71,100 research outputs found
Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method
Photoacoustic computed tomography (PACT) is an emerging computed imaging
modality that exploits optical contrast and ultrasonic detection principles to
form images of the absorbed optical energy density within tissue. If the object
possesses spatially variant acoustic properties that are unaccounted for by the
reconstruction method, the estimated image can contain distortions. While
reconstruction methods have recently been developed to compensate for this
effect, they generally require the object's acoustic properties to be known a
priori. To circumvent the need for detailed information regarding an object's
acoustic properties, we previously proposed a half-time reconstruction method
for PACT. A half-time reconstruction method estimates the PACT image from a
data set that has been temporally truncated to exclude the data components that
have been strongly aberrated. However, this method can be improved upon when
the approximate sizes and locations of isolated heterogeneous structures, such
as bones or gas pockets, are known. To address this, we investigate PACT
reconstruction methods that are based on a variable data truncation (VDT)
approach. The VDT approach represents a generalization of the half-time
approach, in which the degree of temporal truncation for each measurement is
determined by the distance between the corresponding ultrasonic transducer
location and the nearest known bone or gas void location. Computer-simulated
and experimental data are employed to demonstrate the effectiveness of the
approach in mitigating artifacts due to acoustic heterogeneities
Muon Track Reconstruction and Data Selection Techniques in AMANDA
The Antarctic Muon And Neutrino Detector Array (AMANDA) is a high-energy
neutrino telescope operating at the geographic South Pole. It is a lattice of
photo-multiplier tubes buried deep in the polar ice between 1500m and 2000m.
The primary goal of this detector is to discover astrophysical sources of high
energy neutrinos. A high-energy muon neutrino coming through the earth from the
Northern Hemisphere can be identified by the secondary muon moving upward
through the detector. The muon tracks are reconstructed with a maximum
likelihood method. It models the arrival times and amplitudes of Cherenkov
photons registered by the photo-multipliers. This paper describes the different
methods of reconstruction, which have been successfully implemented within
AMANDA. Strategies for optimizing the reconstruction performance and rejecting
background are presented. For a typical analysis procedure the direction of
tracks are reconstructed with about 2 degree accuracy.Comment: 40 pages, 16 Postscript figures, uses elsart.st
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
Iterative image reconstruction algorithms for optoacoustic tomography (OAT),
also known as photoacoustic tomography, have the ability to improve image
quality over analytic algorithms due to their ability to incorporate accurate
models of the imaging physics, instrument response, and measurement noise.
However, to date, there have been few reported attempts to employ advanced
iterative image reconstruction algorithms for improving image quality in
three-dimensional (3D) OAT. In this work, we implement and investigate two
iterative image reconstruction methods for use with a 3D OAT small animal
imager: namely, a penalized least-squares (PLS) method employing a quadratic
smoothness penalty and a PLS method employing a total variation norm penalty.
The reconstruction algorithms employ accurate models of the ultrasonic
transducer impulse responses. Experimental data sets are employed to compare
the performances of the iterative reconstruction algorithms to that of a 3D
filtered backprojection (FBP) algorithm. By use of quantitative measures of
image quality, we demonstrate that the iterative reconstruction algorithms can
mitigate image artifacts and preserve spatial resolution more effectively than
FBP algorithms. These features suggest that the use of advanced image
reconstruction algorithms can improve the effectiveness of 3D OAT while
reducing the amount of data required for biomedical applications
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.Comment: The first two authors contributed equally to this wor
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
FastJet user manual
FastJet is a C++ package that provides a broad range of jet finding and
analysis tools. It includes efficient native implementations of all widely used
2-to-1 sequential recombination jet algorithms for pp and e+e- collisions, as
well as access to 3rd party jet algorithms through a plugin mechanism,
including all currently used cone algorithms. FastJet also provides means to
facilitate the manipulation of jet substructure, including some common boosted
heavy-object taggers, as well as tools for estimation of pileup and
underlying-event noise levels, determination of jet areas and subtraction or
suppression of noise in jets.Comment: 69 pages. FastJet 3 is available from http://fastjet.fr
Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms
PURPOSE: We develop a practical, iterative algorithm for image-reconstruction
in under-sampled tomographic systems, such as digital breast tomosynthesis
(DBT).
METHOD: The algorithm controls image regularity by minimizing the image total
-variation (TpV), a function that reduces to the total variation when
or the image roughness when . Constraints on the image, such as
image positivity and estimated projection-data tolerance, are enforced by
projection onto convex sets (POCS). The fact that the tomographic system is
under-sampled translates to the mathematical property that many widely varied
resultant volumes may correspond to a given data tolerance. Thus the
application of image regularity serves two purposes: (1) reduction of the
number of resultant volumes out of those allowed by fixing the data tolerance,
finding the minimum image TpV for fixed data tolerance, and (2) traditional
regularization, sacrificing data fidelity for higher image regularity. The
present algorithm allows for this dual role of image regularity in
under-sampled tomography.
RESULTS: The proposed image-reconstruction algorithm is applied to three
clinical DBT data sets. The DBT cases include one with microcalcifications and
two with masses.
CONCLUSION: Results indicate that there may be a substantial advantage in
using the present image-reconstruction algorithm for microcalcification
imaging.Comment: Submitted to Medical Physic
- …