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Compressed sensing for low-count PET denoising in measurement space
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0002-5130-1941
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0001-5673-620X
2023 (English)In: NORDSTAT 2023 Gothenburg, Göteborgs universitet, 2023Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Low-count positron emission tomography (PET) data suffer from high noise levels, leading topoor image quality and reduced diagnostic accuracy. Compressed sensing (CS) based denoisingmethods have shown potential in medical imaging. This study investigates the performance ofCS-based denoising methods on PET sinograms.Three simulated datasets were used in this study, including circular phantom, patient pelvisphantom, and patient brain phantom. Ten sampling levels were employed to investigate the effect of data reduction on diagnostic accuracy. CS-based denoising methods were applied prereconstruction, and a conventional Gaussian post-filter was used for comparison. Performancemeasures included rRMSE, SSIM, SNR, line profiles, and FWHM.Overall, the proposed CS-based denoising methods performed similarly to the benchmark interms of lesion contrast, spatial resolution, and noise texture. The proposed methods outperformed the benchmark in low-count situations by suppressing background noise and preservingcontrast better.The results of this study demonstrate that CS-based denoising methods in the sinogram domain can improve the quality of low-count PET images, particularly in suppressing backgroundnoise and preserving contrast. These findings suggest that CS-based denoising could be apromising solution for improving the diagnostic accuracy of low-count PET data.

Place, publisher, year, edition, pages
Göteborgs universitet, 2023.
National Category
Probability Theory and Statistics Medical Image Processing Signal Processing
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-224907OAI: oai:DiVA.org:umu-224907DiVA, id: diva2:1860564
Conference
The 29th Nordic Conference in Mathematical Statistics, Gothenburg, Sweden, June 19-22, 2023.
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research Council
Funder
Swedish Research Council, 340-2013-534Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-05-28Bibliographically approved

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Leffler, KlaraYu, Jun

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Output format
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  • asciidoc
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