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Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.

José Calatayud-Jordán1, Nuria Carrasco-Vela2, José Chimeno-Hernández3

  • 1Department of Nuclear Medicine, La Fe University and Polytechnical Hospital, Av. Fernando Abril Martorell 106, 46026, Valencia, Spain. calatayud_josjor@gva.es.

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|June 17, 2024
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Summary

This study optimized parameters for Y PET/MR imaging using Bayesian penalized likelihood (BPL) reconstruction. Q.Clear with a penalty of 4000 and 15-minute acquisition time yielded optimal image quality and lesion detectability.

Keywords:
Bayesian penalized likelihoodImage qualityPET/MRQ.ClearRadioembolizationYttrium-90

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Radiochemistry

Background:

  • Positron Emission Tomography (PET) imaging with Y is crucial for liver radioembolization, aiding lesion identification and dosimetry.
  • Bayesian penalized likelihood (BPL) reconstruction algorithms offer improved image quality and lesion detectability over traditional OSEM methods.
  • Optimizing parameters for commercial BPL algorithms like GE's Q.Clear in PET/MR is essential for clinical application.

Purpose of the Study:

  • To investigate and determine optimal reconstruction parameters for Y PET/MR imaging using the Q.Clear BPL algorithm.
  • To compare the performance of Q.Clear against OSEM reconstruction methods for Y imaging.
  • To evaluate the impact of key parameters such as the penalty, acquisition time, and pixel size on image quality and quantitative accuracy.

Main Methods:

  • Utilized a NEMA phantom with an 8:1 sphere-to-background ratio, acquiring data at clinically relevant Y activities (0.7-3.3 MBq/ml).
  • Performed Q.Clear reconstructions varying the penalty (20-6000), acquisition time (5-20 min), and pixel size (1.56-4.69 mm).
  • Compared Q.Clear (specifically with = 4000) to OSEM (28 subsets, 2/4 iterations, with/without TOF), assessing recovery coefficients, COV, BV, CNR, and residual activity.

Main Results:

  • Increasing the penalty reduced recovery coefficients, COV, and background variability, while maximizing contrast-to-noise ratio (CNR) at = 4000.
  • Higher values beyond 4000 led to oversmoothing; = 1000-2000 may be suitable for quantification.
  • Longer acquisition times (optimal at 15 min) and Q.Clear reconstructions generally improved CNR compared to OSEM, especially with Time-of-Flight.

Conclusions:

  • A penalty of 4000 with Q.Clear provides optimal image quality for Y PET/MR imaging, balancing lesion detectability and noise reduction.
  • While = 4000 is ideal for image quality, lower values (1000-2000) are recommended for quantitative accuracy in dosimetry.
  • An acquisition time of 15 minutes is proposed as optimal for clinical Y PET/MR liver radioembolization studies, enhancing CNR and reducing noise.