105 results on '"Torsten Hopp"'
Search Results
2. Image-based registration between full x-ray and spot mammograms: analysis of registration accuracy in subgroups.
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Sarah Said, Paola Clauser, Nicole V. Ruiter, Pascal A. Baltzer, and Torsten Hopp
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- 2023
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3. Model-data-driven image reconstruction with neural networks for ultrasound computed tomography breast imaging.
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Yuling Fan, Hongjian Wang, Hartmut Gemmeke, Torsten Hopp, and Juergen Hesser
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- 2022
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4. Memory-Efficient Neural Network For Non-Linear Ultrasound Computed Tomography Reconstruction.
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Yuling Fan, Hongjian Wang, Hartmut Gemmeke, Torsten Hopp, Koen van Dongen, and Jürgen Hesser
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- 2021
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5. X-ray Synthesis Based on Triangular Mesh Models Using GPU-Accelerated Ray Tracing for Multi-modal Breast Image Registration.
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J. Maul, Sarah Said, Nicole V. Ruiter, and Torsten Hopp
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- 2021
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6. Segmentation of the mouse skull for MRI guided transcranial focused ultrasound therapy planning.
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Torsten Hopp, Luca Springer, Carl Gross, Saskia Grudzenski-Theis, Franziska Mathis-Ullrich, and Nicole V. Ruiter
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- 2022
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7. Image based registration between full x-ray and spot mammograms for x-ray guided stereotactic breast biopsy.
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Sarah Said, Paola Clauser, Nicole V. Ruiter, Pascal A. Baltzer, and Torsten Hopp
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- 2022
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- View/download PDF
8. Image registration between MRI and spot mammograms for X-ray guided stereotactic breast biopsy: preliminary results.
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Sarah Said, Paola Clauser, Nicole V. Ruiter, Pascal A. Baltzer, and Torsten Hopp
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- 2021
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- View/download PDF
9. GPU Acceleration of Wave Based Transmission Tomography.
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Hongjian Wang, Thai Hoa Huynh, Hartmut Gemmeke, Torsten Hopp, and Jürgen Hesser
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- 2019
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10. Inspiration from VR Gaming Technology: Deep Immersion and Realistic Interaction for Scientific Visualization.
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Till Bergmann, Matthias Norbert Balzer, Torsten Hopp, Thomas van de Kamp, Andreas Kopmann, Nicholas Tan Jerome, and Michael Zapf
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- 2017
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11. Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography.
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Torsten Hopp, P. Cotic Smole, and Nicole V. Ruiter
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- 2017
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12. Automated Multimodal Computer Aided Detection Based on a 3D-2D Image Registration.
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Torsten Hopp, Bishal Neupane, and Nicole V. Ruiter
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- 2016
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13. First imaging results with the new generation of the KIT 3D ultrasound tomography device
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Torsten Hopp, Michael Zapf, Laura Fernandez-Lago, Fridtjof Feldbusch, Hartmut E. Gemmeke, and Nicole V. Ruiter
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- 2023
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14. Breast Imaging with 3D Ultrasound Computer Tomography: Results of a First In-vivo Study in Comparison to MRI Images.
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Torsten Hopp, Lukas Sroba, Michael Zapf, Robin Dapp, Ernst Kretzek, Hartmut Gemmeke, and Nicole V. Ruiter
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- 2014
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15. Model-data-driven image reconstruction with neural networks for ultrasound computed tomography breast imaging
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Juergen Hesser, Torsten Hopp, Yuling Fan, Hartmut Gemmeke, and Hongjian Wang
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Network architecture ,Ground truth ,Artificial neural network ,Mean squared error ,Breast imaging ,business.industry ,Computer science ,Cognitive Neuroscience ,Attenuation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Iterative reconstruction ,Computer Science Applications ,Artificial Intelligence ,Computer vision ,Artificial intelligence ,business ,Test data - Abstract
With the goal of developing an accurate and fast image reconstruction algorithm for ultrasound computed tomography, we combine elements of model- and data-driven approaches and propose a learned method which addresses the disadvantages of both approaches. We design a deep neural network which accounts for a nonlinear forward operator and primal-dual algorithm by its inherent network architecture. The network is trained end-to-end, with ultrasound pressure field data as input to get directly an optimized reconstruction of speed of sound and attenuation images. The training and test data are based on a set of Optical and Acoustic Breast Phantom Database, where we use the image as ground truth and simulate pressure field data according to our forward model. Extensive experiments show that our method achieves significant improvements over state-of-the-art reconstruction methods in this field. Experiments show that the proposed algorithm improves the measures structural similarity measure (SSIM) from 0.74 to 0.95 and root mean squared error (RMSE) from 0.13 to 0.09 on average concerning the speed of sound reconstruction, while it improves the SSIM from 0.60 to 0.94 and RMSE from 0.24 to 0.10 on average in attenuation reconstruction.
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- 2022
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16. 2D/3D Registration for Localization of Mammographically Depicted Lesions in Breast MRI.
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Torsten Hopp and Nicole V. Ruiter
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- 2012
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17. Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction
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Wenzhao Zhao, Yuling Fan, Hongjian Wang, Hartmut Gemmeke, Koen W A van Dongen, Torsten Hopp, and Jürgen Hesser
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Radiological and Ultrasound Technology ,Radiology, Nuclear Medicine and imaging - Abstract
Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap. Approach. To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods, and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines. Main results. The experimental results show that our deep learning methods help to improve the neural networks’ robustness against noise and the generalizability to real measurement data. Significance. Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for the application of deep learning methods to practical ultrasound tomography image reconstruction based on simulation datasets.
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- 2022
18. Simple Tool for Application Runs and Transfer of Data (S.T.A.R.T.).
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Marcus Hardt, Ahmad Hammad, and Torsten Hopp
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- 2010
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19. Image fusion of Ultrasound Computer Tomography volumes with X-ray mammograms using a biomechanical model based 2D/3D registration.
