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Imaging on a γ-camera with 90Y 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 using 90Y. 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 for 90Y 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.

Original publication




Journal article


J Nucl Med

Publication Date





658 - 664


Bayesian penalized likelihood, Q.Clear, image reconstruction, positron emission tomography, Algorithms, Bayes Theorem, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Lutetium, Phantoms, Imaging, Positron Emission Tomography Computed Tomography, Signal-To-Noise Ratio, Silicates, Yttrium Radioisotopes