Optimization of Image Reconstruction for90Y Selective Internal Radiotherapy on a Lutetium Yttrium Orthosilicate PET/CT System Using a Bayesian Penalized Likelihood Reconstruction Algorithm.
Imaging on a γ-camera with90Y after selective internal radiotherapy (SIRT) may allow for verification of treatment delivery but suffers relatively poor spatial resolution and imprecise dosimetry calculation.90Y PET/CT imaging is possible on 3-dimensional, time-of-flight machines; however, images are usually poor because of low count statistics and noise. A new PET reconstruction software using a Bayesian penalized likelihood (BPL) reconstruction algorithm (termed Q.Clear) was investigated using phantom and patient scans to optimize the reconstruction for post-SIRT imaging and clarify whether BPL leads to an improvement in clinical image quality using90Y.Methods:Phantom studies over an activity range of 0.5-4.2 GBq were performed to assess the contrast recovery, background variability, and contrast-to-noise ratio for a range of BPL and ordered-subset expectation maximization (OSEM) reconstructions on a PET/CT scanner. Patient images after SIRT were reconstructed using the same parameters and were scored and ranked on the basis of image quality, as assessed by visual evaluation, with the corresponding SPECT/CT Bremsstrahlung images by 2 experienced radiologists.Results:Contrast-to-noise ratio was significantly better in BPL reconstructions when compared with OSEM in phantom studies. The patient-derived BPL and matching Bremsstrahlung images scored higher than OSEM reconstructions when scored by radiologists. BPL with a β value of 4,000 was ranked the highest of all images. Deadtime was apparent in the system above a total phantom activity of 3.3 GBq.Conclusion:BPL with a β value of 4,000 is the optimal image reconstruction in PET/CT for confident radiologic reading when compared with other reconstruction parameters for90Y imaging after SIRT imaging. Activity in the field of view should be below 3.3 GBq at the time of PET imaging to avoid deadtime losses for this scanner.
Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom lisa.rowley4@nhs.net.
Lisa M
LM
Rowley
Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; and.
Kevin M
KM
Bradley
Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; and.
Philip
P
Boardman
Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Aida
A
Hallam
Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.|Department of Oncology, University of Oxford, Oxford, United Kingdom.
Daniel R
DR
McGowan
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008187:Lutetium; D019047:Phantoms, Imaging; D000072078:Positron Emission Tomography Computed Tomography; D059629:Signal-To-Noise Ratio; D017640:Silicates; D015021:Yttrium Radioisotopes / Q000627:therapeutic use*
D017640:Silicates; D015021:Yttrium Radioisotopes; C477682:lutetium orthosilicate; D008187:Lutetium
Bayesian penalized likelihood; Q.Clear; image reconstruction; positron emission tomography