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Torsten Hopp, N. Duric, and Nicole V. Ruiter
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- 2015
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20. A MATLAB GUI for the Analysis and Exploration of Signal and Image Data of an Ultrasound Computer Tomograph.
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Torsten Hopp, Gregor Schwarzenberg, Michael Zapf, and Nicole V. Ruiter
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- 2008
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21. Bundling 3D- and 2D-based registration of MRI to x-ray breast tomosynthesis.
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P. Cotic Smole, Nicole V. Ruiter, C. Kaiser, J. Krammer, and Torsten Hopp
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- 2018
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22. Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization.
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Torsten Hopp, Matthias Dietzel, Pascal A. Baltzer, P. Kreisel, Werner A. Kaiser, Hartmut Gemmeke, and Nicole V. Ruiter
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- 2013
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23. 2D/3D image fusion of X-ray mammograms with breast MRI: visualizing dynamic contrast enhancement in mammograms.
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Torsten Hopp, Pascal A. Baltzer, Matthias Dietzel, Werner A. Kaiser, and Nicole V. Ruiter
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- 2012
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24. Wave-based ultrasound transmission tomography using the paraxial approximation in 2D
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Olga Molchanova, Hartmut Gemmeke, Michael Zapf, Torsten Hopp, Hongjian Wang, and Nicole Ruiter
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- 2022
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25. Realization of an pseudo-randomly sampled 3D USCT
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Michael Zapf, Torsten Hopp, Hartmut Gemmeke, Martin Angerer, Zewei Lu, Olga Molchanova, Nima Rashvand, Roberto Blanco, Petra Steck, Benjamin Leyrer, Denis Tcherniakhovski, Dietmar Bormann, Klaus Schlote-Holubek, and Nicole V. Ruiter
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- 2022
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26. Glasses for 3D ultrasound computer tomography: phase compensation.
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Michael Zapf, Torsten Hopp, and Nicole V. Ruiter
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- 2016
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27. Memory-Efficient Neural Network For Non-Linear Ultrasound Computed Tomography Reconstruction
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Jürgen Hesser, Koen W. A. van Dongen, Torsten Hopp, Hartmut Gemmeke, Yuling Fan, and Hongjian Wang
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Artificial neural network ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Iterative reconstruction ,01 natural sciences ,Measure (mathematics) ,Field (computer science) ,Domain (software engineering) ,010309 optics ,Data acquisition ,Frequency domain ,Multilayer perceptron ,0103 physical sciences ,Artificial intelligence ,business - Abstract
Deep neural networks have proven to excel classical medical image reconstruction techniques. Some networks are based on fully connected (FC) layers to achieve domain transformation such as from the data acquisition domain to the image domain. However, FC layers result in huge numbers of parameters which take a lot of GPU memory. Hence, they do not scale well, and the overall performance is limited. For ultrasound computed tomography (USCT) application, we propose a memory-efficient convolutional network that reconstructs images from the frequency domain to image domain with much less parameters compared with multilayer perceptron, by using data-driven learning. Extensive experiments demonstrate that our method achieves high reconstruction quality. It improves the structural similarity measure (SSIM) from 0.73 to 0.99 when compared with state-of-the-art reconstruction methods in this field while reduces 2/3 parameters when compared with deep neural network with FC layers to reconstruct images from frequency domain to image domain.
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- 2021
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28. Dual domain network architecture for non-linear ultrasound transmission tomography reconstruction
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Hongjian Wang, Juergen Hesser, Yuling Fan, Torsten Hopp, and Hartmut Gemmeke
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Network architecture ,Computer science ,Attenuation ,Paraxial approximation ,Ultrasound transmission tomography ,Initialization ,Iterative reconstruction ,Inverse problem ,Overfitting ,Algorithm - Abstract
Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction.
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- 2021
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29. Image registration between MRI and spot mammograms for X-ray guided stereotactic breast biopsy: preliminary results
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Paola Clauser, Sarah Said, Nicole V. Ruiter, Pascal A. T. Baltzer, and Torsten Hopp
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Breast biopsy ,medicine.medical_specialty ,Digital mammography ,medicine.diagnostic_test ,business.industry ,Image registration ,Magnetic resonance imaging ,medicine.disease ,Breast cancer ,Biopsy ,medicine ,Mammography ,Radiology ,ddc:620 ,business ,Contrast-enhanced Magnetic Resonance Imaging ,Engineering & allied operations - Abstract
Breast cancer is the most common cancer type among women. Approximately 40,000 women are expected to die from breast cancer every year. While digital mammography has a central role in the early diagnosis of breast cancer, many cancers are not visible in mammography, for example in women with dense breast tissue. Contrast enhanced magnetic resonance imaging (CE-MRI) of the breast is often used to detect lesions not visible in mammography. Lesions with suspicious characteristics on CE-MRI need to be further assessed with MRI-guided biopsy. However, MRI-guided biopsy is expensive, time consuming, and not widely available. In this paper, a novel method for a matching tool between MRI and spot mammograms is proposed. Our aim is to transfer information that is only visible in MRI onto mammographic spot projections, to enable X-ray guided biopsy even if the lesion is only visible in MRI. Two methods of registration in combination are used; a biomechanical model based registration between MRI and full view X-ray mammograms and a subsequent image based registration between full mammograms and spot mammograms. Preliminary results assessed for one patient from the Medical University of Vienna are presented. The target registration error (TRE) of biomechanical model based registration is 2.4 mm and the TRE of the image based registration is 9.5 mm. The total TRE of the two steps is 7.3 mm.
