FDG-PET画像における体幹部スコアリングシステムの開発と経時差分像技術への応用

摘要

本研究は，FDG脚PET画像におけるSUV（Standardized Uptake Value）を利用して，体幹部領域のスコア画像を作成し，また，経時的変化を可視化する手法を開発した．ここでは，197例の正常例から統計的な正常モデルを構築した.正常モデルは，人体の位置合わせを行って実現した．得られた信頼区間からの偏差を画像化することで，標準的な絶代謝からの異常度を定量的に提示した．また上位置合わせ技術と偏差の計算手法を用いて，経時的な変化のある症例についてもその異常部位を可視化する手法を開発した．%The purpose of this work was to develop an automated method to calculate the score of SUV for torso region on FDG-PET scans. The three-dimensional distributions for the mean and the standard deviation (SD) values of SUV, say a model here, were determined based on our normal patients' database. Unknown scans (patients' images) were then deformed to fit the above model. The modeling methods employed was similar to the Statistical Parametric Mapping (SPM) approach using correction technique of Euler characteristic and Resel (Resolution element). We employed 197 normal cases (male: 143, female: 54) to assemble the normal metabolism distribution of FDG. The physique was registered to each other in a rectangular parallelepiped shape using affine transformation and Thin-Plate-Spline (TPS) technique. The regions of torso, bladder, liver, and body surface were determined based on automated procedures. Seventy-three abnormal spots were used to estimate the effectiveness of the scoring methods. As a result, the score images correctly represented that the scores for normal cases were between zero to plus/minus 2 SD. Most of the scores of abnormal spots associated with cancer were higher than the upper bound of the SUV interval of the above normal organs.