Inverse Problems and Applications (IPA)

IPA group photo April 2024

In general, inverse problems are concerned with (1) reconstructing signals from observations and/or (2) controlling systems to a desired effect. Inverse problems occur in a variety of domains and applications, ranging from tomographic reconstruction to particle physics to machine learning.  The IPA group is dedicated to identifying and solving such inverse problems with a strong focus on radiological applications:
•    Tomographic reconstruction for different modalities under non-optimal conditions
•    Rigid and non-rigid motion estimation and correction
•    Image quality
•    Blood flow analysis
•    Ionizing radiation dose estimation and optimization


Ait4Surgery: Automatic Intraoperative Tracking for Workflow and Dose Monitoring in X-Ray-based Minimally Invasive Surgeries

The goal of this project is the investigation of multimodal methods for the evaluation of interventional workflows in the operation room. This topic will be researched in an international project context with partners in Germany and in Brazil (UNISINOS in Porto Alegre). Methods will be developed to analyze the processes in an OR based on signals from body-worn sensors, cameras and other modalities like X-ray images recorded during the surgeries. For data analysis, techniques from the field of…

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SmartCT: SmartCT - Erforschung und Entwicklung von Methoden der Künstlichen Intelligenz für ein autonomes Roboter-CT System zur 3D-Digitalisierung beliebiger Objekte

In Vorhaben SmartCT sollen KI-Methoden entwickelt und angewendet werden, die Roboter-CT Systemen ermöglicht, selbstständig, also autonom, die äußeren und inneren Strukturen beliebiger Objekte zu digitalisieren. Diese so erzeugten Daten stellen die Basis von neuartigen, innovativen und datengetriebenen Geschäftsmodellen in vielen Bereichen wie Produktentwicklung, Produktion, Handel, Instandhaltung, Sicherheit und Recycling dar.
Roboter-CT Systeme können beliebige Objekte (Fahrzeugkomponenten, Flugz…

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KI4D4E: An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

Synchrotron tomography, using brilliant X-rays, allows for high-resolution, artifact-free imaging. Unlike traditional lab CT, this technique can also conduct rapid 4D tomography experiments, leading to vast amounts of data. The KI4D4E project aims to develop AI methods to process these large data sets. The focus is on artifact reduction, segmentation, and visualization of large 4D data sets. The methods are intended to be applicable to data from both photon and neutron sources.

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RTG 1773: Heterogeneous Image Systems, Project C1

Motion Correction for Weight-Bearing C-arm CT of Knees

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IMQSDL: Improving multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information

This project is about improving image quality in certain SPECT applications.

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Participating Scientists

Colloquium time table


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