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676 results on '"Lee, Peter D"'

1. Eikonal phase retrieval: Unleashing the fourth generation sources potential for enhanced propagation based tomography on biological samples

Author

Mirone, Alessandro, Brunet, Joseph, Admans, Leandre, Boistel, Renaud, Sowinski, Morgane, Berruyer, Camille, Payno, Henri, Boller, Elodie, Paleo, Pierre, Walsh, Claire L., Lee, Peter D., and Tafforeau, Paul

Subjects
Physics - Medical Physics, Physics - Optics
Abstract

The evolution of synchrotrons towards higher brilliance beams has increased the possible sample-to-detector propagation distances for which the source confusion circle does not lead to geometrical blurring. This makes it possible to push near-field propagation driven phase contrast enhancement to the limit, revealing low contrast features which would otherwise remain hidden under an excessive noise. Until today this possibility was hindered, in many objects of scientific interest, by the simultaneous presence of strong phase gradient regions and low contrast features. The strong gradients, when enhanced with the now possible long propagation distances, induce such strong phase effects that the linearisation assumptions of current state-of-the-art single-distance phase retrieval filters are broken, and the resulting image quality is jeopardized. Here, we introduce a new iterative phase-retrieval algorithm and compare it with the Paganin phase-retrieval algorithm, in both the monochromatic and polychromatic cases, obtaining superior image quality. In the polychromatic case the comparison was done with an extrapolated Paganin algorithm obtained by reintroducing, into our phase retrieval algorithm, the linearization approximations underlying the Paganin forward model. Our work provides an innovative algorithm which efficiently performs the phase retrieval task over the entire near-field range, producing images of exceptional quality for mixed attenuation objects.

Published
2023

2. Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging

Author

Yagis, Ekin, Aslani, Shahab, Jain, Yashvardhan, Zhou, Yang, Rahmani, Shahrokh, Brunet, Joseph, Bellier, Alexandre, Werlein, Christopher, Ackermann, Maximilian, Jonigk, Danny, Tafforeau, Paul, Lee, Peter D, and Walsh, Claire

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Automated blood vessel segmentation is vital for biomedical imaging, as vessel changes indicate many pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality, Hierarchical Phase Contrast Tomography (HiP CT). Introduced in 2020 at the European Synchrotron Radiation Facility, HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel, with the capability for localized zooms in selected regions down to 1mm per voxel without sectioning. We have created a training dataset with double annotator validated vascular data from three kidneys imaged with HiP CT in the context of the Human Organ Atlas Project. Finally, utilising the nnU Net model, we conduct experiments to assess the models performance on both familiar and unseen samples, employing vessel specific metrics. Our results show that while segmentations yielded reasonably high scores such as clDice values ranging from 0.82 to 0.88, certain errors persisted. Large vessels that collapsed due to the lack of hydrostatic pressure (HiP CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP CT imaging database.

Published
2023

10. Correlative Synchrotron X-ray Imaging and Diffraction of Directed Energy Deposition Additive Manufacturing

Author

Chen, Yunhui, Clark, Samuel J., Collins, David M., Marussi, Sebastian, Hunt, Simon A., Fenech, Danielle M., Connolley, Thomas, Atwood, Robert C., Magdysyuk, Oxana V., Baxter, Gavin J., Jones, Martyn A., Leung, Chu Lun Alex, and Lee, Peter D.

Subjects
Physics - Applied Physics
Abstract

The governing mechanistic behaviour of Directed Energy Deposition Additive Manufacturing (DED-AM) is revealed by a combined in situ and operando synchrotron X-ray imaging and diffraction study of a nickel-base superalloy, IN718. Using a unique process replicator, real-space phase-contrast imaging enables quantification of the melt-pool boundary and flow dynamics during solidification. This imaging knowledge informed precise diffraction measurements of temporally resolved microstructural phases during transformation and stress development with a spatial resolution of 100 $\mu$m. The diffraction quantified thermal gradient enabled a dendritic solidification microstructure to be predicted and coupled to the stress orientation and magnitude. The fast cooling rate entirely suppressed the formation of secondary phases or recrystallisation in the solid-state. Upon solidification, the stresses rapidly increase to the yield strength during cooling. This insight, combined with IN718 $'$s large solidification range suggests that the accumulated plasticity exhausts the alloy$'$s ductility, causing liquation cracking. This study has revealed additional fundamental mechanisms governing the formation of highly non-equilibrium microstructures during DED-AM.

Published
2020

11. In situ and Operando X-ray Imaging of Directed Energy Deposition Additive Manufacturing

Author

Chen, Yunhui, Clark, Samuel J., Sinclair, Lorna, Leung, Chu Lun Alex, Marussi, Sebastian, Connolley, Thomas, Magdysyuk, Oxana V., Atwood, Robert C., Baxter, Gavin J., Jones, Martyn A., McCartney, David G., Todd, Iain, and Lee, Peter D.

