Radiation image reconstruction and uncertainty quantification using a Gaussian process prior.

We propose a complete framework for Bayesian image reconstruction and uncertainty quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum likelihood expectation maximization (ML-EM) image reconstruction algorithm. The prior distribution is constructed with a zero-mean Gaussian process (GP) with a choice of a covariance function, and a link function is used to map the Gaussian process to an image. Unlike many other maximum a posteriori approaches, our method offers highly interpretable hyperparamters that are selected automatically with the empirical Bayes method.

Coded aperture and Compton imaging for the development of Ac-based radiopharmaceuticals.

Background: Targeted alpha-particle therapy (TAT) has great promise as a cancer treatment. Arguably the most promising TAT radionuclide that has been proposed is Ac. The development of Ac-based radiopharmaceuticals has been hampered due to the lack of effective means to study the daughter redistribution of these agents in small animals at the preclinical stage.

Free-moving Quantitative Gamma-ray Imaging.

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF).

Unbiased and biased estimators in coded aperture imaging for far field standoff detection at low count rates.

In general, the reconstructed image in coded aperture imaging is affected by the source configuration. Fenimore's balanced convolution method in conjunction with the uniformly redundant array can remove the interference due to the source configuration. As an extension of Fenimore's balanced convolution method, we present general conditions for designing an unbiased mean estimator for a far-field coded aperture imaging system with a random binary mask.