9,033 research outputs found
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
Tomography: mathematical aspects and applications
In this article we present a review of the Radon transform and the
instability of the tomographic reconstruction process. We show some new
mathematical results in tomography obtained by a variational formulation of the
reconstruction problem based on the minimization of a Mumford-Shah type
functional. Finally, we exhibit a physical interpretation of this new technique
and discuss some possible generalizations.Comment: 11 pages, 5 figure
3D correlative single-cell imaging utilizing fluorescence and refractive index tomography
Cells alter the path of light, a fact that leads to well-known aberrations in
single cell or tissue imaging. Optical diffraction tomography (ODT) measures
the biophysical property that causes these aberrations, the refractive index
(RI). ODT is complementary to fluorescence imaging and does not require any
markers. The present study introduces RI and fluorescence tomography with
optofluidic rotation (RAFTOR) of suspended cells, quantifying the intracellular
RI distribution and colocalizing it with fluorescence in 3D. The technique is
validated with cell phantoms and used to confirm a lower nuclear RI for HL60
cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is
observed in the RI distribution. The applications shown confirm predictions of
previous studies and illustrate the potential of RAFTOR to improve our
understanding of cells and tissues.Comment: 15 pages, 5 figure
3D ultrastructural organization of whole Chlamydomonas reinhardtii cells studied by nanoscale soft x-ray tomography
The complex architecture of their structural elements and compartments is a hallmark of eukaryotic cells. The creation of high resolution models of whole cells has been limited by the relatively low resolution of conventional light microscopes and the requirement for ultrathin sections in transmission electron microscopy. We used soft x-ray tomography to study the 3D ultrastructural organization of whole cells of the unicellular green alga Chlamydomonas reinhardtii at unprecedented spatial resolution. Intact frozen hydrated cells were imaged using the natural x-ray absorption contrast of the sample without any staining. We applied different fiducial-based and fiducial-less alignment procedures for the 3D reconstructions. The reconstructed 3D volumes of the cells show features down to 30 nm in size. The whole cell tomograms reveal ultrastructural details such as nuclear envelope membranes, thylakoids, basal apparatus, and flagellar microtubule doublets. In addition, the x-ray tomograms provide quantitative data from the cell architecture. Therefore, nanoscale soft x-ray tomography is a new valuable tool for numerous qualitative and quantitative applications in plant cell biology
Tomographic Study of Internal Erosion of Particle Flows in Porous Media
In particle-laden flows through porous media, porosity and permeability are
significantly affected by the deposition and erosion of particles. Experiments
show that the permeability evolution of a porous medium with respect to a
particle suspension is not smooth, but rather exhibits significant jumps
followed by longer periods of continuous permeability decrease. Their origin
seems to be related to internal flow path reorganization by avalanches of
deposited material due to erosion inside the porous medium. We apply neutron
tomography to resolve the spatio-temporal evolution of the pore space during
clogging and unclogging to prove the hypothesis of flow path reorganization
behind the permeability jumps. This mechanistic understanding of clogging
phenomena is relevant for a number of applications from oil production to
filters or suffosion as the mechanisms behind sinkhole formation.Comment: 18 pages, 9 figure
Solving ill-posed inverse problems using iterative deep neural networks
We propose a partially learned approach for the solution of ill posed inverse
problems with not necessarily linear forward operators. The method builds on
ideas from classical regularization theory and recent advances in deep learning
to perform learning while making use of prior information about the inverse
problem encoded in the forward operator, noise model and a regularizing
functional. The method results in a gradient-like iterative scheme, where the
"gradient" component is learned using a convolutional network that includes the
gradients of the data discrepancy and regularizer as input in each iteration.
We present results of such a partially learned gradient scheme on a non-linear
tomographic inversion problem with simulated data from both the Sheep-Logan
phantom as well as a head CT. The outcome is compared against FBP and TV
reconstruction and the proposed method provides a 5.4 dB PSNR improvement over
the TV reconstruction while being significantly faster, giving reconstructions
of 512 x 512 volumes in about 0.4 seconds using a single GPU
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