A Bayesian Optimization Approach for Attenuation Correction in SPECT Brain Imaging

TL;DR

This paper introduces a Bayesian Optimization method for attenuation correction in SPECT brain imaging that improves image quality without needing additional anatomical scans, leveraging HPC for computation.

Contribution

The novel Bayesian Optimization approach (BOAC) enables attenuation correction in SPECT brain imaging without relying on prior anatomical information from CT scans.

Findings

BOAC produces images with higher contrast.

BOAC reduces background artifacts.

BOAC outperforms non-corrected images in quality metrics.

Abstract

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.