NANOlab Center of Excellence literature database -- Query Results

“Single-shot tomography of discrete dynamic objects”. Kadu A, Lucka F, Batenburg KJ, IEEE transactions on computational imaging 10, 941 (2024). http://doi.org/10.1109/TCI.2024.3414320 Abstract: This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that incorporates spatial and temporal information of the dynamic objects. Our method uses the explicit assumption of homogeneous attenuation values of discrete objects. We achieve this computationally through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to variational regularization-based methods using similar image models, our approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains. The supporting data and code are provided. Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) Impact Factor: 5.4 DOI: 10.1109/TCI.2024.3414320 Additional Links: UA library record; WoS full record; WoS full record show.php?record=9315
“Various compressed sensing setups evaluated against Shannon sampling under constraint of constant illumination”. Van den Broek W, Reed BW, Béché, A, Velazco A, Verbeeck J, Koch CT, IEEE transactions on computational imaging 5, 502 (2019). http://doi.org/10.1109/TCI.2019.2894950 Abstract: Under the constraint of constant illumination, an information criterion is formulated for the Fisher information that compressed sensing measurements in optical and transmission electron microscopy contain about the underlying parameters. Since this approach requires prior knowledge of the signal's support in the sparse basis, we develop a heuristic quantity, the detective quantum efficiency (DQE), that tracks this information criterion well without this knowledge. In this paper, it is shown that for the investigated choice of sensing matrices, and in the absence of read-out noise, i.e., with only Poisson noise present, compressed sensing does not raise the amount of Fisher information in the recordings above that of Shannon sampling. Furthermore, enabled by the DQE's analytical tractability, the experimental designs are optimized by finding out the optimal fraction of on pixels as a function of dose and read-out noise. Finally, we introduce a regularization and demonstrate, through simulations and experiment, that it yields reconstructions attaining minimum mean squared error at experimental settings predicted by the DQE as optimal. Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) Impact Factor: 4.546 Times cited: 7 DOI: 10.1109/TCI.2019.2894950 Additional Links: UA library record; WoS full record; WoS citing articles show.php?record=5368

Abstract: This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that incorporates spatial and temporal information of the dynamic objects. Our method uses the explicit assumption of homogeneous attenuation values of discrete objects. We achieve this computationally through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to variational regularization-based methods using similar image models, our approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains. The supporting data and code are provided.

Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)

Impact Factor: 5.4

DOI: 10.1109/TCI.2024.3414320

Additional Links: UA library record; WoS full record; WoS full record

show.php?record=9315

Abstract: Under the constraint of constant illumination, an information criterion is formulated for the Fisher information that compressed sensing measurements in optical and transmission electron microscopy contain about the underlying parameters. Since this approach requires prior knowledge of the signal's support in the sparse basis, we develop a heuristic quantity, the detective quantum efficiency (DQE), that tracks this information criterion well without this knowledge. In this paper, it is shown that for the investigated choice of sensing matrices, and in the absence of read-out noise, i.e., with only Poisson noise present, compressed sensing does not raise the amount of Fisher information in the recordings above that of Shannon sampling. Furthermore, enabled by the DQE's analytical tractability, the experimental designs are optimized by finding out the optimal fraction of on pixels as a function of dose and read-out noise. Finally, we introduce a regularization and demonstrate, through simulations and experiment, that it yields reconstructions attaining minimum mean squared error at experimental settings predicted by the DQE as optimal.