Until today several algorithms have been developed that reduce or avoid artifacts caused by cardiac and respiratory motion in computed tomography (CT). The motion information is converted into so-called motion vector fields (MVFs) and used for motion compensation (MoCo) during the image reconstruction. To analyze these algorithms quantitatively there is the need for ground truth patient data displaying realistic motion. We developed a method to generate a digital ground truth displaying realistic cardiac and respiratory motion that can be used as a tool to assess MoCo algorithms. By the use of available MoCo methods we measured the motion in CT scans with high spatial and temporal resolution and transferred the motion information onto patient data with different anatomy or imaging modality, thereby reanimating the patient virtually. In addition to these images the ground truth motion information in the form of MVFs is available and can be used to benchmark the MVF estimation of MoCo algorithms. We here applied the method to generate 20 CT volumes displaying detailed cardiac motion that can be used for cone-beam CT (CBCT) simulations and a set of 8 MR volumes displaying respiratory motion. Our method is able to reanimate patient data virtually. In combination with the MVFs it serves as a digital ground truth and provides an improved framework to assess MoCo algorithms.
Johannes Mayer, Sebastian Sauppe, Christopher M. Rank, Stefan Sawall, and Marc Kachelrieß, "Reanimating patients: cardio-respiratory CT and MR motion phantoms based on clinical CT patient data," Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321V (Presented at SPIE Medical Imaging: February 16, 2017; Published: 9 March 2017); https://doi.org/10.1117/12.2255628.
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