The purpose of the study is to evaluate the performance of a novel strategy, referred to as "virtual 4D PET", aiming at the optimization of hybrid 4D CT-PET scan for radiotherapy treatment planning. The virtual 4D PET strategy applies 4D CT motion modeling to avoid time-resolved PET image acquisition. This leads to a reduction of radioactive tracer administered to the patient and to a total acquisition time comparable to free-breathing PET studies. The proposed method exploits a motion model derived from 4D CT, which is applied to the free-breathing PET to recover respiratory motion and motion blur. The free-breathing PET is warped according to the motion model, in order to generate the virtual 4D PET. The virtual 4D PET strategy was tested on images obtained from a 4D computational anthropomorphic phantom. The performance was compared to conventional motion compensated 4D PET. Tests were also carried out on clinical 4D CT-PET scans coming from seven lung and liver cancer patients. The virtual 4D PET strategy was able to recover lesion motion, with comparable performance with respect to the motion compensated 4D PET. The compensation of the activity blurring due to motion was successfully achieved in terms of spill out removal. Specific limitations were highlighted in terms of partial volume compensation. Results on clinical 4D CT-PET scans confirmed the efficacy in 4D PET count statistics optimization, as equal to the free-breathing PET, and recovery of lesion motion. Compared to conventional motion compensation strategies that explicitly require 4D PET imaging, the virtual 4D PET strategy reduces clinical workload and computational costs, resulting in significant advantages for radiotherapy treatment planning.
Keywords: 4D CT motion modeling; 4D CT-PET; 4D PET count statistics optimization; Radiation oncology..
© The Author(s) 2014.