A deep-learning noise reduction algorithm outperforms the spatial filters previously required for bone SPECT on a high-speed whole-body 360° CZT-camera.

Achraf Bahloul^1,2, Franklin Rajadhas³, Matthieu Doyen⁴, Yechiel Lamash⁵, Nathaniel Roth⁵, Véronique Roch^3,4, Pierre-Yves Marie^3,4, Laetitia Imbert^3,4

¹Department of Nuclear Medicine and Nancyclotep Imaging platform, CHRU-Nancy, Nancy, France. a.bahloul@chru-nancy.fr.

²Université de Lorraine, INSERM, IADI, Nancy, France. a.bahloul@chru-nancy.fr.

³Department of Nuclear Medicine and Nancyclotep Imaging platform, CHRU-Nancy, Nancy, France.

⁴Université de Lorraine, INSERM, IADI, Nancy, France.

⁵Spectrum Dynamics Medical, Caesarea, Israel.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Spatial filters are required to suppress the statistical noise of SPECT images but with an unavoidable smoothing effect that further decreases the SUV and contrast. This study assesses a deep-learning noise reduction (DLNR) algorithm, previously developed to further reduce bone SPECT recording time on a high-speed whole-body 360° CZT-camera, when used instead of, rather than in additio…

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