PET reconstruction algorithms have long relied on sinogram rebinning. However, as detectors grow smaller in a recent wave of cutting-edge scanners, individual sensors no longer accrue hundreds of photons. Instead, most detect a single photon or none at all, effectively turning sinogram data into point-cloud measurements. The highly heterogeneous sensitivity of these scanners is another issue. We approach sinogram rebinning in the face of these challenges with a density-estimation framework that promotes knot sparsity in an underlying spline basis.
Titre
PET REBINNING WITH REGULARIZED DENSITY SPLINES
Publié dans
2023 Ieee 20Th International Symposium On Biomedical Imaging, Isbi
Présenté à
20th IEEE International Symposium on Biomedical Imaging (ISBI), APR 18-21, 2023, Cartagena, COLOMBIA
Date
2023-01-01
Editeur
IEEE, New York
ISSN
1945-7928
ISBN
978-1-6654-7358-3
Grant
Swiss National Science Foundation under the Sinergia grant: CRSII5 198569
Lausanne University Hospital (CHUV)
University of Lausanne (UNIL)
Ecole polytechnique federale de Lausanne (EPFL)
University of Geneva (UNIGE)
Geneva University Hospitals (HUG)
Swiss National Science Foundation (SNF): CRSII5_198569
Date de création de la notice
2024-02-16