://www.mdpi.com/2075-4418/15/11/1400
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