Virtual myocardial PET generated from deep learning of SPECT and PET datasets: potential for attenuation correction in CT-less SPECT

Abstract

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.