5,078 research outputs found
Towards Picogram Detection of Superparamagnetic Iron-Oxide Particles Using a Gradiometric Receive Coil
Superparamagnetic iron-oxide nanoparticles can be used in a variety of
medical applications like vascular or targeted imaging. Magnetic particle
imaging (MPI) is a promising tomographic imaging technique that allows
visualizing the 3D nanoparticle distribution concentration in a non-invasive
manner. The two main strengths of MPI are high temporal resolution and high
sensitivity. While the first has been proven in the assessment of dynamic
processes like cardiac imaging, it is unknown how far the detection limit of
MPI can be lowered. Within this work, we will present a highly sensitive
gradiometric receive-coil unit combined with a noise-matching network tailored
for the measurement of mice. The setup is capable of detecting 5 ng of iron in
vitro at 2.14 sec acquisition time. In terms of iron concentration we are able
to detect 156 {\mu}g/L marking the lowest value that has been reported for an
MPI scanner so far. In vivo MPI mouse images of a 512 ng bolus at 21.5 ms
acquisition time allow for capturing the flow of an intravenously injected
tracer through the heart of a mouse. Since it has been rather difficult to
compare detection limits across MPI publications we propose guidelines
improving the comparability of future MPI studies.Comment: 15 Pages, 7 Figures, V2: Changed the initials of Author Kannan M
Krishnan, added two citations, corrected typo
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
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