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- 2021
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30. Strain elastography with ultrasound computer tomography: a simulation study based on biomechanical models
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Nicole V. Ruiter and Torsten Hopp
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Strain elastography ,Ground truth ,Discriminator ,Computer science ,business.industry ,Attenuation ,Deformation (meteorology) ,Speed of sound ,Biomechanical model ,Computer vision ,Ultrasonic Tomography ,Artificial intelligence ,ddc:620 ,business ,Engineering & allied operations - Abstract
Ultrasound computer tomography (USCT) is a promising modality for breast cancer diagnosis which images the reflectivity, sound speed and attenuation of tissue. Elastic properties of breast tissue, however, cannot directly be imaged although they have shown to be applicable as a discriminator between different tissue types. In this work we propose a novel approach combining USCT with the principles of strain elastography. Socalled USCT-SE makes use of imaging the breast in two deformation states, estimating the deformation field based on reconstructed images and thereby allows localizing and distinguishing soft and hard masses. We use a biomechanical model of the breast to realistically simulate both deformation states of the breast. The analysis of the strain is performed by estimating the deformation field from the deformed to the undeformed image by a non-rigid registration. In two experiments the non-rigid registration is applied to ground truth sound speed images and simulated SAFT images. Results of the strain analysis show that for both cases soft and hard lesions can be distinguished visually in the elastograms. This paper provides a first approach to obtain mechanical information based on external mechanical excitation of breast tissue in a USCT system.
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- 2021
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31. Towards Subject-Specific Therapy Planning for Non-Invasive Blood Brain Barrier Opening in Mice by Focused Ultrasound
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Saskia Grudzenski-Theis, Torsten Hopp, Carl Gross, Marc Fatar, Nicole V. Ruiter, and Stefan Heger
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Image-Guided Therapy ,Hydrophone ,medicine.diagnostic_test ,business.industry ,Attenuation ,Ultrasound ,Therapy planning ,Magnetic resonance imaging ,Blood–brain barrier ,Transducer ,medicine.anatomical_structure ,medicine ,ddc:620 ,business ,Engineering & allied operations ,Biomedical engineering - Abstract
Focused ultrasound (FUS) is a promising method to open the blood brain barrier (BBB) for treatment of neurodegenerative diseases. Accurate targeting is essential for a successful BBB opening (BBBo). We aim to develop a robust therapy planning for BBBo in mice, which is challenging due to the size of the brain and the influence of the skull on the ultrasound pressure distribution. For enabling mouse individual therapy planning, a simulation tool is proposed, developed and validated. We used the k-Wave toolbox to enable 3D acoustic simulations of the commercial FUS system from Image Guided Therapy (IGT). Micro-CT scans were used to model the geometry of skulls. Simulations using a mouse skull showed an attenuation of approx. 20–24% depending on the position of penetration, which was validated by hydrophone measurements in the same range. Based on these validations we planned BBBo in m ice by placing the transducer at different positions over the mouse brain and varying the excitation amplitude. With different transducer positions, the peak pressure in the brain varied between 0.54 MPa and 0.62 MPa at 11% output level, which is expected to enable safe BBBo. Subsequently, in vivo experiments were conducted using the aforementioned simulation parameters. BBBo was confirmed by contrast enhanced T1 weighted magnetic resonance images immediately after sonication.
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- 2021
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32. Model-Based Dose Identification of Dalbavancin for Long-Term Suppressive Outpatient Treatment of Ventricular Assist Device Infections
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Ute Chiriac, Uwe Liebchen, Otto Roman Frey, Heike Lanzinger, Sabrina Klein, Torsten Hoppe-Tichy, Matthias Karck, Anna Meyer, and Benedict Morath
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dalbavancin ,ventricular assist device ,population pharmacokinetics ,therapeutic drug monitoring ,long-term treatment ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Increasing evidence suggests that dalbavancin is an effective long-term treatment for ventricular assist device (VAD) infections, with various prolonged dosing regimens currently in use. This retrospective study aimed to assess dalbavancin pharmacokinetics in VAD patients and identify optimal, feasible dosing regimens for long-term suppressive outpatient therapy. Data from Heidelberg University Hospital’s VAD register were analyzed using non-linear mixed-effects modeling for pharmacokinetic analysis and dosing simulations (Lixoft®). The probability of target attainment (PTA) and cumulative fraction of response (CFR) were calculated for different protein-binding scenarios considering the minimum inhibitory concentration (MIC) distribution of Staphylococcus aureus. Using data from 13 patients with 38 blood samples, a two-compartment model best described the dalbavancin pharmacokinetics, with a typical value for clearance of 0.050 L/h, central volume of distribution of 6.5 L, and peripheral volume of 15.4 L. No covariates significantly improved the model fit. The observed protein binding varied between 96 and 98%. Dosing simulations demonstrated that 1500 mg every 3 weeks ensured the target attainment for stasis at MIC values of 0.125 mg/L (PTA ≥ 90%) up to a protein binding of 99%. Considering the CRF, longer dosing intervals up to 5 weeks might be possible. Depending on individual MICs and protein binding, a dalbavancin regimen of 1500 mg every 3 to 5 weeks therefore appears to be a valuable option for outpatient therapy of VAD infections. Therapeutic drug monitoring should be considered to manage inter-individual variability and to support clinicians in long-term treatments of subacute and chronic infections.
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- 2024
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33. Fast Image Reconstruction in Ultrasound Transmission Tomography by U-net
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Xueze Qian, Jürgen Hesser, Nicole V. Ruiter, Hongjian Wang, Torsten Hopp, and Hartmut Gemmeke
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Acceleration ,Speedup ,Pixel ,Artificial neural network ,Iterative method ,Computer science ,Noise reduction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Ultrasound transmission tomography ,Iterative reconstruction ,Algorithm - Abstract
Conventional iterative methods for image reconstruction in ultrasound transmission tomography need to perform many iterations where at each iteration one has to compute the complex forward model of ultrasound wave propagation, and hence they are time-consuming. We use a U-net neural network to accelerate the reconstruction, by training the network to map from an initial reconstruction obtained via a few iterations of L-BFGS method to the target ground truth image. Since the computation of a forward pass of the neural network is very fast, we can expect a significant acceleration using the trained network for image reconstruction. Experiments show that our trained network can replace 40 L-BFGS iterations to generate equivalent reconstructions with slightly better quantitative quality in terms of normalized root mean square error and better visual quality due to the network's denoising effect. It can achieve up to 283× speedup compared with L-BFGS method for reconstructing small-size sound speed images with 80×80 pixels. This implies that we can expect even greater acceleration effects when applying such approach to reconstruct large-size 3D images.
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- 2020
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34. Fat ray ultrasound transmission tomography: preliminary experimental results with simulated data
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Nicole V. Ruiter, Torsten Hopp, Pierre-Antoine Comby, and Franziska Zuch
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Physics ,Ground truth ,Compressed sensing ,Fresnel zone ,Breast imaging ,Astrophysics::High Energy Astrophysical Phenomena ,Bent molecular geometry ,Ultrasound transmission tomography ,Acoustic wave equation ,Algorithm ,Synthetic data - Abstract
Ultrasound transmission tomography is a promising modality for breast cancer diagnosis. For image reconstruc- tion approximations to the acoustic wave equation such as straight or bent rays are commonly used due to their low computational complexity. For sparse apertures the coverage of the volume by rays is very limited, thereby requiring strong regularization in the inversion process. The concept of fat rays reduces the sparseness and includes the contributions to the measured signal originating from the first Fresnel zone. In this work we investi- gate the application of the fat ray concept to ultrasound transmission tomography. We implement a straight ray, bent ray and fat ray forward model. For the inversion process a least squares solver (LSQR), a simultaneous al- gebraic reconstruction technique (SART) and a compressive sensing based total variation minimization (TVAL3) is applied. The combination of forward models and inversion processes has been evaluated by synthetic data. TVAL3 outperforms SART and LSQR, especially for sparse apertures. The fat ray concept is able to decrease the error with respect to the ground truth compared to the bent ray method especially for SART and LSQR inversion, and especially for very sparse apertures.
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- 2020
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35. An Innovative Practical Automatic Segmentation of Ultrasound Computer Tomography Images Acquired from USCT System
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Ashkan Tashk, Nicole V. Ruiter, and Torsten Hopp
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Point spread function ,Data processing ,medicine.diagnostic_test ,Computer Networks and Communications ,Computer science ,business.industry ,Energy Engineering and Power Technology ,Binary number ,020207 software engineering ,02 engineering and technology ,Image segmentation ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Preprocessor ,020201 artificial intelligence & image processing ,Segmentation ,3D ultrasound ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Tomography ,Electrical and Electronic Engineering ,business - Abstract
A 3D ultrasound computer tomography (USCT) device with a nearly isotropic and spatially invariant 3D point spread function has been constructed at Institute for Data Processing and Electronic (IPE), Karlsruhe Institute of Technology (KIT). This device is currently applied in clinical studies for breast cancer screening. In this paper, a new method to develop an automated segmentation algorithm for USCT acquired images is proposed. The method employs distance regularized level set evolutionary (DRLSE) active contours along with surface fitting extrapolation and 3D binary mask generation for fully automatic segmentation outcome. In the first stage of the proposed algorithm, DRLSE is applied to those 3D USCT slice images which contain breast and are less affected by noise and ring artifacts named as Cat2. The DRLSE segmentation results are employed to extrapolate the rest of slice images known as Cat1. To overcome defectively segmented slice images, a 3D binary mask is generated out of USCT attenuation images. The 3D binary mask is multiplied by the DRLSE-based segmentation results to form finally segmented 3D USCT images. The method was tested on 12 clinical dataset images. According to F-measure criterion, the proposed method shows higher performance than the previously proposed semiautomatic segmentation one.
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- 2018
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36. 3D ultrasound computer tomography: Hardware setup, reconstruction methods and first clinical results
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Nicole V. Ruiter, Michael Zapf, Hartmut Gemmeke, Torsten Hopp, and Clemens G. Kaiser
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Physics ,Point spread function ,Nuclear and High Energy Physics ,medicine.medical_specialty ,Image fusion ,medicine.diagnostic_test ,business.industry ,02 engineering and technology ,Iterative reconstruction ,021001 nanoscience & nanotechnology ,01 natural sciences ,Imaging phantom ,Data acquisition ,0103 physical sciences ,medicine ,Medical physics ,3D ultrasound ,Computer vision ,Depth of field ,Tomography ,Artificial intelligence ,0210 nano-technology ,business ,010301 acoustics ,Instrumentation - Abstract
A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT). Current experimental USCT systems are still focused in elevation dimension resulting in a large slice thickness, limited depth of field, loss of out-of-plane reflections, and a large number of movement steps to acquire a stack of images. 3D USCT emitting and receiving spherical wave fronts overcomes these limitations. We built an optimized 3D USCT, realizing for the first time the full benefits of a 3D system. The point spread function could be shown to be nearly isotropic in 3D, to have very low spatial variability and fit the predicted values. The contrast of the phantom images is very satisfactory in spite of imaging with a sparse aperture. The resolution and imaged details of the reflectivity reconstruction are comparable to a 3 T MRI volume. Important for the obtained resolution are the simultaneously obtained results of the transmission tomography. The KIT 3D USCT was then tested in a pilot study on ten patients. The primary goals of the pilot study were to test the USCT device, the data acquisition protocols, the image reconstruction methods and the image fusion techniques in a clinical environment. The study was conducted successfully; the data acquisition could be carried out for all patients with an average imaging time of six minutes per breast. The reconstructions provide promising images. Overlaid volumes of the modalities show qualitative and quantitative information at a glance. This paper gives a summary of the involved techniques, methods, and first results.
