Two-Dimensional Unknown View Tomography from Unknown Angle Distributions
This addresses a practical limitation in tomography for fields like structural biology and medical imaging, where angle distributions are typically unknown, but the work appears incremental as it builds on existing 2D UVT literature by removing the assumption of known distributions.
The study tackled the problem of 2D unknown view tomography (UVT) without prior knowledge of the viewing angle distribution, which is common in applications like cryo-electron microscopy, by developing an optimization method that jointly estimates the angle distribution and the 2D structure. The result was an algorithm evaluated under noisy conditions, using techniques like PCA-based denoising and Graph Laplacian Tomography, and compared to baselines, though no concrete performance numbers were provided in the abstract.
This study presents a technique for 2D tomography under unknown viewing angles when the distribution of the viewing angles is also unknown. Unknown view tomography (UVT) is a problem encountered in cryo-electron microscopy and in the geometric calibration of CT systems. There exists a moderate-sized literature on the 2D UVT problem, but most existing 2D UVT algorithms assume knowledge of the angle distribution which is not available usually. Our proposed methodology formulates the problem as an optimization task based on cross-validation error, to estimate the angle distribution jointly with the underlying 2D structure in an alternating fashion. We explore the algorithm's capabilities for the case of two probability distribution models: a semi-parametric mixture of von Mises densities and a probability mass function model. We evaluate our algorithm's performance under noisy projections using a PCA-based denoising technique and Graph Laplacian Tomography (GLT) driven by order statistics of the estimated distribution, to ensure near-perfect ordering, and compare our algorithm to intuitive baselines.