@misc{oai:repo.qst.go.jp:00072507,
 author = {赤松, 剛 and 田島, 英朗 and 脇坂, 秀克 and 前田, 貴雅 and 岩男, 悠真 and 吉田, 英治 and 山下, 大地 and 山谷, 泰賀 and 赤松 剛 and 田島 英朗 and 脇坂 秀克 and 前田 貴雅 and 岩男 悠真 and 吉田 英治 and 山下 大地 and 山谷 泰賀},
 month = {Oct},
 note = {The brain PET imaging technique is very useful for clinical practice and molecular imaging research. Both high sensitivity and high resolution are required for accurate imaging. Therefore, we developed the brain-dedicated compact PET scanner which has a hemispherical helmet detector unit and an add-on detector unit located at the chin position (helmet-chin PET). We developed its prototype system using 4-layer depth-of-interaction detectors. To realize clinical applications of the PET, we need to evaluate the PET system regarding their absolute quantitation, image contrast, noise and uniformity using appropriate phantoms. However, existing standard phantoms are not applicable for the helmet-chin PET scanner because of its hemisphere geometry. Therefore, in this study, we developed two new phantoms, a small sphere contrast phantom and a 3-dimensional hemisphere brain phantom, which model a brain tumor and static regional cerebral blood flow, respectively. These phantoms have an adaptive structure for use with the helmet-chin PET scanner. We applied the developed contrast phantom to the helmet-chin PET prototype and a commercial whole-body PET/CT scanner to evaluate imaging performance of image contrast and noise. As a result, the helmet-chin PET prototype showed higher image contrast and lower image noise compared with the commercial whole-body PET/CT scanner. In conclusion, we were able to evaluate imaging performance of the helmet-chin PET prototype using the developed phantoms. These phantoms are considered to be useful for evaluating the imaging performance of brain-dedicated PET scanners, and for determining the appropriate scanning and reconstruction parameters., 2017 IEEE NSS/MIC},
 title = {New brain phantoms suitable for brain scanners with hemisphere detector arrangement},
 year = {2017}
}