Respiratory Motion Estimation Using Vibration Model and Reducing Motion Blur through Deconvolution: A Simulation Study
Respiratory motion is a main source of degradation in positron emission tomography (PET)/computed tomography (CT) image, it leads to significant motion artifacts of PET image which are known to influence diagnosis and treatments in radiation oncology. Existing approaches to correct motion artifacts involve using gating devices and/or 4D CT. However they either suffer from high CT dose or high computation burden. In this paper we present a sinusoid vibration model to simulate the respiratory motion, the motion extent and direction are derived from Radon transform of the cepstrum of blurred image. Then we employ three typical deconvolution algorithms (Wiener filter, Constrained least square, and Richardson-Lucy) to eliminate the motion blur respectively according to estimated parameters and compare their de-blurring results. The experiments on both synthetic and phantom images show good performance of our method in identifying vibration modeled respiration motion. The advantage of our method lies in its convenience and economy since only static PET image is needed for analysis.
PET/CT respiratory motion vibration motion blur deconvolution
XU Quan-Sheng YUAN Ke-Hong YU Li-Juan WANG Wen-Zhi YE Da-Tian
Biomedical Engineering Department, Tsinghua University, Beijing 100084 Biomedical Engineering Resear PET/CT Center of Tumor Hospital, Harbin Medical University, Harbin 150081
国际会议
南京
英文
75-86
2009-10-23（万方平台首次上网日期，不代表论文的发表时间）