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Attenuation compensation (AC) is beneficial for visual interpretation tasksin single-photon emission computed tomography (SPECT) myocardial perfusionimaging (MPI). However, traditional AC methods require the availability of atransmission scan, most often a CT scan. This approach has the disadvantages ofincreased radiation dose, increased scanner cost, and the possibility ofinaccurate diagnosis in cases of misregistration between the SPECT and CTimages. Further, many SPECT systems do not include a CT component. To addressthese issues, we developed a Scatter-window projection and deep Learning-basedAC (SLAC) method to perform AC without a separate transmission scan. Toinvestigate the clinical efficacy of this method, we then objectively evaluatedthe performance of this method on the clinical task of detecting perfusiondefects on MPI in a retrospective study with anonymized clinical SPECT/CTstress MPI images. The proposed method was compared with CT-based AC (CTAC) andno-AC (NAC) methods. Our results showed that the SLAC method yielded an almostoverlapping receiver operating characteristic (ROC) plot and a similar areaunder the ROC (AUC) to the CTAC method on this task. These results demonstratethe capability of the SLAC method for transmission-less AC in SPECT andmotivate further clinical evaluation.

Development and task-based evaluation of a scatter-window projection and  deep learning-based transmission-less attenuation compensation method for  myocardial perfusion SPECT