Small lesion visibility: Can non-TOF PET with advanced reconstruction techniques or higher statistics achieve the same lesion visibility as TOF PET? (2016)

1966 Objectives TOF PET has been shown to improve the lesion visibility by enriching the information about the possible signal origin up to a TOF resolution limit as compared to non-TOF imaging. On the other hand, there are claims that advanced iterative image reconstruction techniques (such as in-reconstruction point spread function modelling, as well as maximum a posteriori (MAP) reconstruction with edge-preserving penalties) can also improve lesion visibility for non-TOF PET imaging. There are also claims that in most cases higher statistics non-TOF data can achieve similar results as lower statistics TOF data. In this study, we investigated using real patient data whether non-TOF PET with advanced reconstruction or higher count statistics can always achieve similar lesion visibility as provided by the standard TOF PET approach. Methods We have obtained patient studies with lung lesions acquired from an investigational pre-commercial Philips Vereos PET/CT, operating at 325ps TOF resolution. As additional crosscheck, we have also generated synthesized data sets by adding artificial list-mode events from several GATE simulated small lesions (5 mm in diameter) into the real patient data. For the GATE simulation, matched attenuation maps of the corresponding patient studies were used. The data were reconstructed using standard iterative blob-based list-mode PET reconstruction software with TOF as well as without TOF information (the latter to mimic non-TOF imaging). Advanced reconstruction (AR) was also applied to both TOF and non-TOF data using in-reconstruction modeling of the system’s point spread function in combination with MAP regularization by relative differences prior (RDP). The number of iterations for non-TOF reconstruction was appropriately increased in order to compensate for the slower convergence. To examine the TOF gain limits in lesion detectability, we also reduced the patient data statistics to half- and quarter- counts by keeping only every 2nd or every 4th detected list-mode event over the duration of the scan. We assessed whether the lesions that were visible in standard (non-AR) TOF images were also visible in non-TOF reconstruction using the same data, both without and with the advanced reconstruction processing (with AR). Also we evaluated how many lesions were preserved in quarter count and half count TOF-based images, as compared to full count non-TOF images. Results In one patient study, we identified three small lung lesions in a single transaxial slice in the standard TOF image while only one of the three lesions could be found in the standard non-TOF images. Applying AR to non-TOF reconstruction improved image appearance as compared to the standard non-TOF image and also led to a similar maximum intensity value for this single visible lesion as in the TOF image. However, even with AR, non-TOF failed to recover the other two lesions, clearly visible in TOF images. Even when reducing counts to half or quarter of the original data, the TOF was still able to outperform full count non-TOF image. Similar results were obtained for the synthesized data with implanted lesions, in which TOF-based imaging was able to recover all the implanted lesions while some lesions were not discernible/clearly visible in the non-TOF images, either in the standard or advanced non-TOF reconstruction. Conclusions Although advanced reconstruction techniques did improve the image appearance and feature contrast-to-noise ratio in both TOF and non-TOF reconstruction, however, the images illustrate the existence of “visible-in-TOF” lesions that could not be easily recovered by enhancing the non-TOF reconstruction with AR. Thus, we conclude that non-TOF PET, neither with the applied advanced reconstruction techniques nor with high-count statistics, provides equivalent results in terms of small lesion visibility as the TOF PET approach.