Abstract
Lung motion due to respiration causes image degradation in medical imaging, especially in nuclear medicine which requires long acquisition times. We have developed a method for image correction between the respiratory-gated (RG) PET images in different respiration phases or breath-hold (BH) PET images in an inconsistent respiration phase. In the method, the RG or BH-PET images in different respiration phases are deformed under two criteria: similarity of the image intensity distribution and smoothness of the estimated motion vector field (MVF). However, only these criteria may cause unnatural motion estimation of lung. In this paper, assuming the use of a PET-CT scanner, we add another criterion that is the similarity for the motion direction estimated from inhalation and exhalation CT images. The proposed method was first applied to a numerical phantom XCAT with tumors and then applied to BH-PET image data for seven patients. The resultant tumor contrasts and the estimated motion vector fields were compared with those obtained by our previous method. Through those experiments we confirmed that the proposed method can provide an improved and more stable image quality for both RG and BH-PET images.
Highlights
Positron emission tomography (PET) is one of the useful modalities for tumor diagnosis of thoracoabdominal organs
The RG or BH-PET images are deformed under two criteria: (1) similarity of the image intensity distribution and (2) smoothness of the estimated motion vector field (MVF)
By assuming use of a PET-CT scanner, we added a similarity measure on the motion direction estimated from two CT images in different respiration phases as another criterion for the registration and summation method (RSM)
Summary
Positron emission tomography (PET) is one of the useful modalities for tumor diagnosis of thoracoabdominal organs. We have proposed a method for nonlinearly correcting the motion of the lung between RG reconstructed images in different respiratory phases and adding them together to Computational and Mathematical Methods in Medicine obtain an image with less motion blur and less noise [3]. A similar method proposed by Dawood et al [4, 5] utilizes a global optical flow algorithm for motion correction of images in individual gates. As another imaging technique to avoid the respiratory motion blur, a breath-hold (BH) acquisition technique has recently been studied actively [9,10,11,12,13]. The proposed method was applied to clinical data composed of BH images at expiration and results were compared with those of the other two methods as well
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