EF3 – Model-Based Imaging

Project

EF3-5

Direct Reconstruction of Biophysical Parameters Using Dictionary Learning and Robust Regularization

Project Heads

Michael Hintermüller, Tobias Schäffter

Project Members

Staff: Gouzhi Dong (HU & WIAS) 

Associate: Kostas Papafitsoros (WIAS)

Project Duration

01.01.2019 – 31.12.2021

Located at

HU Berlin

Description

Model-based imaging requires knowledge on some physical models. This is often challenge in real applications, as the physical models might be not directly available, but hidden in different kinds of data, either experimentally or numerically. This project aims to develop the concept of model-based imaging methods. The idea is integrating physical models, either dictionary based or learning-informed, into the image reconstruction process. These new mathematical objects then need to be analytically and numerically investigated, including also robust numerical solvers.

Exemplary case study focuses on magnetic resonance imaging. An integrated physics-based models are proposed for quantitatively estimating the tissue parameters, for instance, the T1/T2 relaxation time, the proton spin density. Approaches of using learning-informed physics or dictionary-based physics are under investigation.

Imaging techniques using bilevel optimization schemes and PDE tools have been studied in different context.

Project Webpages

Selected Publications

G. Dong, M. HintermüllerK. PapafitsorosQuantitative magnetic resonance imaging: From fingerprinting to integrated physics-based models, SIAM Journal on Imaging Sciences, 2 (2019), pp. 927–971, DOI 10.1137/18M1222211 .

M. HintermüllerK. PapafitsorosChapter 11: Generating structured nonsmooth priors and associated primal-dual methods, in: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, R. Kimmel, X.-Ch. Tai, eds., 20 of Handbook of Numerical Analysis, Elsevier, 2019, pp. 437–502, (Chapter Published), DOI 10.1016/bs.hna.2019.08.001.

G. Dong, M. Hintermüller, Y. Zhang, A class of geometric second order quasi-linear hyperbolic PDEs and their application in imaging science. WIAS Preprint No. 2591, (2019).

M. HintermüllerK. Papafitsoros, C. N. Rautenberg, H. Sun, Dualization and automatic distributed parameter selection of total generalized variation via bilevel optimization, WIAS Preprint No. 2689, (2020).

 

Selected Pictures

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