3D Tensor Reconstruction in X-Ray Dark-Field Tomography: The First Phantom Result - Hu, Shiyang; Riess, Christian; Hornegger, Joachim; Fischer, Peter; Bayer, Florian; Weber, Thomas; Anton, Gisela; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)

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)

A 3-D Scattering Model for Orientation-dependent X-ray Dark-field Imaging - Hu, Shiyang; Maier, Andreas; Hornegger, Joachim; Bayer, Florian; Weber, Thomas; Anton, Gisela; Riess, Christian (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)

A learning-based material decomposition pipeline for multi-energy x-ray imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang-Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2019)
PurposeBenefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks.
CRIS-Team WoS Importer:2019-03-12

An Improved Extrapolation Scheme for Truncated CT Data Using 2D Fourier-Based Helgason-Ludwig Consistency Conditions - Xia, Yan; Berger, Martin; Bauer, Sebastian; Hu, Shiyang; Aichert, André; Maier, Andreas (International Journal of Biomedical Imaging, 2017)
We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency conditions that mathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier representation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallel-beam and fan-beam imaging geometry. The derivation result indicates that there is a zero energy region forming a double-wedge shape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature. The major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform (FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the parameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be used to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data. Results show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method.
UnivIS-Import:2018-09-05:Pub.2017.tech.IMMD.IMMD5.animpr_2
Keywords: Truncation; CT; Helgason-Ludwig; Fourier-Consistency; Consistency Conditions

Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (Medical Physics, 2020)
Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.
Keywords: Multi-organ; DECT; FCN; U-Net; deep learning

Automatic Multi-Organ Segmentation in Dual Energy CT using 3D Fully Convolutional Network - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (MIDL, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_98

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)

Reconstruction of scalar and vectorial components in X-ray dark-field tomography - Bayer, Florian; Hu, Shiyang; Maier, Andreas; Weber, Thomas; Anton, Gisela; Michel, Thilo; Riess, Christian (Proceedings of the National Academy of Sciences of the United States of America, 2014)
Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomography, which models the orientation- dependent signal as an additional observable from a standard tomographic scan. In detail, we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the metho
UnivIS-Import:2015-04-14:Pub.2014.nat.dphy.PI.LEPDF.recons
Keywords: Anisotropic scattering; Grating interferometer; Microstructure orientation; X-ray phase contrast

Towards quantification of kidney stones using X-ray dark-field tomography - Hu, Shiyang; Yang, F.; Kaufmann, R.; Griffa, M.; Anton, Gisela; Maier, Andreas; Riess, Christian (14th IEEE International Symposium on Biomedical Imaging, 2017)

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)