  Attenuation compensation (AC) is beneficial for visual interpretation tasks
in single-photon emission computed tomography (SPECT) myocardial perfusion
imaging (MPI). However, traditional AC methods require the availability of a
transmission scan, most often a CT scan. This approach has the disadvantages of
increased radiation dose, increased scanner cost, and the possibility of
inaccurate diagnosis in cases of misregistration between the SPECT and CT
images. Further, many SPECT systems do not include a CT component. To address
these issues, we developed a Scatter-window projection and deep Learning-based
AC (SLAC) method to perform AC without a separate transmission scan. To
investigate the clinical efficacy of this method, we then objectively evaluated
the performance of this method on the clinical task of detecting perfusion
defects on MPI in a retrospective study with anonymized clinical SPECT/CT
stress MPI images. The proposed method was compared with CT-based AC (CTAC) and
no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost
overlapping receiver operating characteristic (ROC) plot and a similar area
under the ROC (AUC) to the CTAC method on this task. These results demonstrate
the capability of the SLAC method for transmission-less AC in SPECT and
motivate further clinical evaluation.
