This study investigates the impact of various image registration strategies on test-retest variability of SUV and metabolic volume derived from repeat [18F]FDG PET scans. The main purpose of the study was to identify image registration strategies that can serve as candidates for registration of repeat [18F]FDG PET scans in order to monitor response to treatment. To the best of our knowledge, this is the first study that reports on the impact of various types of image registration (with different levels of image cropping and various types of input data) on quantitative measures derived from [18F]FDG PET scans. Note that this study did not focus on the accuracy of the entire registered images, but on the accuracy of the registered baseline VOIs only. The idea is that not the images but the VOIs will be transformed, such that tracer uptake quantification will always be performed on the original (i.e. non-transformed) images. In this setting, accurate and precise VOI transformation are most important. However, all registered images were checked visually for image artefacts that might have resulted from (non-rigid) image registration. Occasionally, during optimization and calibration of the registration strategies slightly higher DSCs then those reported in this paper were observed for some patients when other registration parameters were used. However, the use of these parameters was considered not feasible for reuse of baseline VOIs due to (severe) image artefacts that were observed in the registered images. Only those parameters were used that showed a high DSC but did not show image artefacts. Despite that the parameters have been chosen carefully, recalibration of the registration strategies might be required for other purposes (other type of studies or tracers) and final registration results should always be supervised (i.e. visually checked). Two independent software packages were used to investigate the various registration strategies. Results were very similar (Figure 1), although the small improvement in DSC values obtained with Elastix was significant.
Consistent with a previous study  showing that the type of intermodality image registration had no significant impact on SUVmax, the present study showed that the type of intramodality registration had no significant impact on absolute TRT of SUVmax compared with delineating VOIs separately on both scans. The same was true for SUVmean (except for CTPET registration) and TLG. Only for absolute TRT of metabolic volume, significant lower values were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET and CTPET registrations. Rigid image registration does not allow any changes in volume, so in theory this should always be zero. Some lesions, however, showed small non-zero TRT values due to small sampling errors or VOIs that were moved partly outside the image borders after registration.
For relative TRT values, similar trends were observed. However, the type of intramodality registration had no significant impact on relative TRT of metabolic volume. In addition, for relative TRT of SUVmean and TLG, significantly higher values were found for all registration strategies, except for relative TRT of TLG obtained using local rigid PET, and non-rigid PET and CTPET.
For most lesions, CT registration provided accurate results. As illustrated in Figure 3, however, some small lung lesions showed small misalignments between PET and CT that could have been caused by respiratory motion. This resulted in a poorer performance of CT registration for these lesions. In these cases, performance of CT image registration could probably be improved by using respiratory gating  or intermodality image registration to correct for small residual misalignments between CT and PET [15, 16]. In general, performance of CT registration might be improved by using the original CT images that were not downsampled to the PET resolution. Van Herk et al  showed that reducing pixel resolution has little effect on performance of rigid CT registration and can be used to speed up the algorithm without loss of accuracy. However, this has yet to be shown for non-rigid CT registrations. NAC image registration was investigated, as (attenuation corrected) PET images could potentially contain errors due to faulty attenuation correction resulting from respiratory motion or from small mismatches between CT and PET. This type of registration, however, provided poorer results than the other registration strategies and, therefore, NAC image registration cannot be recommended.
Despite a small misalignment of a few small lung lesions and the relatively poorer DSC performance compared with PET image registration, CT image registration might still be a good candidate for certain response monitoring studies. Disagreements between CT or PET and clinical response have been observed . When it is of interest to reuse the baseline VOI without changes in volume or shape, i.e. to study changes of [18F]FDG uptake within the anatomical volume , then CT image registration could be of interest, because (local) rigid CT image registration showed good similarity (median DSC: 0.72) with no change in absolute volume TRT.
Using various levels of image cropping did not show an effect on non-rigid image registration. Roels et al  suggested using more local non-rigid image registration to minimize effects of high uptake regions such as the bladder in patients with rectal cancer. In the present study, however, effects of these regions were already minimized by setting the maximum allowed SUV to 10. This could explain why local non-rigid image registrations did not show an improvement in performance.
Rigid PET image registration showed poor results for most lung lesions. In contrast, non-rigid PET and CTPET registrations showed good performance for all types of lesions, and they provided high similarity and similar trends as delineating VOI separately. In addition, CTPET provided significantly lower absolute TRT of volume and SUVmean. Nevertheless, as CTPET requires two non-rigid registrations (first CT, followed by PET), non-rigid PET image registration is preferred. For both non-rigid PET and CTPET registrations, DSC was somewhat lower for small lesions (0.79) compared to that for large lesions (0.88). Partial volume effects may be responsible for this. Differences in quantitative measures obtained using non-rigid PET image registration were not significantly different from those obtained by drawing VOI independently on both scans (except for relative TRT in SUVmean). This may suggest that there is no additional benefit in using non-rigid image registration. Use of non-rigid image registration, however, will make data analysis easier and faster, because manual search for lesions in the retest scan can be avoided . In addition, drawing VOI on both scans independently is not perfect either, because it still shows an absolute volume TRT of 14%. Although more sophisticated tumour delineation methods may lower this value , a significantly smaller absolute volume TRT was observed for non-rigid PET image registration (7.7%). Therefore, in agreement with previous findings , non-rigid image registration may be a good alternative for obtaining accurate VOI in response monitoring studies. These results are also consistent with a previous study  showing that non-rigid intermodality image registration is a significant improvement over rigid registration for fusion between [18F]FDG PET and CT. The present study should primarily be seen as a first attempt to exclude those registration strategies that perform poorly in cases without change in metabolic volume or tracer uptake. However, because tumours are likely to change in metabolic volume and/or tracer uptake during treatment, the remaining registration strategies need to be validated in clinical response monitoring studies.