Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Abstract

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most imaging systems necessarily use an image-reconstruction algorithm to create an image; the design of these algorithms has been a topic of research since at least the 1960's. In the last few years, machine learning-based approaches have shown impressive performance on image reconstruction problems, triggering a wave of enthusiasm and creativity around the paradigm of learning. Our goal is to unify this body of research, identifying common principles and reusable building blocks across decades and among diverse imaging modalities. We first describe system modeling, emphasizing how a few building blocks can be used to describe a broad range of imaging modalities. We then discuss reconstruction algorithms, grouping them into three broad generations. The first are the classical direct methods, including Tikhonov regularization; the second are the variational methods based on sparsity and the theory of compressive sensing; and the third are the learning-based (also called data-driven) methods, especially those using deep convolutional neural networks. There are strong links between these generations: classical (first-generation) methods appear as modules inside the latter two, and the former two are used to inspire new designs for learning-based (third-generation) methods. As a result, a solid understanding of all of three generations is necessary for the design of state-of-the-art algorithms.

References in corpus (16)

• Intriguing properties of neural networks
• 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?
• Understanding Deep Convolutional Networks
• Deep Learning Microscopy
• Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
• Denoising Prior Driven Deep Neural Network for Image Restoration
• Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
• Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
• An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
• Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
• Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling
• Learning Convex Regularizers for Optimal Bayesian Denoising

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• Fast Three-dimensional Opto-acoustic Simulation for Linear Array with Rectangular Elements
• Score-based Diffusion Models for Bayesian Image Reconstruction
• HPC-based Solvers of Minimisation Problems for Signal Processing
• Survey of Deep Learning Methods for Inverse Problems
• A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods