Learning a microlocal prior for limited-angle tomography

TL;DR

This paper introduces a machine learning-based method for limited-angle tomography that estimates interfaces between regions, improving feature detection despite the ill-posed nature of the problem.

Contribution

It presents a novel approach combining geometry and machine learning to estimate wavefront sets and enhance feature delineation in limited-angle tomography reconstructions.

Findings

Improved interface detection in limited-angle tomography

Enhanced feature separation along the central projection direction

Effective use of complex wavelets and morphological operations

Abstract

Limited-angle tomography is a highly ill-posed linear inverse problem. It arises in many applications, such as digital breast tomosynthesis. Reconstructions from limited-angle data typically suffer from severe stretching of features along the central direction of projections, leading to poor separation between slices perpendicular to the central direction. A new method is introduced, based on machine learning and geometry, producing an estimate for interfaces between regions of different X-ray attenuation. The estimate can be presented on top of the reconstruction, indicating more reliably the true form and extent of features. The method uses directional edge detection, implemented using complex wavelets and enhanced with morphological operations. By using machine learning, the visible part of the wavefront set is first extracted and then extended to the full domain, filling in the parts of the wavefront set that would otherwise be hidden due to the lack of measurement directions.