Reconstruction space non rigid respiratory motion correction in whole body PET/MR imaging

Objectives Respiratory motion correction in PET/MR imaging leads to reduced quantitative and qualitative image accuracy. Correction methodologies using respiratory synchronized gated frames lead to low signal to noise ratio (SNR) because of reduced statistics. In this work, elastic transformations extracted from 4D MRI datasets were applied to (i). list-mode data during image reconstruction and (ii). the reconstructed respiratory synchronized images to obtain respiration corrected PET images.

Methods Five patient datasets acquired on the SIEMENS mMR PET/MR system were used. T1-weighted 4D MR images were registered to the end expiration image using a non-rigid B-spline registration algorithm to derive transformation parameters accounting for the respiratory motion. The derived matrices were subsequently applied during the PET image reconstruction (List Mode OSEM) of the original emission list-mode data. The corrected images were compared with those produced by applying the elastic transformation in the image space followed by summing the realigned gated frames, as well as with uncorrected motion averaged images.

Results Results demonstrate that both correction techniques lead to significant improvement in accounting for respiratory motion artifacts when compared to uncorrected motion average images (>30% differences in tumor position and size and 25% SNR increase). Both correction approaches lead to nearly equivalent improvements (differences of <8% and <6% for the lesion FWHM and the SNR respectively).

Conclusions A list-mode reconstruction based respiratory motion correction for PET s using elastic transformations derived from 4D MRI has been implemented and its performance evaluated. Our results show significant respiratory motion compensation when compared to the motion average PET images, with slightly better contrast and SNR compared to an equivalent 4D PET image space elastic motion correction method.