Biomedical Imaging as an Inverse Problem
P. del Aguila Pla
European Molecular Imaging Meeting (EMIM'20), Virtual, August 24-28, 2020.
Learning Objective To obtain a structured understanding of the field of biomedical imaging from the perspective of inverse problems. Specifically, to form an understanding of
- the basic building blocks of any imaging problem,
- the taxonomy of image reconstruction algorithms in 1st (classical), 2nd (sparsity-based) and 3rd (deep learning) generation techniques, together with their respective foundations, advantages, and disadvantages,
- how to prototype reconstruction techniques using GlobalBioIm, a convenient MATLAB library to accelerate development in the field.
Content The impact of biomedical imaging has increased steadily over the past four decades. Part of this is due to the improvement of reconstruction methods, which have provided increasing image quality and resolution. In this tutorial, we present a well-structured view of the field of biomedical image reconstruction based on the few basic building blocks of any imaging system and the three generations of available methods, i.e., classical, sparsity-based, and deep neural networks techniques. For any given imaging problem, attendants will learn to quickly identify its fundamental building blocks from an inverse-problems perspective and propose adequate methodology. Furthermore, we will introduce how to obtain fast prototypes of reconstruction techniques using GlobalBioIm, an efficient MATLAB library tailored to image reconstruction problems.
@INPROCEEDINGS(http://bigwww.epfl.ch/publications/delaguilapla2003.html,
AUTHOR="del Aguila Pla, P.",
TITLE="Biomedical Imaging as an Inverse Problem",
BOOKTITLE="European Molecular Imaging Meeting ({EMIM'20})",
YEAR="2020",
editor="",
volume="",
series="",
pages="",
address="Virtual",
month="August 24-28,",
organization="",
publisher="",
note="")