  Photon attenuation and scatter are the two main physical factors affecting
the diagnostic quality of SPECT in its applications in brain imaging. In this
work, we present a novel Bayesian Optimization approach for Attenuation
Correction (BOAC) in SPECT brain imaging. BOAC utilizes a prior model
parametrizing the head geometry and exploits High Performance Computing (HPC)
to reconstruct attenuation corrected images without requiring prior anatomical
information from complementary CT scans. BOAC is demonstrated in SPECT brain
imaging using noisy and attenuated sinograms, simulated from numerical
phantoms. The quality of the tomographic images obtained with the proposed
method are compared to those obtained without attenuation correction by
employing the appropriate image quality metrics. The quantitative results show
the capacity of BOAC to provide images exhibiting higher contrast and less
background artifacts as compared to the non-attenuation corrected MLEM images.
