Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise
This work addresses computational bottlenecks in transmission tomography for medical imaging, though it is incremental as it extends existing ARD methods to Poisson noise models.
The authors tackled image reconstruction in transmission tomography under Poisson noise by proposing a globally convergent alternating minimization algorithm with automatic relevance determination, which achieved performance comparable to standard methods without tuning parameters and outperformed prior ARD algorithms based on Gaussian noise models.
We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law. The algorithm promotes solutions that are sparse in the pixel/voxel-differences domain by introducing additional latent variables, one for each pixel/voxel, and then learning these variables from the data using a hierarchical Bayesian model. Importantly, the proposed AM algorithm is free of any tuning parameters with image quality comparable to standard penalized likelihood methods. Our algorithm exploits optimization transfer principles which reduce the problem into parallel 1D optimization tasks (one for each pixel/voxel), making the algorithm feasible for large-scale problems. This approach considerably reduces the computational bottleneck of ARD associated with the posterior variances. Positivity constraints inherent in transmission tomography problems are also enforced. We demonstrate the performance of the proposed algorithm for x-ray computed tomography using synthetic and real-world datasets. The algorithm is shown to have much better performance than prior ARD algorithms based on approximate Gaussian noise models, even for high photon flux.