  Attenuation compensation (AC) is a pre-requisite for reliable quantification
and beneficial for visual interpretation tasks in single-photon emission
computed tomography (SPECT). Typical AC methods require the availability of an
attenuation map obtained using a transmission scan, such as a CT scan. This has
several disadvantages such as increased radiation dose, higher costs, and
possible misalignment between SPECT and CT scans. Also, often a CT scan is
unavailable. In this context, we and others are showing that scattered photons
in SPECT contain information to estimate the attenuation distribution. To
exploit this observation, we propose a physics and learning-based method that
uses the SPECT emission data in the photopeak and scatter windows to perform
transmission-less AC in SPECT. The proposed method uses data acquired in the
scatter window to reconstruct an initial estimate of the attenuation map using
a physics-based approach. A convolutional neural network is then trained to
segment this initial estimate into different regions. Pre-defined attenuation
coefficients are assigned to these regions, yielding the reconstructed
attenuation map, which is then used to reconstruct the activity map using an
ordered subsets expectation maximization-based reconstruction approach. We
objectively evaluated the performance of this method using a highly realistic
simulation study conducted on the clinically relevant task of detecting
perfusion defects in myocardial perfusion SPECT. Our results showed no
statistically significant differences between the performance achieved using
the proposed method and that with the true attenuation maps. Visually, the
images reconstructed using the proposed method looked similar to those with the
true attenuation map. Overall, these results provide evidence of the capability
of the proposed method to perform transmission-less AC and motivate further
evaluation.
