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Manu N. Lakshmanan

Researcher at Duke University

Publications -  27
Citations -  482

Manu N. Lakshmanan is an academic researcher from Duke University. The author has contributed to research in topics: Coded aperture & Monte Carlo method. The author has an hindex of 11, co-authored 27 publications receiving 373 citations. Previous affiliations of Manu N. Lakshmanan include National Institutes of Health & Cornell University.

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Journal ArticleDOI

Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

TL;DR: Photon‐counting spectral CT allowed simultaneous material decomposition of multiple contrast agents in vivo and tissue enhancement at multiple phases was observed in a single CT acquisition, potentially obviating the need for multiphase CT scans and thus reducing radiation dose.
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Dual-contrast agent photon-counting computed tomography of the heart: initial experience

TL;DR: Simultaneous multi-contrast agent cardiac imaging is feasible with photon-counting detector CT using occlusion-reperfusion canine model of myocardial infarction to simultaneously assess both first-pass and late enhancement of the myocardium.
Journal ArticleDOI

Comparison of dc and superconducting rf photoemission guns for high brightness high average current beam production

TL;DR: In this paper, a comparison of the two most prominent electron sources of high average current high brightness electron beams, dc and superconducting rf photoemission guns, is carried out using a large-scale multivariate genetic optimizer interfaced with space charge simulation codes.
Proceedings ArticleDOI

Coding and sampling for compressive x-ray diffraction tomography

TL;DR: In this article, a specific coded aperture x-ray coherent scatter imaging architecture is proposed to improve the sampling efficiency of xray tomography and increase the physical diversity of the xray phenomena measured.
Proceedings ArticleDOI

Optimization of a coded aperture coherent scatter spectral imaging system for medical imaging

TL;DR: A coherent-scatter imaging approach based on the use of coded apertures that enables fast, dose-efficient, high-resolution scatter imaging of biologically-relevant materials and is used to demonstrate automated material detection in biological tissue.