Abstract
Objective:18F-Fluciclovine (FACBC) is an amino acid PET radiotracer approved for recurrent prostate cancer imaging. We investigate the use of Bayesian penalised likelihood (BPL) reconstruction for 18F-fluciclovine PET.Methods:15 18F-fluciclovine scans were reconstructed using ordered subset expectation maximisation (OSEM), OSEM + point spread function (PSF) modelling and BPL using β-values 100–600. Lesion maximum standardised uptake value (SUVmax), organ SUVmean and standard deviation were measured.Deidentified reconstructions (OSEM, PSF, BPL using β200–600) from 10 cases were visually analysed by two readers who indicated their most and least preferred reconstructions, and scored overall image quality, noise level, background marrow image quality and lesion conspicuity.Results:Comparing BPL to OSEM, there were significant increments in lesion SUVmax and signal-to-background up to β400, with highest gain in β100 reconstructions (mean ΔSUVmax 3.9, p < 0.0001). Organ noise levels increased on PSF, β100 and β200 reconstructions. Across BPL reconstructions, there was incremental reduction in organ noise with increasing β, statistically significant beyond β300–500 (organ-dependent). Comparing with OSEM and PSF, lesion signal-to-noise was significantly increased in BPL reconstructions where β ≥ 300 and ≥ 200 respectively.On visual analysis, β 300 had the first and second highest scores for image quality, β500 and β600 equal highest scores for marrow image quality and least noise, PSF and β 200 had first and second highest scores for lesion conspicuity. For overall preference, one reader preferred β 300 in 9/10 cases and the other preferred β 200 in all cases.Conclusion:BPL reconstruction of 18F-fluciclovine PET images improves signal-to-noise ratio, affirmed by overall reader preferences. On balance, β300 is suggested for 18F-fluciclovine whole body PET image reconstruction using BPL.Advances in knowledge:The optimum β is different to that previously published for 18F-fluorodeoxyglucose, and has practical implications for a relatively new tracer in an environment with modern reconstruction technologies.
Highlights
The optimisation of Bayesian penalised likelihood (BPL) reconstruction (Q.Clear, GE Healthcare) for 18F-fluorodeoxyglucose (FDG) whole body PET,[1] and its effect on evaluating various clinical entities on 18F-FDG PET/CT have been reported.[2,3,4,5] BPL runs to effective convergence and includes point spread function (PSF) modelling while controlling noise through the use of a penalty term (β), which achieves greater noise reduction as β is increased.[1]
Compared to ordered subset expectation maximisation (OSEM), lesion SUVmax and signal-to-background ratio (SBR) increased in β100–400 and PSF, with highest gain in β100 reconstructions (Figure 1)
Lesion signal-to-noise ratio (SNR) was increased in BPL reconstructions compared to OSEM where β ≥ 300 (Figure 1), with no significant intergroup difference between β300–600 (p = 0.562)
Summary
The optimisation of Bayesian penalised likelihood (BPL) reconstruction (Q.Clear, GE Healthcare) for 18F-fluorodeoxyglucose (FDG) whole body PET,[1] and its effect on evaluating various clinical entities on 18F-FDG PET/CT have been reported.[2,3,4,5] BPL runs to effective convergence and includes point spread function (PSF) modelling while controlling noise through the use of a penalty term (β), which achieves greater noise reduction as β is increased.[1]. Apart from affording a step-up in patient care, through improved image quality and confidence in interpretation, this should avoid the unnecessary complexities of relearning should current mainstream reconstruction technology get eclipsed Our experience with this process supports an ongoing collaborative effort amongst academic leaders and industry involved in this and other novel radiotracers, to maintain clear interpretation guidelines based on collective experience with different reconstruction technologies. This has to be supported by prospective study of diagnostic performance based on advanced reconstruction technology, and retrospective evaluation of the like where possible, using the same approach as this study, exploiting saved sinogram data. We propose these actions for forthcoming imaging agents as they attain regulatory approval
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