Impact of point spread function modelling and time of flight on FDG uptake measurements in lung lesions using
Ian S Armstrong1,2, Matthew D Kelly3, Heather A Williams4
1Nuclear Medicine, Central Manchester University Hospitals, Oxford Road, Manchester, UK. Ian.Armstrong@cmft.nhs.uk.
Background:
The use of maximum standardised uptake value (SUVmax) is commonplace in oncology positron emission tomography (PET). Point spread function (PSF) modelling and time-of-flight (TOF) reconstructions have a significant impact on SUVmax, presenting a challenge for centres with defined protocols for lesion classification based on SUVmax thresholds. This has perhaps led to the slow adoption of these reconstructions. This work evaluated the impact of PSF and/or TOF reconstructions on SUVmax, SUVpeak and total lesion glycolysis (TLG) under two different schemes of post-filtering.
Methods:
Post-filters to match voxel variance or SUVmax were determined using a NEMA NU-2 phantom. Images from 68 consecutive lung cancer patients were reconstructed with the standard iterative algorithm along with TOF; PSF modelling - Siemens HD·PET (HD); and combined PSF modelling and TOF - Siemens ultraHD·PET (UHD) with the two post-filter sets. SUVmax, SUVpeak, TLG and signal-to-noise ratio of tumour relative to liver (SNR(T-L)) were measured in 74 lesions for each reconstruction. Relative differences in uptake measures were calculated, and the clinical impact of any changes was assessed using published guidelines and local practice.
Results:
When matching voxel variance, SUVmax increased substantially (mean increase +32% and +49% for HD and UHD, respectively), potentially impacting outcome in the majority of patients. Increases in SUVpeak were less notable (mean increase +17% and +23% for HD and UHD, respectively). Increases with TOF alone were far less for both measures. Mean changes to TLG were <10% for all algorithms for either set of post-filters. SNR(T-L) were greater than ordered subset expectation maximisation (OSEM) in all reconstructions using both post-filtering sets.
Conclusions:
Matching image voxel variance with PSF and/or TOF reconstructions, particularly with PSF modelling and in small lesions, resulted in considerable increases in SUVmax, inhibiting the use of defined protocols for lesion classification based on SUVmax. However, reduced partial volume effects may increase lesion detectability. Matching SUVmax in phantoms translated well to patient studies for PSF reconstruction but less well with TOF, where a small positive bias was observed in patient images. Matching SUVmax significantly reduced voxel variance and potential variability of uptake measures. Finally, TLG may be less sensitive to reconstruction methods compared with either SUVmax or SUVpeak.
