Optimizing Attenuation Correction in ⁶⁸Ga-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 ⁶⁸Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair...

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Detalhes bibliográficos

Principais autores:	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
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-05-01
coleção:	Diagnostics
Assuntos:	attenuation correction positron emission tomography computed tomography deep learning image artifacts neural networks
Acesso em linha:	https://www.mdpi.com/2075-4418/15/11/1400
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