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Background : Myocardial blood flow quantification (MBF) is one of the distinctive features for cardiac positron emission tomography. The MBF calculation is mostly obtained by estimating the input function from the time activity curve in dynamic scan. However, there is a substantial risk of count-loss because the high radioactivity pass through the left ventricular (LV) cavity within a short period. We aimed to determine the optimal intraventricular activity using the noise equivalent count rate (NECR) analysis with simplified phantom model. Methods : Positron emission tomography computed tomography scanner with LYSO crystal and time of flight was used for phantom study. 150 MBq/mL of 13 N was filled in 10 mL of syringe, placed in neck phantom to imitate end-systolic small LV. 3D list-mode acquisition was repeatedly performed along radioactive decay. Net true and random count rate were calculated and compared to the theoretical activity in the syringe. NECR curve analysis was used to determine the optimal radioactive concentration. Result : The attenuation curves showed good correlation to the theoretical activity between 20 to 370, and 370 to 740 MBq (r 2 =1.0 ± 0.0001, p<0.0001; r 2 =0.99 ± 0.0001, p<0.0001 for 20 to 370, and 370 to 740, respectively), while did not over 740 MBq (p=0.62). NECR analysis revealed that the peak rate was at 2.9 Mcps, there at the true counts were significantly suppressed. The optimal radioactive concentration was determined as 36 MBq/mL. Conclusion : Simulative analysis for high-dose of 13 N using the phantom imitating small LV confirmed that the risk of count-loss was increased. The result can be useful information in assessing the feasibility of MBF quantification in clinical routine.

Objective Deep-learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computerized tomography (SPECT). This study evaluated the improvement in visual ischemia scoring accuracy to investigate the performance of virtual positron emission tomography (vPET) generated by a deep-learning model. Methods This retrospective study included the patient-to-patient stress, resting SPECT, and PET datasets of 54 patients. The vPET generation model was trained and validated using 34 cases with over 1200 image pairs using an image-to-image translation network. The SPECT, PET, and vPET images from another 20 cases were blindly scored in the stress and resting states. Results The SPECT rest scores at septal and inferior walls (segments #2 and #15) were significantly higher than those of PET. However, no significant differences were observed between the vPET and PET scores. Diagnostic performance of SPECT for detecting PET defect areas were improved with the use of vPET visual scores. Conclusions vPET, a new approach for improving ischemic visual score at rest in the well-known attenuated region on SPECT, can be applied as a clinical support tool that provides powerful auxiliary information for myocardial blood flow diagnosis since standalone SPECT is used worldwide.