2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Roser, Philipp; Felsner, Lina; Preuhs, Alexander; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

A 3-D Projection Model for X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Maier, Andreas; Bopp, Johannes; Ludwig, Veronika; Anton, Gisela; Riess, Christian (Scientific Reports, 2019)

A 3-D Projection Model for X-ray Dark-field Imaging - Hu, Shiyang; Felsner, Lina; Maier, Andreas; Ludwig, Veronika; Anton, Gisela; Riess, Christian (2018)

Advanced 3-D Reconstruction of Talbot-Lau Data - Felsner, Lina (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
X-ray imaging is a standard technique for examinations and interventions in medicine. Based on X-rays, Phase-Contrast Imaging (PCI) can provide an excellent soft-tissue contrast. The most promising setup for measuring phase-contrast in a medical context is the Talbot-Lau Interferometer (TLI), where specialized gratings are integrated between a regular medical X-ray tube and X-ray detector. The TLI allows to measure the classical X-ray attenuation in combination with the phase-contrast and dark-field signal. All three signals provide unique information and can in combination with each other enable an improved medical diagnostic. Acquiring volumetric scans with a TLI enables multi-modal imaging in which the reconstructed volumes are inherently registered. While medical procedures could greatly benefit from such a system, current implementations of such a setup in a medical environment are prevented by hardware- and software-based problems. In this work, we propose algorithms for solving algorithmic challenges towards the reconstruction of phase-contrast and dark-field signals with clinical compatibility. In the first part, advanced reconstruction techniques for the differential phase-contrast signal from a TLI are investigated. Here, the focus is on improving the reconstruction of the phase contrast data from a TLI for large objects. Current Talbot-Lau systems suffer from a small field of view, which leads to truncation for large objects, such as humans. To avoid artifacts in the reconstruction, we propose a method for phase-sensitive Region-of-Interest (ROI) Computed Tomography (CT). The proposal involves a special setup, where a large detector allows measuring complete non-truncated attenuation images, while additional gratings allow for phase-contrast imaging of a ROI. A corresponding correction algorithm is proposed to correct the truncation of the phase-contrast images. With experiments, we show that the proposed method can enable a clinically practical TLI implementation. Unfortunately, the signal strength of the phase measurement depends on the position of the object in setup. This leads to a change in the projection model and subsequent artifacts in the reconstruction. The artifacts prevent the evaluation of quantitative values and, therefore, the use of the data in a medical environment. In particular, this challenge introduces artifacts in the reconstruction of large objects. We propose two correction algorithms to alleviate this issue. The excellent performance of both algorithms in the experiments indicate the possibility for phase-sensitive CT for large objects. In the second part we consider the reconstruction of the dark-field signal. The scattering of the dark-field signal is high-dimensional, which leads to an orientation-dependent signal strength and prevents the use of Filtered Back-Projection (FBP) algorithms. Current projection models describe the signal formation only in two dimensions. Therefore, in order to reconstruct the dark-field, complex sampling trajectories must be used, which are clinically impractical. To tackle this challenge, we propose a general 3-D dark-field projection model, which enables to model the signal formation directly in 3-D. The general description facilitates a high degree of flexibility and enables modeling complex trajectories such as a helix. Our experimental evaluation shows a good agreement with wavefront simulations and real measurements. The projection model also allows a reconstruction of the dark-field signal with a true 3-D dark-field reconstruction algorithm. Experiments show the feasibility of a helical reconstruction and investigated the stability of the algorithm with respect to different influences. Overall, these promising first results indicate that a reconstruction of directed structures with a spiral trajectory is feasible.<br

Comparison of methods for sensitivity correction in Talbot–Lau computed tomography - Felsner, Lina; Roser, Philipp; Maier, Andreas; Riess, Christian (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: In Talbot–Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object. Methods: In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp–Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods. Results: All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior. Conclusion: The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.
CRIS-Team Scopus Importer:2021-09-17
Keywords: computed tomography; known operator learning; sensitivity correction; Talbot–Lau interferometer; X-ray phase contrast imaging

Epipolar consistency conditions for motion correction in weight-bearing imaging - Bier, Bastian; Aichert, André; Felsner, Lina; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen (Bildverarbeitung für die Medizin 2017), 2017)

Helical Dark-field Fiber Reconstruction - Felsner, Lina; Syben-Leisner, Christopher; Maier, Andreas; Riess, Christian (International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)

Learning the Inverse Weighted Radon Transform - Roser, Philipp; Felsner, Lina; Maier, Andreas; Riess, Christian (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Motion Estimation in Rotational Angiography with alpha–Expansion Moves - Felsner, Lina; Aichert, André; Unberath, Mathias; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference, 2017)

On the Characteristics of Helical 3-D X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Ludwig, Veronika; Anton, Gisela; Maier, Andreas; Rieß, Christian (Bildverarbeitung für die Medizin (BVM 2019), 2019)

Phase-Sensitive Region-of-Interest Computed Tomography - Felsner, Lina; Berger, Martin; Käppler, Sebastian; Bopp, Johannes; Ludwig, Veronika; Weber, Thomas; Pelzer, Georg; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (21st International Conference on Medical Image Computing and Computer Assisted Intervention, 2018)

Reconstruction of Voxels with Position- and Angle-Dependent Weightings - Felsner, Lina; Würfl, Tobias; Syben-Leisner, Christopher; Roser, Philipp; Preuhs, Alexander; Maier, Andreas; Riess, Christian (The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Truncation Correction for X-ray Phase-Contrast Region-of-Interest Tomography - Felsner, Lina; Käppler, Sebastian; Maier, Andreas; Riess, Christian (IEEE Transactions on Computational Imaging, 2020)

X-ray Phase Contrast: Research on a Future Imaging Modality - Bopp, Johannes; Felsner, Lina; Hu, Shiyang; Käppler, Sebastian; Riess, Christian (Springer, 2018)
UnivIS-Import:2019-03-25:Pub.2018.tech.IMMD.IMMD5.xrayph_1

X-ray Phase Contrast: Research on a Future Imaging Modality - Bopp, Johannes; Felsner, Lina; Hu, Shiyang; Käppler, Sebastian; Riess, Christian (Medical Imaging Systems - An Introductory Guide, 2018)

X-ray Scatter Estimation Using Deep Splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Felsner, Lina; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics’ constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal’s frequency characteristics.
CRIS-Team Scopus Importer:2021-05-07
Keywords: Approximation; B-spline; Detectors; Estimation; Neural network; Neural networks; Photonics; Splines (mathematics); Thorax; X-ray imaging; X-ray scatter