Modeling Vibrations of a Tiled Talbot-Lau Interferometer on a Clinical CT

Schmid, Clemens; Viermetz, Manuel; Gustschin, Nikolai; Noichl, Wolfgang; Haeusele, Jakob; Lasser, Tobias; Kœhler, Thomas; Pfeiffer, Franz

doi:10.1109/tmi.2022.3217662

Cited by 10 publications

(15 citation statements)

References 35 publications

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“…The presented results demonstrate that dark-field CT of the human body is feasible using a Talbot-Lau interferometer in a state of the art clinical CT gantry. Previous publications discussed the design, processing, and the initial implementation of the dark-field CT system [11], [19], [31]. Here, a detailed characterization of the interferometer analyses the challenges Fig.…”

Section: Discussionmentioning

confidence: 99%

“…To still be able to draw all information from this data, a specialized processing pipeline has been developed which can separate the three image channels, i.e., intensity, visibility, and interferometer phase. It was initially sketched in [11] and a full description of the processing framework including all the related optimization and correction mechanisms is presented in [31]. With this processing applied to the measured data, we can extract the interferometer state at each projection.…”

Section: Translation From Laboratory To Gantrymentioning

confidence: 99%

“…The various sources of vibration lead to displacement of the gratings, and this consequently gives rise to fluctuation of the interferometer state. The processing of the air-scan data models and fits these fluctuations and returns the interferometer performance parameters and all repetitive deviations [31]. These processing results therefore contain information about the vibrations and how they alter the intensity, visibility, and phase of the Moiré pattern.…”

Section: Vibration Analysismentioning

confidence: 99%

“…We therefore expect a visibility variation at twice the frequency of the previously evaluated interferometer phase variation (175 Hz) coupled to the absolute speed of the gratings, i.e., the absolute rate of phase change. The derivation of the exact relation can be found in [31,Eq. 7].…”

Section: Vibration Analysismentioning

confidence: 99%

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Initial Characterization of Dark-Field CT on a Clinical Gantry

Viermetz

Gustschin

Schmid

et al. 2023

IEEE Trans. Med. Imaging

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X-ray computed tomography (CT) is an important non-destructive imaging technique, particularly in clinical diagnostics. Even with the latest innovations like dual-energy and photon-counting CT, the image contrast is solely generated from attenuation in the tissue. An extension -fully compatible with these novelties -is darkfield CT, which retrieves an additional, so-called dark-field contrast. Unlike the attenuation channel, the dark-field channel is sensitive to tissue microstructure and porosity below the resolution of the imaging system, which allows additional insights into the health of the lung tissue or the structure of calcifications. The potential clinical value has been demonstrated in several preclinical studies and recently also in radiography patient studies. Just recently the first dark-field CT for the human body was established at the Technical University of Munich and in this paper, we discuss the performance of this prototype. We evaluate the interferometer components and the imposed challenges that the integration into the CT gantry brings by comparing the results to simulations and measurements at a laboratory setup. The influence of the clinical X-ray source on the Talbot-Lau interferometer and the impact of vibrations, which are immanent on the clinical CT gantry, are analyzed in detail to reveal their characteristic frequencies and origin. A beam hardening correction is introduced as an important step to adapt to the poly-chromatic spectrum and make quantitative dark-field imaging possible. We close with an analysis of the image resolution and the applied patient dose, and conclude that the performance is sufficient to suggest initial patient studies using the presented dark-field CT system.

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Section: Discussionmentioning

confidence: 99%

Section: Translation From Laboratory To Gantrymentioning

confidence: 99%

Section: Vibration Analysismentioning

confidence: 99%

Section: Vibration Analysismentioning

confidence: 99%

See 2 more Smart Citations

Initial Characterization of Dark-Field CT on a Clinical Gantry

Viermetz

Gustschin

Schmid

et al. 2023

IEEE Trans. Med. Imaging

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“…Since statistical information from the measured data can be included into optimization, this process is called statistical phase retrieval (SPR). It is discussed in more detail in [26]. Conventional sliding-window-based demodulation uses the SPR algorithm and is explained in the following.…”

Section: Sliding-window-based Phase Retrievalmentioning

confidence: 99%

Advanced Phase-Retrieval for Stepping-Free X-Ray Dark-Field Computed Tomography

Haeusele

Schmid

Viermetz

et al. 2023

IEEE Trans. Med. Imaging

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Grating-based phase-and dark-field-contrast X-ray imaging is a novel technology that aims to extend conventional attenuation-based X-ray imaging by unlocking two additional contrast modalities. The so called phasecontrast and dark-field channels provide enhanced soft tissue contrast and additional microstructural information. Accessing this additional information comes at the expense of a more intricate measurement setup and necessitates sophisticated data processing. A big challenge for translating grating-based dark-field computed tomography to medical applications lies in minimizing the data acquisition time. While a continuously moving detector is ideal, it prohibits conventional phase stepping techniques that require multiple projections under the same angle with different grating positions. One solution to this problem is the so-called sliding window processing approach that is compatible with continuous data acquisition. However, conventional sliding window techniques lead to crosstalk-artifacts between the three image channels, if the projection of the sample moves too fast on the detector within a processing window. In this work we introduce a new interpretation of the phase retrieval problem for continuous acquisitions as a demodulation problem. In this interpretation, we identify the origin of the crosstalk-artifacts as partially overlapping modulation side bands. Furthermore, we present three algorithmic

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Streak artefact removal in x‐ray dark‐field computed tomography using a convolutional neural network

Kumschier,

Thalhammer,

Schmid

et al. 2024

Medical Physics

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BackgroundComputed tomography (CT) relies on the attenuation of x‐rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X‐ray dark‐field (DF) imaging is a recently developed technology that utilizes x‐ray optical gratings to enable small‐angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human‐scale x‐ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefact removal.PurposeReducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post‐processing for artefact reduction in x‐ray DF CT and how multi‐rotation scans can serve as a pathway for training data.MethodsWe employed a supervised deep‐learning approach using a three‐dimensional dual‐frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x‐ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose‐compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose‐, nor time‐compatible and, therefore, unfeasible for clinical imaging of patients.ResultsThe trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images.ConclusionsOur results showcase the potential of a neural network to reduce streak artifacts in x‐ray DF CT. The image quality is successfully enhanced in dose‐compatible x‐ray DF CT, which plays an essential role for the adoption of x‐ray DF CT into modern clinical radiology.

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Modeling Vibrations of a Tiled Talbot-Lau Interferometer on a Clinical CT

Cited by 10 publications

References 35 publications

Initial Characterization of Dark-Field CT on a Clinical Gantry

Initial Characterization of Dark-Field CT on a Clinical Gantry

Advanced Phase-Retrieval for Stepping-Free X-Ray Dark-Field Computed Tomography

Streak artefact removal in x‐ray dark‐field computed tomography using a convolutional neural network

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