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

The mechanical performance of Directed Energy Deposition Additive Manufactured (DED-AM) components can be highly material dependent. Through in situ and operando synchrotron X-ray imaging we capture the underlying phenomena controlling build quality of stainless steel (SS316) and titanium alloy (Ti6242 or Ti-6Al-2Sn-4Zr-2Mo). We reveal three mechanisms influencing the build efficiency of titanium alloys compared to stainless steel: blown powder sintering; reduced melt-pool wetting due to the sinter; and pore pushing in the melt-pool. The former two directly increase lack of fusion porosity, while the later causes end of track porosity. Each phenomenon influences the melt-pool characteristics, wetting of the substrate and hence build efficacy and undesirable microstructural feature formation. We demonstrate that porosity is related to powder characteristics, pool flow, and solidification front morphology. Our results clarify DED-AM process dynamics, illustrating why each alloy builds differently, facilitating the wider application of additive manufacturing to new materials., Comment: 20 pages, 4 figures, submitted to Science Advances

Published
2020

23. The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling

Author

Ackermann, Maximilian, Kamp, Jan C., Werlein, Christopher, Walsh, Claire L., Stark, Helge, Prade, Verena, Surabattula, Rambabu, Wagner, Willi L., Disney, Catherine, Bodey, Andrew J., Illig, Thomas, Leeming, Diana J., Karsdal, Morten A., Tzankov, Alexandar, Boor, Peter, Kühnel, Mark P., Länger, Florian P., Verleden, Stijn E., Kvasnicka, Hans M., Kreipe, Hans H., Haverich, Axel, Black, Stephen M., Walch, Axel, Tafforeau, Paul, Lee, Peter D., Hoeper, Marius M., Welte, Tobias, Seeliger, Benjamin, David, Sascha, Schuppan, Detlef, Mentzer, Steven J., and Jonigk, Danny D.

Published
2022
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38. Direct high-order edge-preserving regularization for tomographic image reconstruction

Author

Kazantsev, Daniil, Ovtchinnikov, Evgueni, Lionheart, William R. B., Withers, Philip J., and Lee, Peter D.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Mathematical Software, Computer Science - Numerical Analysis, Mathematics - Numerical Analysis
Abstract

In this paper we present a new two-level iterative algorithm for tomographic image reconstruction. The algorithm uses a regularization technique, which we call edge-preserving Laplacian, that preserves sharp edges between objects while damping spurious oscillations in the areas where the reconstructed image is smooth. Our numerical simulations demonstrate that the proposed method outperforms total variation (TV) regularization and it is competitive with the combined TV-L2 penalty. Obtained reconstructed images show increased signal-to-noise ratio and visually appealing structural features. Computer implementation and parameter control of the proposed technique is straightforward, which increases the feasibility of it across many tomographic applications. In this paper, we applied our method to the under-sampled computed tomography (CT) projection data and also considered a case of reconstruction in emission tomography The MATLAB code is provided to support obtained results., Comment: 16 pages, 11 figures

Published
2015

44. A closer look at high-energy X-ray-induced bubble formation during soft tissue imaging

Author

Xian, R. Patrick, Brunet, Joseph, Huang, Yuze, Wagner, Willi L., Lee, Peter D., Tafforeau, Paul, Walsh, Claire L., Xian, R. Patrick, Brunet, Joseph, Huang, Yuze, Wagner, Willi L., Lee, Peter D., Tafforeau, Paul, and Walsh, Claire L.

Abstract

Improving the scalability of tissue imaging throughput with bright, coherent X-rays requires identifying and mitigating artifacts resulting from the interactions between X-rays and matter. At synchrotron sources, long-term imaging of soft tissues in solution can result in gas bubble formation or cavitation, which dramatically compromises image quality and integrity of the samples. By combining in-line phase-contrast imaging with gas chromatography in real time, we were able to track the onset and evolution of high-energy X-ray-induced gas bubbles in ethanol-embedded soft tissue samples for tens of minutes (two to three times the typical scan times). We demonstrate quantitatively that vacuum degassing of the sample during preparation can significantly delay bubble formation, offering up to a twofold improvement in dose tolerance, depending on the tissue type. However, once nucleated, bubble growth is faster in degassed than undegassed samples, indicating their distinct metastable states at bubble onset. Gas chromatography analysis shows increased solvent vaporization concurrent with bubble formation, yet the quantities of dissolved gasses remain unchanged. By coupling features extracted from the radiographs with computational analysis of bubble characteristics, we uncover dose-controlled kinetics and nucleation site-specific growth. These hallmark signatures provide quantitative constraints on the driving mechanisms of bubble formation and growth. Overall, the observations highlight bubble formation as a critical yet often overlooked hurdle in upscaling X-ray imaging for biological tissues and soft materials and we offer an empirical foundation for their understanding and imaging protocol optimization. More importantly, our approaches establish a top-down scheme to decipher the complex, multiscale radiation–matter interactions in these applications.

Published
2024

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