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Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

M.T. McCann, M. Unser

Foundations and Trends® in Signal Processing, vol. 13, no. 3, pp. 280-359, December 3, 2019.



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 three generations is necessary for the design of state-of-the-art algorithms.

M.T. McCann, M. Unser, "Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks," arXiv:1901.03565 [eess.IV]


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TITLE="Biomedical Image Reconstruction: {F}rom the Foundations to Deep
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