@article{oai:repo.qst.go.jp:00086006,
 author = {Yuma, Iwao and Go, Akamatsu and Hideaki, Tashima and Miwako, Takahashi and Taiga, Yamaya and Yuma, Iwao and Go, Akamatsu and Hideaki, Tashima and Miwako, Takahashi and Taiga, Yamaya},
 issue = {2},
 journal = {Radiological Physics and Technology},
 month = {Mar},
 note = {Marker-less head motion correction methods have been well-studied, but no report has discussed potential issues in the positional calibration between a PET system and an external sensor. Here, we develop a method for positional calibration between the PET system and an external range sensor for practical head motion correction. The basic concept of the developed method involves using the subject’s face model as a marker not only for head motion detection but also for the system positional calibration. The subject’s face model, which can be easily obtained with the range sensor, can also be calculated from a computed tomography (CT) image of the same subject. By applying an appropriate matching algorithm between CT and PET images, the CT image, which is acquired separately for the purpose of attenuation correction in PET, has the same coordinates as the PET image. The proposed method was implemented in helmet-type PET and motion correction accuracy was assessed quantitatively using a mannequin head. The phantom experiments demonstrated the performance of the developed motion correction method; high-resolution images with no trace of the applied motion were obtained as if no motion had been given. Statistical analysis supported the visual assessment results in terms of the spatial resolution, contrast recovery, and uniformity, and the results implied that motion with correction slightly improved image quality compared with the motionless case. Tolerance of the developed method to potential tracking errors had at least a difference of 10 % in the amplitude of the rotation angle.},
 pages = {125--134},
 title = {Marker-less and calibration-less motion correction method for brain PET},
 volume = {15},
 year = {2022}
}