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257 results on '"Stierstorfer, Karl"'

1. A Monte Carlo assessment of the spectral performance of four types of photon counting detectors

Author

Stierstorfer, Karl and Hupfer, Martin

Subjects
Physics - Medical Physics, Physics - Instrumentation and Detectors
Abstract

In previous publications, we have presented an alternative approach to determine essential detector properties like the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and the Detective Quantum Efficiency (DQE) based on a Monte Carlo model of the detection process. If a Monte Carlo model for the detector response to photons impinging at various locations of a pixel is available, the full statistics of the detector can be derived in a straightforward manner. The purpose of this paper is to describe the method in detail and to apply it to four types of realistic detectors: direct converting detectors using CdTe and silicon, a CdTe photon counter with additional coincidence counters and an optical counting system using LaBr3 as scintillator., Comment: 13 pages

Published
2024

3. CT Field of View Extension Using Combined Channels Extension and Deep Learning Methods

Author

Fournié, Éric, Baer-Beck, Matthias, and Stierstorfer, Karl

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper proposes a method to extend the field of view of computed tomography images. In a first step, the field of view is increased by extrapolating linearly the outer channels in the sinogram space. The modified sinogram is then used to reconstruct extended field of view (EFoV) images containing artifacts due to the channels extension. In a second step, those artifacts are reduced by a deep learning network in image space. The proposed method has been evaluated on a collection of clinical scans. The resulting volumes have been checked for consistency and plausibility and compared to an existing state of the art EFoV method., Comment: MIDL 2019 [arXiv:1907.08612]

Published
2019

7. Raw data consistent deep learning‐based field of view extension for dual‐source dual‐energy CT.

Author

Maier, Joscha, Erath, Julien, Sawall, Stefan, Fournié, Eric, Stierstorfer, Karl, and Kachelrieß, Marc

Subjects
DEEP learning, COMPUTED tomography, COINCIDENCE, DEEP brain stimulation
Abstract

Background: Due to technical constraints, dual‐source dual‐energy CT scans may lack spectral information in the periphery of the patient. Purpose: Here, we propose a deep learning‐based iterative reconstruction to recover the missing spectral information outside the field of measurement (FOM) of the second source‐detector pair. Methods: In today's Siemens dual‐source CT systems, one source‐detector pair (referred to as A) typically has a FOM of about 50 cm, while the FOM of the other pair (referred to as B) is limited by technical constraints to a diameter of about 35 cm. As a result, dual‐energy applications are currently only available within the small FOM, limiting their use for larger patients. To derive a reconstruction at B's energy for the entire patient cross‐section, we propose a deep learning‐based iterative reconstruction. Starting with A's reconstruction as initial estimate, it employs a neural network in each iteration to refine the current estimate according to a raw data fidelity measure. Here, the corresponding mapping is trained using simulated chest, abdomen, and pelvis scans based on a data set containing 70 full body CT scans. Finally, the proposed approach is tested on simulated and measured dual‐source dual‐energy scans and compared against existing reference approaches. Results: For all test cases, the proposed approach was able to provide artifact‐free CT reconstructions of B for the entire patient cross‐section. Considering simulated data, the remaining error of the reconstructions is between 10 and 17 HU on average, which is about half as low as the reference approaches. A similar performance with an average error of 8 HU could be achieved for real phantom measurements. Conclusions: The proposed approach is able to recover missing dual‐energy information for patients exceeding the small 35 cm FOM of dual‐source CT systems. Therefore, it potentially allows to extend dual‐energy applications to the entire‐patient cross section. [ABSTRACT FROM AUTHOR]

Published
2024
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29. First performance evaluation of a dual-source CT (DSCT) system

Author

Flohr, Thomas G., McCollough, Cynthia H., Bruder, Herbert, Petersilka, Martin, Gruber, Klaus, Süβ, Christoph, Grasruck, Michael, Stierstorfer, Karl, Krauss, Bernhard, Raupach, Rainer, Primak, Andrew N., Küttner, Axel, Achenbach, Stefan, Becker, Christoph, Kopp, Andreas, and Ohnesorge, Bernd M.

Published
2006
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