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Jennifer Maier, M. Sc.

  • Organization: Department of Computer Science
  • Working group: Chair of Computer Science 5 (Pattern Recognition)
  • Phone number: +49 9131 85 27891
  • Fax number: +49 9131 85 27270
  • Email: jennifer.maier@fau.de
  • Website:
  • Address:
    Martensstr. 3
    91058 Erlangen
    Room 09.157

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Since 05/2017 Researcher and Ph.D. student in the Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
Since 01/2016 Researcher and Ph.D. student in the Medical Image Reconstruction Group, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
05/2015 – 10/2015 Master thesis: Research stay at the Laboratory of Movement Analysis and Measurement, École polytechnique fédérale de Lausanne
10/2013 – 12/2015 M.Sc. in Medical Engineering at the Friedrich-Alexander-Universität Erlangen-Nürnberg
10/2010 – 03/2014 B. Sc in Medical Engineering at the Friedrich-Alexander-Universität Erlangen-Nürnberg
09/2001 – 06/2010 Secondary school at Gymnasium Naila

2019

2018

2017

2016

2013

My PhD project focuses on the correction of involuntary motion during C-arm CT scans using biomechanical modeling.

2019

  • PPP Brasilien 2019
    (Third Party Funds Single)
    Term: 1. January 2019 - 31. December 2020
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)

2018

  • Automatic Intraoperative Tracking for Workflow and Dose Monitoring in X-Ray-based Minimally Invasive Surgeries
    (Third Party Funds Single)
    Term: 1. June 2018 - 31. May 2021
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
    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 computer vision, machine learning and pattern recognition will be applied. The system will be integrated in a way that body-worn sensors developed by Portabiles as well as angiography systems produced by Siemens Healthcare can be included alongside.

2012

  • RTG 1773: Heterogeneous Image Systems, Project C1
    (Third Party Funds Group – Sub project)
    Overall project: GRK 1773: Heterogene Bildsysteme
    Term: 1. October 2012 - 31. March 2017
    Funding source: DFG / Graduiertenkolleg (GRK)
    Especially in aging populations, Osteoarthritis (OA) is one of the leading causes for disability and functional decline of the body. Yet, the causes and progression of OA, particularly in the early stages, remain poorly understood. Current OA imaging measures require long scan times and are logistically challenging. Furthermore they are often insensitive to early changes of the tissue.

    The overarching goal of this project is the development of a novel computed tomography imaging system allowing for an analysis of the knee cartilage and menisci under weight-bearing conditions. The articular cartilage deformation under different weight-bearing conditions reveals information about abnormal motion patterns, which can be an early indicator for arthritis. This can help to detect the medical condition at an early stage.

    To allow for a scan in standing or squatting position, we opted for a C-arm CT device that can be almost arbitrarily positioned in space. The standard application area for C-arm CT is in the interventional suite, where it usually acquires images using a vertical trajectory around the patient. For the recording of the knees in this project, a horizontal trajectory has been developed.

    Scanning in standing or squatting position makes an analysis of the knee joint under weight-bearing conditions possible. However, it will also lead to involuntary motion of the knees during the scan. The motion will result in artifacts in the reconstruction that reduce the diagnostic image quality. Therefore, the goal of this project is to estimate the patient motion during the scan to reduce these artifacts. One approach is to compute the motion field of the knee using surface cameras and use the result for motion correction. Another possible approach is the design and evaluation of a biomechanical model of the knee using inertial sensors to compensate for movement.

    After the correction of the motion artifacts, the reconstructed volume is used for the segmentation and quantitative analysis of the knee joint tissue. This will give information about the risk or the progression of an arthrosis disease.