Abstract
Simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) is a promising new technique allowing the fusion of functional (PET) and anatomical/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames which lead to low signal to noise ratio (SNR) considering the associated low statistics. More advanced correction approaches, previously developed in PET/Computed Tomography (CT) imaging, consist on either registering all the reconstructed gated frames to the reference one or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two, previously implemented in PET/CT, correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4D-PET/MR acquisitions. Two different correction approaches were evaluated, comparing the incorporation of elastic transformations extracted from four dimensional (4D) MRI datasets during PET list-mode image reconstruction to a post-reconstruction image based approach. Methods: Eleven 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 deformation matrices (DMs) accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space (RS) method). The corrected images were compared with those produced by applying the DMs in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion averaged images. Results: Both correction techniques lead to significant improvement in accounting for respiratory motion artifacts when compared to uncorrected motion average images. These improvements include SNR (mean increase of 28.0% and 24.2% for the RS and IS methods respectively), the lesion size (reduction of 60.4% and 47.9% respectively), lesion contrast (increase of 70.1% and 57.2%) and lesion position (changes of 60.9% and 46.7%). Conclusion: Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D-PET RS approach.
- Image Processing
- Image Reconstruction
- Oncology: Lung
- PET/MRI
- Respiratory
- 4D PET/MR
- Oncology
- Reconstruction
- Respiratory motion correction
- Copyright © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.