Login

PSMR-TBP 2022 9th Conference on PET/MR and SPECT/MR & Total-body PET workshop

28 May 2022 to 1 June 2022
La Biodola, Isola d'Elba
Europe/Rome timezone

Contact

Session

Image reconstruction for PET/MR and TB-PET

31 May 2022, 15:30
La Biodola, Isola d'Elba

La Biodola, Isola d'Elba

Hotel Hermitage

Conveners

Image reconstruction for PET/MR and TB-PET

  • Marco Aiello

Presentation materials

There are no materials yet.

  1. 40. MRI-guided PET Reconstruction with Adaptive Prior Strength
    Dr Anazodo Udunna (Lawson Health Research Institute, London, Ontario, Canada.)
    31/05/2022, 15:30
    Image reconstruction for PET/MR and TB-PET
    Oral

    MRI-guided PET reconstruction can potentially reduce noise and increase spatial resolution. However, the balance between measured data and prior information usually requires manual tuning depending on the measured statistics and desired image quality. This work presents an adaptive method of MRI-guided PET reconstruction which does not require manual tuning, and is robust against a wide range...

    Go to contribution page
  2. 33. AI-supported ROI list-mode reconstruction for improved lesion detectability in large-FOV PET
    Dr Joerg Peter (German Cancer Research Center)
    31/05/2022, 15:50
    Image reconstruction for PET/MR and TB-PET
    Oral

    The extended number of detectors in large-field-of-view (LFOV) PET provides the possibility to increase the number of lines-of-response (LORs), as axial acceptance angles (α) become significantly larger when compared with conventional PET. Considering a small region inside the LFOV, however, its corresponding LORs become a very small fraction of the total detected signal and, hence, their...

    Go to contribution page
  3. 82. Evaluation of different image regularization techniques on simulated phantoms with the TRIMAGE brain PET scanner
    Luigi Masturzo (Istituto Nazionale di Fisica Nucleare)
    31/05/2022, 16:10
    Image reconstruction for PET/MR and TB-PET
    Oral

    This work presents the study of the performance of the TRIMAGE brain PET scanner obtained through experimental detectors characterization, simulated phantom acquisitions and image reconstruction optimization. The TRIMAGE scanner uses dual-layer staggered LYSO:Ce crystal matrices coupled to silicon photomultipliers (SiPM). The dual layer architecture provides depth of interaction (DOI)...

    Go to contribution page
  4. 49. An unsupervised deep learning framework for respiratory motion correction in PET
    Bia Rosin (Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, PET Department)
    31/05/2022, 16:30
    Image reconstruction for PET/MR and TB-PET
    Oral

    Introduction: Breathing related patient motion during PET scans causes image artifacts, notably spatially variant blurring and degradation of contrast recovery. Dealing with these artifacts commonly involves respiratory gating, i.e. splitting the acquisition data into several temporal bins ("gates") depending on the respiratory cycle. With registration of the different gated images to one...

    Go to contribution page
  5. 4. Development of a deep learning method for CT-free correction for an ultra-long axial field of view PET scanner
    Song Xue
    31/05/2022, 16:50
    Image reconstruction for PET/MR and TB-PET
    Oral

    Introduction: The possibility of low-dose positron emission tomography (PET) imaging using high sensitivity long axial field of view (FOV) PET/computed tomography (CT) scanners makes CT a critical radiation burden in clinical applications. Artificial intelligence has shown the potential to generate PET images from non-corrected PET images. Our aim in this work is to develop a CT-free...

    Go to contribution page
  6. 70. Invited Talk - Deep learning reconstruction for PET-MR and total-body PET: present status and future perspectives
    Andrew Reader (Kings College London)
    31/05/2022, 17:10
  7. 71. Final remarks
    31/05/2022, 17:50
Building timetable...