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- 2017
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37. Ultrasound transmission tomography image reconstruction with a fully convolutional neural network
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Hongjian Wang, Jürgen Hesser, Wenzhao Zhao, Koen W. A. van Dongen, Hartmut Gemmeke, and Torsten Hopp
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Image quality ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,Iterative reconstruction ,Convolutional neural network ,Imaging phantom ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Paraxial approximation ,Image Processing, Computer-Assisted ,medicine ,Radiology, Nuclear Medicine and imaging ,Tomography ,Radiological and Ultrasound Technology ,Artificial neural network ,Phantoms, Imaging ,business.industry ,Pattern recognition ,medicine.disease ,Wave equation ,Ultrasound transmission tomography ,Ultrasonic Waves ,030220 oncology & carcinogenesis ,Image reconstruction ,Neural Networks, Computer ,Artificial intelligence ,Fully convolutional neural network ,business - Abstract
Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.
- Published
- 2020
38. Accelerating image reconstruction in ultrasound transmission tomography using L-BFGS algorithm
- Author
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Hongjian Wang, Torsten Hopp, Hartmut Gemmeke, and Jürgen Hesser
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Nonlinear conjugate gradient method ,Hessian matrix ,symbols.namesake ,Computer science ,Broyden–Fletcher–Goldfarb–Shanno algorithm ,Conjugate gradient method ,Jacobian matrix and determinant ,symbols ,Ultrasound transmission tomography ,Iterative reconstruction ,Inverse problem ,Algorithm - Abstract
In ultrasound transmission tomography, image reconstruction is an inverse problem which is solved iteratively based on a forward model that simulates the wave propagation of ultrasound. A commonly used forward model is paraxial approximation of the Helmholtz equation, which is time-consuming. Hence developing optimizers that minimize the number of forward solutions is crucial to achieve clinically acceptable reconstruction time, while the state-of-the-art methods in this field such as Gauss-Newton conjugate gradient (CG) and nonlinear CG are not capable of reaching this goal. To that end, we focus on Jacobian-free optimizers or accelerators in this paper, since the computation of the Jacobian is expensive. We investigate the limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm as a preconditioning technique due to its ability to efficiently approximate inverse Hessian without performing forward model or its adjoint. We show L-BFGS can reach a speedup of more than one order of magnitude for the noise-free case, while the method still halves the reconstruction time in presence of noise in the data. The performance drop is explained by perturbed gradients due to noise in the data. We also show when used alone as a quasi-Newton method, L-BFGS is competitive with the accelerated CG based methods regarding the number of iterations, and outperforms them regarding reconstruction time.
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- 2019
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39. Experimental analysis of ray-based sound speed reconstruction algorithms for phase aberration corrected USCT SAFT imaging
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Nicole V. Ruiter, Torsten Hopp, Franziska Zuch, Michael Zapf, and Hartmut Gemmeke
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Phase aberration ,Optics ,business.industry ,Computer science ,Speed of sound ,business - Published
- 2019
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40. Burden of intracerebral haemorrhage in Europe: forecasting incidence and mortality between 2019 and 2050Research in context
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Hatem A. Wafa, Iain Marshall, Charles D.A. Wolfe, Wanqing Xie, Catherine O. Johnson, Roland Veltkamp, Yanzhong Wang, Kirsten H. Harvey, Eleni Korompoki, Lucio D’Anna, Omid Halse, Emily R. Harvey, Klemens Hügen, Uwe Malzahn, Sabine Ullmann, Carolin Schuhmann, Gabriele Putz Todd, Hannes Brinz, Cornelia Fiessler, Peter U. Heuschmann, Kirsten Haas, Viktoria Rücker, Christian Enzinger, Stefan Ropele, Daniela Pinter, Melanie Haidegger, Thomas Gattringer, Simon Fandler-Höfler, Joan Montaner, Elena Palà, Anna Penalba, Marcel Lamana Vallverdu, Daisy Guaman Pilco, Stéphanie Debette, Igor Sibon, Pauline Renou, Morgane Lachaize, Léa Milan, Nathalie Heyvang, Sylvain Ledure, Pascale Michel, Johanna Conhoc, Léa Donnadieu, Kelly Hyves, Valeria Caso, Maria Giulia Mosconi, Mara Graziani, Virginia Cancelloni, Laura Marchini, Bianca Emanuela Koehler, Peter Brønnum Nielsen, Torben Bjerregaard Larsen, Gregory Y.H. Lip, Solveigh Horstmann, Jan Purrucker, Peter Ringleb, Mariam Haffa, Sabrina Klein, Lenka Taylor, Torsten Hoppe-Tichy, Walter E. Haefeli, Hanna M. Seidling, Jürgen Burhenne, Kathrin I. Foerster, Viktoria Wurmbach, Claudia Marquart, Deirdre A. Lane, Elena Ivany, and Robyn Lotto
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Future ,Stroke ,Intracerebral haemorrhage ,Epidemiology ,Europe ,Public aspects of medicine ,RA1-1270 - Abstract
Summary: Background: Anticipating the burden of intracerebral haemorrhage is crucial for proactive management and building resilience against future health challenges. Prior forecasts are based on population demography and to a lesser extent epidemiological trends. This study aims to utilise selected modifiable risk factors and socio-demographic indicators to forecast the incidence and mortality of intracerebral haemorrhage in Europe between 2019 and 2050. Methods: Three intracerebral haemorrhage risk factors identified in the Global Burden of Diseases, Injuries, and Risk Factors study (GBD 2019)—high systolic blood pressure, high fasting plasma glucose, and high body mass index—were utilised to predict the risk-attributable fractions between 2019 and 2050. Disease burden not attributable to these risk factors was then forecasted using time series models (autoregressive integrated moving average [ARIMA]), incorporating the Socio-demographic Index (SDI) as an external predictor. The optimal parameters of ARIMA models were selected for each age-sex-country group based on the Akaike Information Criterion (AIC). Different health scenarios were constructed by extending the past 85th and 15th percentiles of annualised rates of change in risk factors and SDI across all location-years, stratified by age and sex groups. A decomposition analysis was performed to assess the relative contributions of population size, age composition, and intracerebral haemorrhage risk on the projected changes. Findings: Compared with observed figures in 2019, our analysis predicts an increase in the burden of intracerebral haemorrhage in Europe in 2050, with a marginal rise of 0.6% (95% uncertainty interval [UI], −7.4% to 9.6%) in incident cases and an 8.9% (−2.8% to 23.6%) increase in mortality, reaching 141.2 (120.6–166.5) thousand and 144.2 (122.9–172.2) thousand respectively. These projections may fluctuate depending on trajectories of the risk factors and SDI; worsened trends could result in increases of 16.7% (8.7%–25.3%) in incidence and 31.2% (17.7%–48%) in mortality, while better trajectories may lead to a 10% (16.4%–2.3%) decrease in intracerebral haemorrhage cases with stabilised mortality. Individuals aged ≥80 years are expected to contribute significantly to the burden, comprising 62.7% of the cases in 2050, up from 40% in 2019, and 72.5% of deaths, up from 50.5%. Country-wide variations were noted in the projected changes, with decreases in the standardised rates across all nations but varying crude rates. The largest relative reductions in counts for both incidence and mortality are expected in Latvia, Bulgaria, and Hungary—ranging from −38.2% to −32.4% and −37.3% to −30.2% respectively. In contrast, the greatest increases for both measures were forecasted in Ireland (45.7% and 74.4%), Luxembourg (45% and 70.7%), and Cyprus (44.5% and 74.2%). The modelled increase in the burden of intracerebral haemorrhage could largely be attributed to population ageing. Interpretation: This study provides a comprehensive forecast of intracerebral haemorrhage in Europe until 2050, presenting different trajectories. The potential increase in the number of people experiencing and dying from intracerebral haemorrhage could have profound implications for both caregiving responsibilities and associated costs. However, forecasts were divergent between different scenarios and among EU countries, signalling the pivotal role of public health initiatives in steering the trajectories. Funding: The European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 754517. The National Institute for Health and Care Research (NIHR) under its Programme Grants for Applied Research (NIHR202339).
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- 2024
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41. Effectiveness, barriers and facilitating factors of strategies for active delabelling of patients with penicillin allergy labels: a systematic review protocol
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Torsten Hoppe-Tichy, Benedict Morath, Claudia Denkinger, Elham Khatamzas, Hannah Nürnberg, Tabea Krause, Lars Oetken, Sophie Rauer, and Amelie Rapp
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Medicine - Abstract
Introduction Up to 15% of adult patients in the clinical setting report to be allergic to penicillin. However, in most cases, penicillin allergy is not confirmed. Due to the negative aspects associated with erroneous penicillin allergy, the implementation of active delabelling processes for penicillin allergy is an important part of antibiotic stewardship programmes. Depending on the clinical setting, different factors need to be considered during implementation. This review examines the effectiveness of different delabelling interventions and summarises components and structures that facilitate, support or constrain structured penicillin allergy delabelling.Methods and analysis This review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The databases MEDLINE (via PubMed), EMBASE and Cochrane Library were searched for studies reporting on any intervention to identify, assess or rule out uncertain penicillin allergy. To improve completeness, two further databases are also searched for grey literature. Study design, intervention type, professional groups involved, effectiveness, limitations, barriers, facilitating factors, clinical setting and associated regulatory factors will be extracted and analysed. In addition, exclusion criteria for participation in the delabelling intervention and criteria for not delabelling penicillin allergy will be summarised. In case of failed protocols, these are highlighted and quantitatively analysed if possible. Two independent reviewers will perform the screening process and data extraction. Discordant decisions will be resolved through review by a third reviewer. Bias assessment of the individual studies will be performed using the Newcastle Ottawa Scale.Ethics and dissemination Because individual patient-related data are not analysed, an ethical approval is not required. The review will be published in a peer-reviewed scientific journal.
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- 2024
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42. Ultrasound Image Reconstruction Using Nesterov's Accelerated Gradient
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Jürgen Hesser, Burak Dalkilic, Hongjian Wang, Hartmut Gemmeke, and Torsten Hopp
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010302 applied physics ,Line search ,Optimization problem ,Computational complexity theory ,Computer science ,Wolfe conditions ,Inverse problem ,01 natural sciences ,010309 optics ,Rate of convergence ,Conjugate gradient method ,ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION ,0103 physical sciences ,Gradient descent ,Algorithm - Abstract
The purpose of this paper is to investigate Nesterov’s accelerated gradient (NAG) method for the reconstruction of speed of sound and attenuation images in ultrasound computed tomography. The inverse problem of reconstruction is tackled via minimizing the deviation between exact measurements and the predicted measurements based on a paraxial approximation of the Helmholtz equation which simulates the ultrasound wave forward propagation. To solve this optimization problem, NAG is performed and compared with other algorithms. Also, a line search method is used to compute the step size for each iteration since finding proper step sizes is crucial for the convergence of such optimization algorithms. The strong Wolfe conditions are adopted as the termination condition for line search. We have compared five algorithms, namely Gauss-Newton conjugate gradient, gradient descent, NAG, gradient descent with line search, and NAG with line search. On one hand, NAG with line search has the fastest convergence rate in respect to the number of used iterations compared to the other methods. However, due to the increased computational complexity of line search for each iteration, it requires extra computational time. On the other hand, NAG with a fixed step size for all iterations is the fastest method among all the tested methods regarding computational time.
