Application Of Respiration Simulation Phantom In PET-CT Non-small Lung Cancer Patient Positioning

The essay mainly aims to the affection of SUVmaxby analyzing its frequency,rang and tumor phatom size of respiration simulation motion phantom scanned withPET-CT. The neural net algorithm was used to build a Prediction Model to providePET imaging threshold to delineate motion virtual volume in radiation oncologydepartment.The respiration simulate phatom was designed and put into action ahead PET-CTscanning and the tumor phantom is replaceable.The respiration simulate phatom wasplaced on examining table in real patient examining way. All SUVmaxwere acquiredon PET images via PET View software and used to analyzed the significant factor indifferent frequency, rang and tumor phatom size by three way ANOVA of Sigmaplot12.3. The tumor phantom’s static volumes were delineated on TPS, and then margin0.5cm,1.0cm,1.5cm,2.0cm as tumor phantom motion virtual volumes respectively.The changed images threshold of PET in terms of figures which contain a form ofSUV%, until the hot region of PET image covered the length of virtual volume inmotion direction, recorded the SUV%value. The recorded SUV%, motionfrequencies, motion ranges and tumor phantom sizes were input. Matlab RBF neuralnet algorithm uses these values to build a Prediction Model to predict the imagingthreshold for motion virtual volume delineation.The research results showed that changing motion frequency has no significanceeffects to SUVmax,(p=0.249). The motion rangs and tumor phantom sizes havesignificant affection to SUVmax,(p<0.001). SUV%, frequencies and tumor phantomsizes were input model to check the model prediction threshold value accuracy. It waschecked for four times and showed that the values produced through PredictionModel had no significant difference with actual values. Therefore, the PredictionModel may help radiation oncologist in tumor target delineation.The research indicated that application SUVmaxto estimate whether the tumor ismalignant or not in clinical practice, those small size cancers and lymph nodes shouldbe regarded, because the SUVmaxof small size cancers and lymph nodes could be inaccurate. Consider the SUVmaxredused by tumors motion, estimating whether thesmall size cancers and lymph nodes are malignant or not, must be a lower SUVmaxvalue campare with usual boundary value. The prediction model can help radiationoncologists delineate tumor ITV on PET-CT image, but the tumor motion range mustnot exceeding1.5cm.