Journal Article Simulation study comparing the helmet-chin PET with a cylindrical PET of the same number of detectors

Ahmed, Abdella  ,  Tashima, Hideaki  ,  Yoshida, Eiji  ,  Nishikido, Fumihiko  ,  Yamaya, Taiga

62pp.4541 - 4550 , 2017-05 , IOP Publishing
There is a growing interest in developing brain PET scanners with highsensitivity and high spatial resolution for early diagnosis of neurodegenerativediseases and studies of brain functions. Sensitivity of the PET scanner can beimproved by increasing the solid angle. However, conventional PET scannersare designed based on a cylindrical geometry, which may not be the mostefficient design for brain imaging in terms of the balance between sensitivityand cost. We proposed a dedicated brain PET scanner based on a hemisphericshape detector and a chin detector (referred to as the helmet-chin PET), whichis designed to maximize the solid angle by increasing the number of linesof-response in the hemisphere. The parallax error, which PET scanners witha large solid angle tend to have, can be suppressed by the use of depth-ofinteractiondetectors. In this study, we carry out a realistic evaluation of thehelmet-chin PET using Monte Carlo simulation based on the 4-layer GSOdetector which consists of a 16 × 16 × 4 array of crystals with dimensionsof 2.8 × 2.8 × 7.5 mm3. The purpose of this simulation is to show the gain inimaging performance of the helmet-chin PET compared with the cylindricalPET using the same number of detectors in each configuration. The sensitivityof the helmet-chin PET evaluated with a cylindrical phantom has a significantincrease, especially at the top of the (field-of-view) FOV. The peak-NECRof the helmet-chin PET is 1.4 times higher compared to the cylindrical PET.The helmet-chin PET provides relatively low noise images throughout theFOV compared to the cylindrical PET which exhibits enhanced noise at theperipheral regions. The results show the helmet-chin PET can significantlyimprove the sensitivity and reduce the noise in the reconstructed images.

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