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- 2018
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43. Multigrid Method for Solving Linearized Systems in Ultrasound Transmission Tomography
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Hongjian Wang, Torsten Hopp, Hartmut Gemmeke, and Jürgen Hesser
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Computer science ,Linear system ,02 engineering and technology ,Iterative reconstruction ,Solver ,Grid ,030218 nuclear medicine & medical imaging ,Reduction (complexity) ,03 medical and health sciences ,0302 clinical medicine ,Multigrid method ,Conjugate gradient method ,0202 electrical engineering, electronic engineering, information engineering ,Ultrasound transmission tomography ,020201 artificial intelligence & image processing ,Algorithm - Abstract
Ultrasound transmission tomography can offer quantitative characterization of breast tissue by reconstructing its sound speed and attenuation images. The image reconstruction process is a nonlinear inverse problem which we iteratively solve using the paraxial approximation of wave equation. The problem is tackled via the Gauss-Newton method yielding a set of linear systems and iteratively solving these linear systems. In this paper, we study multigrid methods for solving these linear systems. We test three multigrid schemes including V-cycle, W-cycle, and full multigrid (FMG), with up to four resolution levels. At each grid level, we use the conjugate gradient (CG) method as a standard solver. Our interest is at first by how far these schemes allow us to reduce the computations alone. For performance evaluation, we compare the multigrid methods with fixed-grid CG method where we directly apply CG on the finest grid. Results show that all tested multigrid methods have an accelerating effect in terms of needing fewer CG iterations on the finest grid. The best-case reduction is 32% for V-cycle, 33% for W-cycle, and 27% for FMG. This means that our multigrid scheme has the potential for significantly reducing reconstruction time for large-scale 2D or 3D images, where the computation cost on the finest grid is very high.
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- 2018
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44. A Comparison of Biomechanical Models for MRI to Digital Breast Tomosynthesis 3D Registration
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Nicole V. Ruiter, P. Cotic Smole, Clemens G. Kaiser, J. Krammer, and Torsten Hopp
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Similarity (geometry) ,medicine.diagnostic_test ,business.industry ,Computer science ,Image registration ,Magnetic resonance imaging ,Digital Breast Tomosynthesis ,medicine.disease ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Metric (mathematics) ,medicine ,Mammography ,Computer vision ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Volume (compression) - Abstract
Increasing interest in multimodal breast cancer diagnosis has led to the development of methods for MRI to X-ray mammography registration. The severe breast deformation in X-ray mammography is often tackled by biomechanical models, yet there is no common consensus in literature about the required complexity of the deformation model and the simulation strategy. We present for the first time an automated patient-specific biomechanical model based image registration of MRI to digital breast tomosynthesis (DBT). DBT provides three-dimensional information of the compressed breast and as such drives the registration by a volume similarity metric. We compare different simulation strategies and propose a patient-specific optimization of simulation and model parameters. The average three-dimensional breast overlap measured by Dice coefficient of DBT and registered MRI improves for four analyzed subjects by including the estimation of unloaded state, simulation of gravity, and a concentrated pull force that mimics manual positioning of the breast on the plates from 88.1% for a mere compression simulation to 93.1% when including all our proposed simulation steps, whereas additional parameter optimization further increased the value to 94.4%.
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- 2018
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45. Bundling 3D- and 2D-based registration of MRI to x-ray breast tomosynthesis
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Nicole V. Ruiter, Clemens G. Kaiser, J. Krammer, P. Cotic Smole, and Torsten Hopp
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Similarity (geometry) ,medicine.diagnostic_test ,business.industry ,Computer science ,medicine ,Mammography ,Computer vision ,Breast deformation ,Artificial intelligence ,Digital Breast Tomosynthesis ,business ,Projection (set theory) ,Tomosynthesis - Abstract
Increasing interest in multimodal breast cancer diagnosis has led to the development of methods for MRI to X-ray mammography registration to provide direct correlation of modalities. The severe breast deformation in X-ray mammography is often tackled by biomechanical models, which however have not yet brought the registration accuracy to a clinically applicable level. We present a novel registration approach of MRI to X-ray tomosynthesis. Tomosynthesis provides three-dimensional information of the compressed breast and as such has the ability to open new possibilities in the registration of MRI and X-ray data. By bundling the 3D information from the tomosynthesis volume with the 2D projection images acquired at different measuring angles, we provide a correlation between the registration error in 3D and 2D and evaluate different 3D- and 2D-based similarity metrics to drive the optimization of the automated patient-specific registration approach. From the preliminary study of four analysed patients we found that the projected registration error is in general larger than the 3D error in case of small registration errors in the cranio-caudal direction. Although both image shape and intensitybased 2D similarity metrics showed a clear correlation with the 2D registration error at different projection angles, metrics that relied on the combined 2D and 3D information yielded in most of the cases the minimal registration error and as such had better performance than similarity metrics that rely only on the shape similarity of volumes.
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- 2018
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46. Experimental evaluation of straight ray and bent ray phase aberration correction for USCT SAFT imaging
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Michael Zapf, Nicole V. Ruiter, Torsten Hopp, and Hartmut Gemmeke
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Physics ,Bresenham's line algorithm ,business.industry ,Image quality ,030206 dentistry ,Iterative reconstruction ,01 natural sciences ,03 medical and health sciences ,0302 clinical medicine ,Optics ,Speed of sound ,0103 physical sciences ,Point (geometry) ,business ,010301 acoustics ,Image resolution ,Electrical impedance ,Fast marching method - Abstract
In Ultrasound computer tomography (USCT) Synthetic aperture focusing technique (SAFT) is often applied for reflectivity image reconstruction. Phase aberration correction is essential to cope with the large sound speed differences in water and the different human tissues. In this paper we compare two approaches for phase aberration correction: a straight ray approximation using the Bresenham algorithm (B-SAFT) and a bent ray approximating using a multi-stencil Fast Marching Method (FMM-SAFT). The analysis is carried out with simulated point scatterers and simulated phantoms to measure the effect on the image resolution and contrast. The method is additionally applied to experimental data. B-SAFT degrades the image resolution and contrast in cases of large sound speed differences of objects and if the reconstructed point is close to a boundary where a change in impedance is present. FMM-SAFT is able to recover the image quality in these cases if the sound speed distribution is known accurately and with high resolution. If these requirements cannot be met, B-SAFT proved to be more robust.
