A Bayesian Optimization Approach for Attenuation Correction in SPECT Brain Imaging
This addresses image quality issues in SPECT brain imaging for medical diagnostics, but it is incremental as it builds on existing optimization techniques.
The paper tackles photon attenuation and scatter in SPECT brain imaging by proposing a Bayesian Optimization approach for Attenuation Correction (BOAC), which improves image quality with higher contrast and fewer artifacts compared to non-corrected methods.
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.