Computed Tomography Reconstruction using Generative Energy-Based Priors

03/23/2022
by   Martin Zach, et al.
0

In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which manifests in noisy or incomplete measurements. Thus, the need for robust reconstruction algorithms arises. In this work, we learn a parametric regularizer with a global receptive field by maximizing it's likelihood on reference CT data. Due to this unsupervised learning strategy, our trained regularizer truly represents higher-level domain statistics, which we empirically demonstrate by synthesizing CT images. Moreover, this regularizer can easily be applied to different CT reconstruction problems by embedding it in a variational framework, which increases flexibility and interpretability compared to feed-forward learning-based approaches. In addition, the accompanying probabilistic perspective enables experts to explore the full posterior distribution and may quantify uncertainty of the reconstruction approach. We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin.

READ FULL TEXT

page 1

page 5

page 6

research
04/08/2019

Deep Learning Based Computed Tomography Whys and Wherefores

This is an article about the Computed Tomography (CT) and how Deep Learn...
research
05/24/2022

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization

Learning neural networks using only a small amount of data is an importa...
research
11/02/2022

Unsupervised denoising for sparse multi-spectral computed tomography

Multi-energy computed tomography (CT) with photon counting detectors (PC...
research
09/27/2020

Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model

Dose reduction in computed tomography (CT) is essential for decreasing r...
research
06/27/2023

Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction

Emerging neural reconstruction techniques based on tomography (e.g., NeR...
research
11/03/2022

Active CT Reconstruction with a Learned Sampling Policy

Computed tomography (CT) is a widely-used imaging technology that assist...
research
03/28/2023

DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding

Reducing the radiation dose in computed tomography (CT) is important to ...

Please sign up or login with your details

Forgot password? Click here to reset