Elastic motion correction (eMOCO) is usually used in PET/CT to correct for cardiac and respiratory motions in PET images, which has proven to be effective24–26.
In this study, we implemented the eMOCO technique on a PET/MRI system and investigated its performance in a phantom as well as clinical setting. The main finding from this study is that the eMOCO improved the STD in different reconstruction and thereby provided lower noise levels within the images. The phantom results suggest that eMOCO would not necessarily improve the SNR in cold lesions. However, it may still improve detectability since the SNR at cold lesion is not necessarily correlated to detection performance as it is with hot lesions.
The statistical analysis of the hot lesions within the liver presented no significant differences between the mean SUV, however the STD from eMOCO images was found to be statistically significantly lower when compared with the gated techniques. This indicates that the eMOCO enables PET-images with less statistical noise compared to the standard clinical gating technique. The lower STD in images corrected by eMOCO was apparent in liver, lung and heart VOIs measurements and in particular when cardiac and dual gating was applied. This suggests a dependency of the cardiac and dual gating modes on the correction technique. One explanation is that the eMOCO algorithm, when applied to all phases of the cardiac cycle (8 phases), includes the counts of all cardiac gates into the reference gate24.
From a clinical/practical perspective, the here found advantages of the eMOCO might not be as impressive as initially anticipated, at least not within our group. However, the specific improvements which have been found are nevertheless important for clinical reading. First of all, the fact that the results from the phantom measurements are translated into patient imaging findings speaks for the robustness of the method. The results, namely the partly reduced noise/increased SNR are important for clinical reading as it provides the radiologist/imaging reader with increased accuracy for quantification. This is especially important in follow up studies where clinical decisions are dependent of accurate measurement of differences or quantification of lesions. Many therapy decision criteria are now based on measurement of ratio’s (i.e. Deauville Criteria for lymphoma or myeloma, Hopkins Criteria for Head and Neck cancer, PROMISE score for PSMA imaging or Krenning Score for neuroendocrine tumour imaging). The partly increased consistency and reduction of standard deviation provided by the eMOCO compared to other reconstruction methods is offering additional accuracy for those specific clinical scenarios.
There are several strategies to achieve PET motion compensation within PET/MRI. The core requirement for a given strategy is an accurate respiratory motion model that can be generated with either the aid of external device (belt), MR data or PET data. Common MR-based respiratory motion models such as retrospective gating and averaging over multiple respiratory cycles27 usually provide better image quality but are unable to reflect inter-cycle variations in the respiration pattern. Another MR-based motion model can be generated using fast MR pulse sequences, but images may suffer loss of signal-to-noise ratio and decreased spatial resolution. While, MR-based MOCO techniques can be applied to PET data using either motion compensated image reconstruction (MCIR)27 or post-reconstruction registration (PRR)28, they usually require specific modified pulse sequence in place, and/or performing MRI scanning throughout the whole PET acquisition time. One example of these techniques is the BodyCompassTM29–31 which utilizes a 3D T1-weighted radial stack-of-stars MRI pulse sequence to calculate the respiratory signal and create the motion model. In this sequence, the respiratory signal is acquired during the full PET acquisition in order to preserve both high spatial resolution and the different respiratory pattern. The technique does not require navigator echo nor external devices as a source of motion signal, instead, self-gating in a retrospective PRR reconstruction fashion is utilized. The drawback of this technique is the need for optimization of number of bins and bin sizes which are required to reduce the intra-bin motion at the diaphragm. Additionally, to generate a sufficient motion model in this case, relatively long scan time is also required which subsequently may thwarts useful MRI information during this time. It is worth mentioning here that BodyCompass was not reported in this study, since it could not be implemented with phantom acquisitions.
In contrast to self-gated MR-based MOCO, PET-based eMOCO, reported in this study, depends only on the sensor that provides the motion information while utilizing the PET data itself without the dependency on inputs from the MR side. This allowed the use of the PET-data fully without the loss of information from both MRI and PET modalities. The eMOCO algorithm computation time was also tolerable to a clinical setting which is an advantage in terms of the complete reconstruction time. Overall eMOCO is partly significantly better in phantom measurements, in breathing type motion corrections, even with lower doses – overall offering several advantages for those type of acquisitions. This was found to partly even true for cardiac imaging.
The decision on which technique to be used for motion correction still depends on the availability of the algorithms used in motion model generation and correction, as well as and the scanner time. In our study we were able to perform the eMOCO technique with the aid of offline reconstruction-eMOCO combined algorithm which required specific setting that is not yet available for clinical routine. Implementation of the eMOCO into the PET/MRI modality still to be achieved before it is available for all studies.