Evaluation of the difference-correction effect of the gamma camera systems used by easy Z-score Imaging System (eZIS) analysis

• Published in: Annals of nuclear medicine 2014-04, Vol.28 (3), p.263-275
• Main Authors: Yamamoto, Yasushi, Onoguchi, Masahisa, Kawakami, Kazunori, Haramoto, Masuo, Wake, Rei, Horiguchi, Jun, Kitagaki, Hajime
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Brain - diagnostic imaging; Female; Gamma Cameras; Humans; Image Processing, Computer-Assisted; Imaging; Male; Medicine; Medicine & Public Health; Middle Aged; Nuclear Medicine; Original; Original Article; Phantoms, Imaging; Radiology; Tomography, Emission-Computed, Single-Photon - instrumentation
• ISSN: 0914-7187; 1864-6433
• DOI: 10.1007/s12149-014-0807-z

Objective We examined the difference of the effect by data to revise a gamma camera difference. The difference-correction method of the camera is incorporated in eZIS analysis. Methods We acquired single photon emission computed tomography (SPECT) data from the three-dimensional (3D) Hoffman brain phantom (Hoffman), the three-dimensional brain phantom (3D-Brain), Pool phantom (pool) and from normal subjects (Normal-SPECT) to investigate compensating for a difference in gamma camera systems. We compared SPECT counts of standard camera with the SPECT counts that revised the difference of the gamma camera system (camera). Furthermore, we compared the “Z-score map (Z-score)”. To verify the effect of the compensation, we examined digitally simulated data designed to represent a patient with Alzheimer’s dementia. We carried out both eZIS analysis and “Specific Volume of interest Analysis (SVA)”. Results There was no great difference between the correction effect using Hoffman phantom data and that using 3D-Brain phantom data. Furthermore, a good compensation effect was obtained only over a limited area. The compensation based on the pool was found to be less satisfactory than any of the other compensations according to all results of the measurements examined in the study. The compensation based on the Normal-SPECT data resulted in a Z-score map (Z-score) for the result that approximated that from the standard camera. Therefore, we concluded that the effect of the compensation based on Normal-SPECT data was the best of the four methods tested. Conclusions Based on eZIS analysis, the compensation using the pool data was inferior to the compensations using the other methods tested. Based on the results of the SAV analysis, the effect of the compensation using the Hoffman data was better than the effect of the compensation using the 3D-Brain data. By all end-point measures, the compensation based on the Normal-SPECT data was more accurate than the compensation based on any of the other three phantoms.