Abstract
Purpose: F-18-FPCIT that shows strong familiarity with DAT located at a neural terminal site offers diagnostic information about DAT density state in the region of the striatum especially Parkinson's disease. In this study, we altered the iteration and subset and measured SUV${\pm}$SD and Contrasts from phantom images which set up to specific iteration and subset. So, we are going to suggest the appropriate range of the iteration and subset. Materials and Methods: This study has been performed with 10 normal volunteers who don't have any history of Parkinson's disease or cerebral disease and Flangeless Esser PET Phantom from Data Spectrum Corporation. $5.3{\pm}0.2$ mCi of F-18-FPCIT was injected to the normal group and PET Phantom was assembled by ACR PET Phantom Instructions and it's actual ratio between hot spheres and background was 2.35 to 1. Brain and Phantom images were acquired after 3 hours from the time of the injection and images were acquired for ten minutes. Basically, SIEMENS Bio graph 40 True-point was used and True-X method was applied for image reconstruction method. The iteration and Subset were set to 2 iterations, 8 subsets, 3 iterations, 16 subsets, 6 iterations, 16 subsets, 8 iterations, 16 subsets and 8 iterations, 21 subsets respectively. To measure SUVs on the brain images, ROIs were drawn on the right Putamen. Also, Coefficient of variance (CV) was calculated to indicate the uniformity at each iteration and subset combinations. On the phantom study, we measured the actual ratio between hot spheres and back ground at each combinations. Same size's ROIs were drawn on the same slide and location. Results: Mean SUVs were 10.60, 12.83, 13.87, 13.98 and 13.5 at each combination. The range of fluctuation by sets were 22.36%, 10.34%, 1.1%, and 4.8% respectively. The range of fluctuation of mean SUV was lowest between 6 iterations 16 subsets and 8 iterations 16 subsets. CV showed 9.07%, 11.46%, 13.56%, 14.91% and 19.47% respectively. This means that the numerical value of the iteration and subset gets higher the image's uniformity gets worse. The range of fluctuation of CV by sets were 2.39, 2.1, 1.35, and 4.56. The range of fluctuation of uniformity was lowest between 6 iterations, 16 subsets and 8 iterations, 16 subsets. In the contrast test, it showed 1.92:1, 2.12:1, 2.10:1, 2.13:1 and 2.11:1 at each iteration and subset combinations. A Setting of 8 iterations and 16 subsets reappeared most close ratio between hot spheres and background. Conclusion: Findings on this study, SUVs and uniformity might be calculated differently caused by variable reconstruction parameters like filter or FWHM. Mean SUV and uniformity showed the lowest range of fluctuation at 6 iterations 16 subsets and 8 iterations 16 subsets. Also, 8 iterations 16 subsets showed the nearest hot sphere to background ratio compared with others. But it can not be concluded that only 6 iterations 16 subsets and 8 iterations 16 subsets can make right images for the clinical diagnosis. There might be more factors that can make better images. For more exact clinical diagnosis through the quantitative analysis of DAT density in the region of striatum we need to secure healthy people's quantitative values.
F-18-FPCIT는 뇌 선조체에 주로 분포된 도파민 운반체에 강한 친화력을 보이며, 이는 파킨슨 씨 병의 진단에 유용한 진단적 정보를 제공한다. 본 연구에서는 iteration과 subset에 따른 영상의 변화를 관찰하고 적정한 iteration과 subset의 범위를 제안해 보고자 한다. 영상의 획득은 ACR 팬텀과 뇌 질환이 없는 정상인의 뇌 영상을 획득하였다. 정상인의 뇌영상은 F-18-FPCIT를 정맥주사 후 3시간째 획득하였으며, iteration과 subset의 조건을 5가지로 구분하여 영상을 재구성하였다. 영상의 분석은 동일한 위치에 같은 크기의 ROI를 그려 평균, 최대, 최소의 SUV를 측정하였고, 이를 바탕으로 표준편차, 변이계수를 계산하였다. 또한 팬텀영상에서는 각 조건별 열소와 냉소의 SUV를 비교하여 어떠한 조건에서 실제와 가장 비슷한 SUV ratio를 재현하는지 조사하였다. 위 실험에서 얻어진 값은 Spearman test를 통해 유의성을 유무를 판별하였다. 따라 SUV는 증가하였고 이러한 추세는 Spearman test에서 유의성을 나타내었다. 표준편차 역시 iteration, subset조건이 증가함에 따라 값의 증가를 보였다. 산출된 값들은 통계적으로 유의하였다. 팬텀 연구에서는 6 iteraions, 16 iterations 에서 실제와 가장 비슷한 SUV ratio를 재현하였다. 하지만 iteration, subset 조건별로 얻어진 SUV ratio들은 통계적으로 유의하지 않았다.