Abstract
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Objectives Different approaches have been previously proposed to correct for either respiratory motion artifacts (RMA) or partial volume effects (PVE) in PET/CT imaging. The aim of this abstract is to propose and evaluate a joint correction approach to simultaneously correct for RMA and PVE.
Methods The proposed joint correction approach incorporates a model of RMA, PVE and object size/shape. The motion blurring kernel (MBK) is then estimated from the deconvolution of the joint model while the activity concentration (AC) of the tumor is estimated from the normalization of the MBK. To evaluate its performance, a simulation and a phantom study were performed while a uniformly-distributed and a sinusoidal motion waveform were used to control the tumor motion respectively. The resultant MBK was compared to the true MBK by measuring a correlation coefficient between the two kernels. The measured tumor AC derived from the proposed method was compared to the true AC as well as the ACs of the motion only corrected image, PVE only corrected image and image without any corrections.
Results For the simulation and phantom studies, the estimated MBK approximates the true MBK with a correlation coefficient of 0.94 and 0.92 with the true motion respectively. In both studies, the derived tumor ACs of the jointly corrected images were similar to the true AC with an average difference of 0.4% and 1.0% respectively. Furthermore, the tumor ACs for the computer simulation study (phantom study) on the motion only corrected images, PVE only corrected images and images without correction were on average 70.2% (75%), 54.8% and 49.8% (47%) of the true AC respectively.
Conclusions The proposed joint correction approach can simultaneously compensate for the RMA and PVE in lung PET/CT imaging thereby improving PET image resolution and accuracy in quantification