2,300 research outputs found
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation
technique used in a variety of applications ranging from security to medicine.
The limited angle coverage in LACT is often a dominant source of severe
artifacts in the reconstructed images, making it a challenging inverse problem.
We present DOLCE, a new deep model-based framework for LACT that uses a
conditional diffusion model as an image prior. Diffusion models are a recent
class of deep generative models that are relatively easy to train due to their
implementation as image denoisers. DOLCE can form high-quality images from
severely under-sampled data by integrating data-consistency updates with the
sampling updates of a diffusion model, which is conditioned on the transformed
limited-angle data. We show through extensive experimentation on several
challenging real LACT datasets that, the same pre-trained DOLCE model achieves
the SOTA performance on drastically different types of images. Additionally, we
show that, unlike standard LACT reconstruction methods, DOLCE naturally enables
the quantification of the reconstruction uncertainty by generating multiple
samples consistent with the measured data.Comment: 29 pages, 21 figure
Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium
CT imaging works by reconstructing an object of interest from a collection of
projections. Traditional methods such as filtered-back projection (FBP) work on
projection images acquired around a fixed rotation axis. However, for some CT
problems, it is desirable to perform a joint reconstruction from projection
data acquired from multiple rotation axes.
In this paper, we present Multi-Pose Fusion, a novel algorithm that performs
a joint tomographic reconstruction from CT scans acquired from multiple poses
of a single object, where each pose has a distinct rotation axis. Our approach
uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play,
as a framework for integrating projection data from different poses. We apply
our method on simulated data and demonstrate that Multi-Pose Fusion can achieve
a better reconstruction result than single pose reconstruction.Comment: To appear in 58th Annual Allerton Conference on Communication,
Control, and Computin
- …