Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods

Abstract

In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the existing approaches and obtain quantitative results after training them with different amounts of data. We find that the learned primal-dual has an outstanding performance in terms of reconstruction quality and data efficiency. However, in general, end-to-end learned methods have two issues: a) lack of classical guarantees in inverse problems and b) lack of generalization when not trained with enough data. To overcome these issues, we bring in the deep image prior approach in combination with classical regularization. The proposed methods improve the state-of-the-art results in the low data-regime.

References in corpus (13)

• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Image reconstruction by domain transform manifold learning
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
• On instabilities of deep learning in image reconstruction - Does AI come at a cost?
• Learned Primal-dual Reconstruction
• Solving ill-posed inverse problems using iterative deep neural networks
• CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Compressed Sensing with Deep Image Prior and Learned Regularization
• Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
• Deep Null Space Learning for Inverse Problems: Convergence Analysis and Rates
• Regularization by architecture: A deep prior approach for inverse problems

Cited by in corpus (21)

• The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
• Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
• Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study
• Benchmarking Deep Learning-Based Low-Dose CT Image Denoising Algorithms
• An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior
• PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
• Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
• An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
• Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications
• SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations
• Unsupervised Knowledge-Transfer for Learned Image Reconstruction
• Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
• WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
• Regularization Theory of the Analytic Deep Prior Approach
• Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
• A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
• Deformable image registration with deep network priors: a study on longitudinal PET images
• Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI
• Imaging based on Compton scattering: model-uncertainty and data-driven reconstruction methods
• Deep learning based dictionary learning and tomographic image reconstruction
• Fast Inexact Bilevel Optimization for Analytical Deep Image Priors