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PreprintCC BY-NC-SA 4.0arXiv

Attenuation corrected activity reconstruction for listmode TOF-PET data

  • Ahmadreza Rezaei
  • Suleman Surti
  • Samuel Matej
  • Joel S. Karp
  • Johan Nuyts
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Abstract

Time-of-flight (TOF) positron emission tomography (PET) emission data allow joint estimation of the activity and attenuation images. The methods proposed so far rely on reconstructions from TOF histograms of the measurements. However, with the rapid data increase of total-body (TB) scanners and the ever improving TOF and intrinsic resolutions, more memory and computation efficient techniques for joint reconstruction need to be developed. In this work, a modified version of the maximum likelihood activity and attenuation correction factors algorithm (MLACF) is presented, that jointly estimates the activity image together with the attenuation factors (AFs) from listmode emission data. The method does not require TOF histogramming of the data, however it still relies on a coarsely sampled (i.e. mashed) non-TOF histogram of the emission data. The activity distribution is subsequently quantified by reconstructing and enforcing known expected tissue attenuation on an attenuation reconstruction from the estimated AF values. The algorithm is evaluated using 3D simulations as well as clinical TOF-PET measurements of a patient scan and compared to reference reconstructions. As could be expected, we find the reconstruction accuracies improve with an "optimal" histogramming of the TOF emission data, increasing the signal to noise ratio of the data used for joint estimation. With an "optimal" histogramming of the measurements, the attenuation corrected activity distributions from TOF emission data only are comparable to the gold-standard in PET/CT.

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Cite

BibTeXRIS
BibTeX
@misc{rezaei2026attenuation,
  title = {{Attenuation corrected activity reconstruction for listmode TOF-PET data}},
  author = {Ahmadreza Rezaei and Suleman Surti and Samuel Matej and Joel S. Karp and Johan Nuyts},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.23655},
  eprint = {2610.23655},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.23655},
  note = {CC BY-NC-SA 4.0},
}
RIS
TY  - UNPB
TI  - Attenuation corrected activity reconstruction for listmode TOF-PET data
AU  - Ahmadreza Rezaei
AU  - Suleman Surti
AU  - Samuel Matej
AU  - Joel S. Karp
AU  - Johan Nuyts
PY  - 2026
DA  - 2026/10/16
PB  - arXiv
DO  - 10.48550/arxiv.2610.23655
UR  - https://arxiv.org/abs/2610.23655
AB  - Time-of-flight (TOF) positron emission tomography (PET) emission data allow joint estimation of the activity and attenuation images. The methods proposed so far rely on reconstructions from TOF histograms of the measurements. However, with the rapid data increase of total-body (TB) scanners and the ever improving TOF and intrinsic resolutions, more memory and computation efficient techniques for joint reconstruction need to be developed. In this work, a modified version of the maximum likelihood activity and attenuation correction factors algorithm (MLACF) is presented, that jointly estimates the activity image together with the attenuation factors (AFs) from listmode emission data. The method does not require TOF histogramming of the data, however it still relies on a coarsely sampled (i.e. mashed) non-TOF histogram of the emission data. The activity distribution is subsequently quantified by reconstructing and enforcing known expected tissue attenuation on an attenuation reconstruction from the estimated AF values. The algorithm is evaluated using 3D simulations as well as clinical TOF-PET measurements of a patient scan and compared to reference reconstructions. As could be expected, we find the reconstruction accuracies improve with an "optimal" histogramming of the TOF emission data, increasing the signal to noise ratio of the data used for joint estimation. With an "optimal" histogramming of the measurements, the attenuation corrected activity distributions from TOF emission data only are comparable to the gold-standard in PET/CT.
ER  -