My research interests include a broad variety of topics, ranging from medical image analysis and signal processing, machine learning and artificial intelligence, which I mainly focused on during my Ph.D. research. As a member of the Digital Athlete project of the Wu Tsai Performance Allience, I am now pursuing research to investigate how we can use wearable sensors, machine learning and biomechanical simulations to improve athlete performance, prevent injuries and support rehabilitation after injury.
I completed my Bachelor of Science and Master of Science degrees in medical engineering from Friedrich-Alexander-University Erlangen-Nuernberg (FAU). In 2015, I worked on my master’s thesis under the supervision of Prof. Kamiar Aminian during a research stay in the Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne (EPFL), supported by a DAAD Scholarship. Afterwards, I pursued my Ph.D. at FAU in the Pattern Recognition Laboratory under the supervision of Prof. Andreas Maier and in the Machine Learning and Data Analytics Lab under the supervision of Prof. Bjoern Eskofier. I worked on projects in collaboration with Stanford University and the Universidade do Vale do Rio dos Sinos (UNISINOS) and conducted several short-term research stays at the partner universities. After finishing my Ph.D. in 2021, I joined Stanford University as a postdoctoral scholar advised by Prof. Ellen Kuhl.
Ellen Kuhl, Postdoctoral Faculty Sponsor
Rigid and Non-rigid Motion Compensation in Weight-bearing CBCT of the Knee using Simulated Inertial Measurements.
IEEE transactions on bio-medical engineering
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation.We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications.All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 105 which is currently only achieved by specialized hardware not robust enough for the application.Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application.The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.
View details for DOI 10.1109/TBME.2021.3123673
View details for PubMedID 34714730
3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT
2021; 9: 71821-71831
Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject's knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.
View details for DOI 10.1109/ACCESS.2021.3079368
View details for Web of Science ID 000652048500001
View details for PubMedID 34141516
View details for PubMedCentralID PMC8208599
Feasibility of Motion Compensation using Inertial Measurement in C-arm CT
View details for Web of Science ID 000601256000197
Comparison of Different Approaches for Measuring Tibial Cartilage Thickness
JOURNAL OF INTEGRATIVE BIOINFORMATICS
2017; 14 (2)
Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject's knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.
View details for DOI 10.1515/jib-2017-0015
View details for Web of Science ID 000406931200005
View details for PubMedID 28753537
Object Removal in Gradient Domain of Cone-Beam CT Projections
View details for Web of Science ID 000432419500102
Analog Non-Linear Transformation-Based Tone Mapping for Image Enhancement in C-arm CT
View details for Web of Science ID 000432419500112