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- 2018
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47. Image fusion of Ultrasound Computer Tomography volumes with X-ray mammograms using a biomechanical model based 2D/3D registration
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Nicole V. Ruiter, Neb Duric, and Torsten Hopp
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Breast imaging ,Image registration ,Breast Neoplasms ,Health Informatics ,Models, Biological ,Multimodal Imaging ,Sensitivity and Specificity ,Pattern Recognition, Automated ,Imaging, Three-Dimensional ,Image Interpretation, Computer-Assisted ,Humans ,Medicine ,Mammography ,Computer Simulation ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Image fusion ,Ground truth ,3d registration ,Modality (human–computer interaction) ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,X-Ray Film ,Reproducibility of Results ,Image Enhancement ,Computer Graphics and Computer-Aided Design ,Subtraction Technique ,Female ,Ultrasonic Tomography ,Ultrasonography, Mammary ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Algorithms - Abstract
Ultrasound Computer Tomography (USCT) is a promising breast imaging modality under development. Comparison to a standard method like mammography is essential for further development. Due to significant differences in image dimensionality and compression state of the breast, correlating USCT images and X-ray mammograms is challenging. In this paper we present a 2D/3D registration method to improve the spatial correspondence and allow direct comparison of the images. It is based on biomechanical modeling of the breast and simulation of the mammographic compression. We investigate the effect of including patient-specific material parameters estimated automatically from USCT images. The method was systematically evaluated using numerical phantoms and in-vivo data. The average registration accuracy using the automated registration was 11.9mm. Based on the registered images a method for analysis of the diagnostic value of the USCT images was developed and initially applied to analyze sound speed and attenuation images based on X-ray mammograms as ground truth. Combining sound speed and attenuation allows differentiating lesions from surrounding tissue. Overlaying this information on mammograms, combines quantitative and morphological information for multimodal diagnosis.
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- 2015
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48. Automated breast segmentation in ultrasound computer tomography SAFT images
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Nicole V. Ruiter, Michael Zapf, Hartmut Gemmeke, Torsten Hopp, W. Y. Tan, and W. You
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Active contour model ,Vector flow ,business.industry ,Breast imaging ,Computer science ,Scale-space segmentation ,Image segmentation ,medicine.disease ,Breast cancer ,medicine ,Segmentation ,Computer vision ,Tomography ,Artificial intelligence ,business - Abstract
Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.
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- 2017
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49. Time of flight interpolated synthetic aperture focusing technique
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Michael Zapf, Ernst Kretzek, Torsten Hopp, Hartmut Gemmeke, and Nicole V. Ruiter
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business.industry ,Computer science ,Image quality ,3D reconstruction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,computer.software_genre ,01 natural sciences ,030218 nuclear medicine & medical imaging ,Reduction (complexity) ,03 medical and health sciences ,Time of flight ,0302 clinical medicine ,Data acquisition ,Voxel ,Speed of sound ,0103 physical sciences ,Computer vision ,Ultrasonic Tomography ,Artificial intelligence ,business ,010301 acoustics ,Algorithm ,computer ,Interpolation - Abstract
Synthetic Aperture Focusing Technique (SAFT) allows fast data acquisition and optimally focused images. The computational burden for 3D imaging is large as for each voxel the delay for each acquired A-scan has to be calculated, e.g. O(N5) for N3 voxels and N2 A-scans. For 3D reconstruction of objects which are large in terms of the wavelength, e.g. ≥ (100 λ)3, the computation of one volume takes several days on a current multicore PC. If the 3D distribution of the speed of sound is applied to correct the delays, the computation time increases further. In this work a time of flight interpolation based GPU implementation (TOFI-SAFT) is presented which accelerates our previous GPU implementation of speed of sound corrected SAFT by a factor of 7 to 16 min. with only minor reduction of image quality.
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- 2017
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50. Glucocorticoid-induced microRNA-378 signaling mediates the progression of pancreatic cancer by enhancing autophagy
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Li Liu, Shanshan Han, Xi Xiao, Xuefeng An, Jury Gladkich, Ulf Hinz, Stefan Hillmer, Torsten Hoppe-Tichy, Yi Xu, Michael Schaefer, Oliver Strobel, and Ingrid Herr
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Cytology ,QH573-671 - Abstract
Abstract Glucocorticoids (GCs) are widely used in tumor therapy to reduce tumor growth, inflammation, edema, and other side effects. Controversially, GCs may also cause the progression of highly aggressive pancreatic ductal adenocarcinoma (PDAC). Because microRNA (miR) and autophagy signaling support the invasive growth of PDAC, we asked whether these mechanisms may be targeted by GCs. Six established human PDAC cell lines, tissue from patients who received GC medication (n = 35) prior to surgery, or not (n = 35), and tumor xenografts were examined by RT‒qPCR, transmission electron microscopy (TEM), monodansylcadaverine (MDC) staining, immunohistochemistry, in situ hybridization, gene array and Kaplan‒Meier analysis with bioinformatics, and MTT, western blot, colony, spheroid, migration, and invasion assays. We found that various GCs, including dexamethasone (DEX), induced typical features of macroautophagy with the appearance of autolysosomes, enhanced LC3-II, decreased SQSTM1/p62 expression and induced epithelial-mesenchymal transition (EMT) and gemcitabine resistance. The GC receptor (GR) antagonist mifepristone (RU486) counteracted DEX-induced autophagy features, suggesting that the GC-GR complex is involved in the induction of autophagy. The autophagy-related miR-378i and miR-378a-3p were selected as the top upregulated candidates, and their high expression in PDAC patient tissue correlated with low survival. siRNA-mediated downregulation of miR-378 inhibited DEX-induced autophagy, and tumor progression. Bioinformatics confirmed the contribution of miR-378 to the regulation of signaling networks involved in GC-induced autophagy and tumor progression. The construction of a molecular docking model revealed stable binding of miR-378 to the DEX-GR complex, suggesting direct regulation. These substantial, novel, in-depth data reveal that GCs favor autophagy-mediated cancer progression by inducing miR-378 and GR binding and implicate GR and miR-378 as new therapeutic targets.
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- 2022
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