Motion Compensation Methods
Compensation of respiratory motion
For accurate quantification of tumor metabolism and tumor size reliable delineation of the true tumour boundaries is required. Patient motion leads to blurring of the acquired PET images which adversely affects this task. This is especially true for breathing induced motion. In such cases PET images of the chest will contain intrinsic motion artefacts that can make it almost impossible to, e.g., precisely delineate lung tumours. We are, therefore, working on motion compensation methods which allow to minimize the adverse effects of patient motion.
Regarding respiratory motion we have developed different motion compensation approaches which allow to track patient motion either using optical tracking systems or by using belt-based systems to register respiratory motion in a clinical setup. This allows to not only display and evaluate the effects of patient motion on a PET acquisition but also apply motion compensation methods such as amplitude-based gating which splits PET data based on phase information gathered from detailed motion analyses. Furthermore, we are working on development of motion compensation methods which allow to map data of different gates to a single motion compensated image (using non-rigid motion transforms) without loosing the statistical precision a single gate solution imposes. In fact, we could show that our motion compensation methods are working in a clinical setup and that there is a huge potential for improving quantitative assessment of the tumour metabolism if our methods are directly applied.References:
Compensation of head motion
Apart from respiratory motion, involuntary or voluntary patient movement during a PET acquisition is another source of image blurring. While in case of whole body motion motion compensation methods are substantially complicated due to the non-rigid nature of motion in that case, motion of the head during a brain acquisition can be corrected quite accurately. One well-known method for correction of head motion is the so-called MAF (multiple acquisition framing) method which splits PET data into seperate (acquisition) time frames based on the significance of occurred motion. This has the disadvantage, however, that one needs to compromise between accuracy of the motion correction and the minimum required statistical accuracy (i.e. acquisition time) of the separate acquisition frames.
We, therefore, developed a motion compensation method which is applied directly to the raw list-mode data of a PET measurement instead. By spatially correcting each single coincidence event with the motion information gained by extern motion tracking devices we were able to show that applying a list-mode based motion compensation can outperform the standard MAF method for correcting head motion affected PET data.References:
- Langner, Ph.D. thesis, 2009
- Langner et al., EANM, 2008
- Bühler et al., IEEE Trans. Med. Imaging, 1176-1185, 2004
Translation into clinical application
In close collaboration with the University Hospital Dresden our motion compensation methods have been integrated into clinical routine. In over 1000 patient measurements we were able to apply our head motion compensation techniques and provide nuclear physicians with motion compensated PET images as well as information on the significance of occurred patient motion. Furthermore, in case of respiratory motion we have implemented our method in a way that medical physics experts are able to compensate for respiratory motion and generate corrected images for evaluation by the resonsible physicians. For ease of use in the clinical context dedicated graphical user interfaces have been developed as well.References: