Invited plenary talks
| Elsa Angelini |
Compressed Biological Imaging |
| Guillaume Bal |
Hybrid Inverse Problems and Internal Functionals |
| Charles L. Epstein |
New approaches to the numerical solution of Maxwell's
Equations |
| Aaron Fenster |
Use of 3D Ultrasound Imaging in Diagnosis, Treatment
and Research |
| Mathias Fink |
Multiwave Imaging and Elastography |
| Polina Golland |
Non-parametric Atlas-Based Segmentation of Highly
Variable Anatomy |
| David Isaacson |
Problems in Electrical Impedance Imaging (talk
cancelled) |
| Ender Konukoglu |
Personalizing Mathematical Models with Sparse
Medical Data: Applications to Tumor Growth and Electrocardiophysiology |
| Jeremy Magland |
Processing strategies for real-time neurofeedback
using fMRI |
| Anne Martel |
Assessing response of cancer to therapy using
MRI |
| Dr Michael Miller |
Diffeomorpic Shape Momentum in Computational Anatomy
and Neuroinformatics |
| Xavier Pennec |
Statistical Analysis on Manifolds in Medical Image
Analysis |
| Justin Romberg |
A Survey of Compressed Sensing and Applications
to Medical Imaging |
| Yoram Rudy |
Noninvasive Electrocardiographic Imaging (ECGI)
of Cardiac Electrophysiology and Arrhythmia |
| John Schotland |
Physical Aspects of Hybrid Inverse Problems |
| Emil Sidky |
What does compressive sensing mean for X-ray CT
and comparisons with its MRI application |
| Samuli Siltanen |
Low-dose three-dimensional X-ray imaging |
| Gunther Uhlmann |
Thermoacoustic tomography with a variable sound
speed |
| Lihong V. Wang |
Photoacoustic Tomography: Ultrasonically Breaking
through the Optical Diffusion Limit |
| Graham Wright |
MRI for Management of Ventricular Arrhythmias |
Abstracts Invited plenary talks
Compressed Biological Imaging
by
Elsa Angelini
Institut Telecom, Telecom ParisTech
Coauthors: Jean-Christophe Olivo-Marin, Marcio Marim de Moraes,
Michael Atlan, Yoann Le Montagner
Compressed sensing (CS) is a new sampling theory that was recently
introduced for efficient acquisition of compressible signals. In
the presented work, we have studied practical applications of the
Fourier-based CS sampling theory for biological microscopy imaging,
with two main contributions:
(i) Image denoising: microscopic images suffer from complex artifacts
associated with noise and non-perfect illumination conditions. In
fluorescence microscopy, noise and photobleaching degrade the quality
of the image. In this work, we have exploited the CS theory as an
image denoising tool, using multiple random undersampling in the
Fourier domain and the Total Variation as a spatial sparsity prior.
Compounding of images reconstructed from multiple sets of random
measurements enforce spatial coherence of meaningful signal components
and decorrelate noisy components. We have studied the relation between
signal sparsity and noise reduction performance under different
noise conditions. We have demonstrated on simulated and practical
experiments on fluorescence microscopy that the proposed denoising
framework provide images with similar or increased signal-to-noise
ratio (SNR) compared to state of the art denoising methods while
relying on a limited number of samples.
If Fourier-domain image point acquisitions were feasible, the proposed
denoising could be used as a fast acquisition scheme which would
enable to reduce exposition times, and reduce the photobleaching
effects.
(ii) Compressed digital holographic microscopy: high data throughput
is becoming increasingly important in microscopy, with high-resolution
cameras (i.e. large numbers of samples per acquisition) and long
observation times. The compressed sensing theory provides a framework
to reconstruct images from fewer samples than traditional acquisition
approaches. However, the very few measurements must be spread over
a large field of view, which is difficult to achieve in conventional
microscopy.
In a first experiment, we have proposed a computational scheme
to perform fast temporal acquisitions of sequences of Fourier amplitude
measures in optical Fourier imaging and estimate the missing phase
information from spectra interpolation between few in-between complete
keyframes. This approach was evaluated for high-frame rate imaging
of moving cells.
In a second experiment, we have implemented a real CS acquisition
scheme for digital holographic microscopy, acquiring a diffraction
map of the optical field and recovering high quality images from
as little as 7% of random measurements. The CS acquisition setup
was successfully extended to high speed low-light single-shot off-axis
holography.
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Hybrid Inverse Problems and Internal Functionals
by
Guillaume Bal
Columbia University
Hybrid inverse problems aim at combining the high contrast of one
imaging modality (such as e.g. Electrical Impedance Tomography or
Optical Tomography in medical imaging) with the high resolution
of another modality (such as e.g. based on ultrasound or magnetic
resonance). Mathematically, these problems often take the form of
inverse problems with internal information. This talk will review
several results of uniqueness and stability obtained recently in
the field of hybrid inverse problems.
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New approaches to the numerical solution
of Maxwell's Equations
by
Charles L. Epstein
University of Pennsylvania
Coauthors: Leslie Greengard (NYU) Michael O'Neil (NYU)
We develop a new integral representation for the solution of the
time harmonic Maxwell equations in media with piecewise constant
dielectric permittivity and magnetic permeability in R^3. This representation
leads to a coupled system of Fredholm integral equations of the
second kind for four scalar densities supported on the material
interface. Like the classical Muller equation, it has no spurious
resonances. Unlike the classical approach, however, the representation
does not suffer from low frequency breakdown. We earlier presented
a similar method for the perfect conductor problem.
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Use of 3D Ultrasound Imaging in Diagnosis,
Treatment and Research
by
Aaron Fenster
Robarts Research Institute
The last two decades have witnessed unprecedented developments
of new imaging systems making use of 3D visualization. These new
technologies have revolutionized diagnostic radiology, as they provide
the clinician with information about the interior of the human body
never before available. Ultrasound imaging is an important cost-effective
technique used routinely in the management of a number of diseases.
However, 2D viewing of 3D anatomy, using conventional ultrasound,
limits our ability to quantify and visualize the anatomy and guide
therapy, because multiple 2D images must be integrated mentally.
This practice is inefficient, and leads to variability and incorrect
diagnoses. Also, since the 2D ultrasound image represents a thin
plane at an arbitrary angle in the body, reproduction of this plane
at a later time is difficult.
Over the past 2 decades, investigators have addressed these limitations
by developing 3D ultrasound techniques. In this paper we describe
developments of 3D ultrasound imaging instrumentation and techniques
for use in diagnosis and image-guided interventions. As ultrasound
imaging is an interactive imaging modality, providing the physician
with real-time visualization of anatomy and function, the development
of image analysis and guidance tools is challenging. Typically,
these tools require segmentation, classification, tracking and visualization
of pathology and instruments to be executed in real-time, accurately,
reproducibly and robustly. As an illustration of these needs, we
will present some diagnostic and image-guided intervention applications
that would benefit from these developments. Examples will be given
for imaging various organs, such as the prostate, carotid arteries,
and breast, and for the use in 3D ultrasound-guided prostate therapy.
In addition, we describe analysis methods to be used for quantitative
analysis of disease progression and regression.
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Multiwave Imaging and Elastography
by
Mathias Fink
Institut Langevin, ESPCI ParisTech, France
Interactions between different kinds of waves can yield images
that beat the single-wave diffraction limit. Multiwave Imaging consists
of combining two different waves-- one to provide contrast, another
to provide spatial resolution - in order to build a new kind of
image. Contrary to single-wave imaging that is always limited by
the contrast and resolution properties of the wave that generated
it, multiwave imaging provides a unique image of the most interesting
contrast with the most interesting resolution. Multiwave imaging
opens new avenues in medical imaging and a large interest for this
approach is now emerging in geophysics and non-destructive testing.
We will describe the different potential interactions between waves
that can give rise to multiwave imaging and we will emphasize the
various multiwave approaches developed in the domain of medical
imaging. Common to all these approaches, ultrasonic waves are almost
always used as one of the wave to provide spatial resolution, while
optical, electromagnetic or sonic shear waves provide the contrast.
Among various multiwave techniques, we will mainly focus on photo-acoustic
and shear wave imaging. Through various medical applications going
from cancer diagnosis to cardiovascular imaging, we will emphasize
the recent clinical successes of multiwave imaging.
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Non-parametric Atlas-Based Segmentation
of Highly Variable Anatomy
by
Polina Golland, MIT
Coauthors: Michal Depa, Mert Sabuncu
We propose a non-parametric probabilistic model for automatic segmentation
of medical images. The resulting inference algorithms register individual
training images to the new image, transfer the segmentation labels
and fuse them to obtain the final segmentation of the test subject.
Our generative model yields previously proposed label fusion algorithms
as special cases, but also leads to a new variant that aggregates
evidence locally in determining the segmentation labels. We demonstrate
the advantages of our approach in two clinical application: segmentation
of neuroanatomical structures and segmentation of the left heart
atrium whose shape varies significantly across the population.
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Problems in Electrical Impedance Imaging
(Talk Canceled)
by
David Isaacson, Mathematical Sciences Department, Rensselaer Polytechnic
Institute
Coauthors: J.C. Newell, and G. Saulnier
Electrical impedance imaging systems apply currents to the surface
of a body and measure the resulting voltages. From this electromagnetic
data an approximate reconstruction and display of the internal electrical
properties of the body are made. We explain how this process leads
to inverse boundary value problems for Maxwell's equations. Since
the conductivity of hearts, and lungs change as blood enters and
leaves these organs , impedance images can be used to monitor heart
and lung function. Since the electrical properties of some cancers
are different from surrounding normal tissues, electrical impedance
spectroscopy may be used to help diagnose some cancers.
Images and movies of heart and lung function, as well as breast
cancers, made with the RPI Adaptive current tomography systems will
be shown. It will be explained how the analysis of spectral properties
of the Dirichlet to Neumann map lead to the design of these adaptive
current tomography systems.
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Personalizing Mathematical Models with
Sparse Medical Data: Applications to Tumor Growth and Electrocardiophysiology
by
Ender Konukoglu
Microsoft Research Cambridge
Coauthors: Nicholas Ayache, Maxime Sermesant, Olivier Clatz, Bjoern
H. Menze
Mathematical models for biophysical systems are crucial in understanding
the underlying physiological dynamics as well as tailoring patient-specific
treatment.One of the biggest challenges for biophysical models is
the identification of patient-specific parameters and the personalized
model. This talk will focus on the problem of parameter identification
using sparse medical data. Challenges associated with the medical
data will be demonstrated with two model problems, tumor growth
and electrocardiophysiology, incorporating different types of data,
i.e. MR images and cardiac mappings. Different techniques for dealing
with sparse data will be presented including deterministic and probabilistic
methods.
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Processing strategies for real-time neurofeedback
using fMRI
by
Jeremy Magland
University of Pennsylvania
Coauthors: Anna Rose Childress
Functional magnetic resonance imaging (fMRI) is traditionally used
as a probe for patterns of brain activity in response to instructed
cognitive tasks and stimuli by averaging of the blood oxygenation
level-dependent (BOLD) response over an entire scan session (usual
10-60 minutes). Recently, real-time feedback approaches have expanded
fMRI from a brain probe to include potential brain interventions.
However, real-time measurements and analyses require entirely different
data processing techniques, because measurements must be made prospectively
(on the fly) throughout the scan using only a subset of the acquired
data. In this talk, we outline the challenges associated with performing
real-time fMRI experiments, and describe the specific techniques
we have found to be successful for providing meaningful and robust
neurofeedback in real time.
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Assessing response of cancer to therapy using
MRI
by
Anne Martel
Sunnybrook Health Sciences Centre, University of Toronto
Personalized medicine, the practice of tailoring medical therapies
to the specific genetic and disease profiles of patients, represents
a major shift from the epidemiologically based model of traditional
medicine. This has lead to the development of novel new therapies
for cancer, for example Herceptin for breast cancer and Gleevec
for chronic myelogenous leukemia. Although this is an exciting development
it poses several challenges to the clinician. These therapies are
extremely expensive and are only designed to work on a subset of
patients hence there is a pressing need for tools that can determine
whether a therapy is effective early in the treatment regime.
Dynamic contrast-enhanced MRI (DCE-MRI) can provide valuable information
about the efficacy of drug therapy. In addition to traditional measures
of lesion size, it has been shown that DCE-MRI can provide important
information about tumour function, for example by providing information
about blood flow and permeability. In this talk I will give an overview
of the role DCE-MRI has to play in monitoring response to therapy
and outline some of the challenges involved in bringing this technology
into routine clinical use. I will also describe some of the work
done in my lab in the areas of quantitative analysis and image registration
to address some of these challenges.
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Diffeomorpic Shape Momentum in Computational
Anatomy and Neuroinformatics
by
Dr Michael Miller
Johns Hopkins University Center for Imaging Science
Over the past decade Computational Anatomy has been the study of
structure and function in registered atlas coordinates.
Unlike Google Maps which has been based on the rigid motions with
scale for aligning coordinate systems, the underlying "alignment"
groups in CA are the infinite dimensional diffeomorphisms. For rigid
motion angular momentum plays a parsimonious roll; in diffeomorphic
motion the analogous roll is played by diffeomorphic shape momentum.
We present results from computational codes for generating diffeomorphic
correspondences between anatomical coordinate systems and their
encoding via diffeomorphic shape momentum. Statistics will be examined
for quantifying probabilistic shape momentum representations of
neuroanatomy at 1mm scale. As well we will present results on functional
and structural neuroinformatics in populations of normals and diseased
populations in registered atlas coordinates.
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Statistical Analysis on Manifolds in Medical
Image Analysis
by
Xavier Pennec
INRIA, Asclepios team, Sophia-Antipolis, France
To analyze and model the biological variability of the human anatomy,
the general method is to identify anatomically representative geometric
features (points, tensors, curves, surfaces, volume transformations),
and to describe and compare their statistical distribution in different
populations. As these geometric features most often belong to manifolds
that have no canonical Euclidean structure, we have to rely on more
elaborated algorithmical bases.
I will first describe the Riemannian structure, which proves to
be powerfull to develop a consistent framework for simple statistics
on manifolds. It can be further extend to a complete computing framework
on manifold-valued images. For instance, the choice of a convenient
Riemannian metric on symmetric positive define matrices (SPD) allows
to generalize consistently to fields of SPD matrices (e.g. DTI images)
many important geometric data processing algorithms. This allows
for instance to introduce anisotropic spatial priors in DTI estimation
or to realize statistical models of the cardiac muscle fibers.
Then I will focus on statistics on deformations. The natural extension
of the Riemannian framework is the use of right-invariant metrics
on diffeomorphisms (often called LDDMM). When used on curves and
surfaces modeled with geometric currents, the registration problem
becomes finite-dimensional thanks to the representer theorem. An
example application is the construction of a statistical model of
the remodeling of the heart in rToF. For continuous images, however,
the complexity remains very high. Dropping the metric, we propose
to use the geodesics of the canonical Cartan connection (translates
of one-parameter subgroups) for which very efficient algorithms
exist. This log-demons framework will be illustrated with the individual
and groupwise modeling of the morphological changes of the full
brain in Alzheimer's disease.
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A Survey of Compressed Sensing and Applications
to Medical Imaging
by
Justin Romberg
Georgia Tech
We will overview the fundamental results and recent theoretical
trends in compressive sensing, and discuss current state-of-the-art
models and algorithms for image reconstruction. We will present
applications in medical imaging (including accelerated MRI and ultrasound),
and discuss sparsity-based models being used for other imaging modalities
and how they might apply to medical imaging.
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Noninvasive Electrocardiographic Imaging (ECGI)
of Cardiac Electrophysiology and Arrhythmia
by
Yoram Rudy
Washington University in St Louis
A noninvasive imaging modality for cardiac electrophysiology and
arrhythmias is not yet available for clinical application.Such modality
could be used to identify patients at risk, provide accurate diagnosis
and guide therapy. Standard noninvasive diagnostic techniques, such
as the electrocardiogram (ECG) provide only low-resolution reflection
of cardiac electrical activity on the body surface.
In my presentation, I will describe the application in humans of
a new imaging modality called Electrocardiographic Imaging (ECGI)
that noninvasively images cardiac electrical activity on the heart’s
epicardial surface.
In ECGI, a multi-electrode vest (or strips) records 250 body-surface
electrocardiograms; then, using geometrical information from a CT
scan and an inverse solution to Laplace equation, electrical potentials,
electrograms, activation sequences (isochrones) and repolarization
patterns are reconstructed on the heart‘s surface.
I will show examples of imaged atrial and ventricular activation
and ventricular repolarization in the normal heart and during cardiac
arrhythmias.
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Physical Aspects of Hybrid Inverse Problems
by
John Schotland
University of Michigan
There is considerable interest in the development of optical methods
for biomedical imaging. The mathematical problem consists of recovering
the optical properties of a highly-scattering medium. This talk
will review recent work on related inverse scattering problems for
the radiative transport equation and efficient fast image reconstruction
algorithms for large data sets. Numerical simulations and experimental
data from model systems are used to illustrate the results.
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What does compressive sensing mean for X-ray
CT and comparisons with its MRI application
by
Emil Sidky
University of Chicago
This talk will trace our attempts at understanding compressive
sensing (CS) concepts in the context of X-ray computed tomography
(CT) and translating CS ideas to realize sparse-data image reconstruction
in CT.
The arrival of CS could not come at a more interesting time for
CT. Research on iterative image reconstruction applied to actual
CT systems has only recently begun due to the developments in computational
technology that allow for data processing at the gigabyte level.
From the application side, more and more CT exams are being prescribed
and there is pressure to reduce dose to the patients as evidenced
by the rapid deployment of "low-dose CT" products by the
major CT manufacturers. The promise of sparse-data image reconstruction
from CS may thus play an important role in these recent technological
developments.
I will show results with actual CT data that seem to indicate that
CS style optimization problems do indeed yield "high quality"
images from sparse projection data. I will then point out various
issues that arise in integrating iterative image reconstruction,
in general, and CS methods, specifically, into CT systems. I will
address questions such as: Which data model to use and how accurate
does it have to be? Given that object functions are continuous,
...what is meant by object sparsity? ...what is sparse data and
what is fully sampled data? How should we validate the new CS algorithms?
These questions will be addressed for CT and comparisons made with
MRI where the application of CS is more familiar.
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Low-dose three-dimensional X-ray imaging
by
Samuli Siltanen
University of Helsinki, Finland
A new kind of tomographic X-ray imaging modality is discussed,
where the patient is radiated as little as possible while recovering
enough three-dimensional information for the clinical task at hand.
The input can be only a dozen projection images collected from different
directions. Such sparse data typically represent limited-angle and
local tomography configurations and lead to severely ill-posed reconstruction
problems. This differs from traditional CT imaging, where a comprehensive
data set is collected and the (only mildly ill-posed) reconstruction
problem is solved using the classical filtered back-projection (FBP)
algorithm. The incompleteness of sparse data violates the assumptions
of FBP, leading to unacceptable reconstruction quality. However,
statistical inversion methods can be used with sparse tomographic
data. They yield clinically useful reconstructions, as demonstrated
by real-data examples related to mammography, surgical imaging and
dental imaging. Some of these methods have already entered commercial
products: see http://www.vtcube.com.
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Thermoacoustic tomography with a variable
sound speed
by
Gunther Uhlmann
University of California irvine and University of Washington
Coauthors: Plamen Stefanov
We will discuss some recent results on termoacoustic tomography
with a variable sound speed including the smooth case and the non-smooth
one, the latter motivated by brain imaging. We will also present
some numerical results based on the analytic reconstruction which
is joint work with Jianliang Qian and Hongkai Zhao.
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Photoacoustic Tomography: Ultrasonically Breaking
through the Optical Diffusion Limit
by
Lihong V. Wang
Washington University in St. Louis
We develop photoacoustic imaging technologies for in vivo early-cancer
detection and functional or molecular imaging by physically combining
non-ionizing electromagnetic and ultrasonic waves. Unlike ionizing
x-ray radiation, non-ionizing electromagnetic waves—such as
optical and radio waves—pose no health hazard and reveal new
contrast mechanisms. Unfortunately, electromagnetic waves in the
non-ionizing spectral region do not penetrate biological tissue
in straight paths as x-rays do. Consequently, high-resolution tomography
based on non-ionizing electromagnetic waves alone—such as confocal
microscopy, two-photon microscopy, and optical coherence tomography—is
limited to superficial imaging within approximately one optical
transport mean free path (~1 mm in the skin) of the surface of scattering
biological tissue. Ultrasonic imaging, on the contrary, provides
good image resolution but has strong speckle artifacts as well as
poor contrast in early-stage tumors. Ultrasound-mediated imaging
modalities that combine electromagnetic and ultrasonic waves can
synergistically overcome the above limitations. The hybrid modalities
provide relatively deep penetration at high ultrasonic resolution
and yield speckle-free images with high electromagnetic contrast.
In photoacoustic computed tomography, a pulsed broad laser beam
illuminates the biological tissue to generate a small but rapid
temperature rise, which leads to emission of ultrasonic waves due
to thermoelastic expansion. The short-wavelength pulsed ultrasonic
waves are then detected by unfocused ultrasonic transducers. High-resolution
tomographic images of optical contrast are then formed through image
reconstruction. Endogenous optical contrast can be used to quantify
the concentration of total hemoglobin, the oxygen saturation of
hemoglobin, and the concentration of melanin. Melanoma and other
tumors have been imaged in vivo. Exogenous optical contrast can
be used to provide molecular imaging and reporter gene imaging.
In photoacoustic microscopy, a pulsed laser beam is focused into
the biological tissue to generate ultrasonic waves, which are then
detected with a focused ultrasonic transducer to form a depth resolved
1D image. Raster scanning yields 3D high-resolution tomographic
images. Super-depths beyond the optical diffusion limit have been
reached with high spatial resolution.
Thermoacoustic tomography is similar to photoacoustic tomography
except that low-energy microwave pulses, instead of laser pulses,
are used. Although long-wavelength microwaves diffract rapidly,
the short-wavelength microwave-induced ultrasonic waves provide
high spatial resolution, which breaks through the microwave diffraction
limit. Microwave contrast measures the concentrations of water and
ions.
The annual conference on this topic has been doubling in size approximately
every three years since 2003 and has become the largest in SPIE’s
Photonics West as of 2009
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MRI for Management of Ventricular Arrhythmias
by
Graham Wright
Sunnybrook Health Sciences Centre, University of Toronto
Ventricular Arrhythmias are a major cause of sudden cardiac death.
Magnetic resonance imaging (MRI) has the potential to identify those
at greatest risk. In this presentation, current approaches to detection
and treatment of ventricular arrhythmias as well as evidence of
MRI’s potential clinical role are briefly reviewed. Emerging
methods to better characterize the structural substrate of ventricular
arrhythmia, notably scar and heterogeneous infarct, with MRI are
presented . This characterization has been used to customize mathematical
models of electrical propagation in the heart. The modeling results
correspond well to experimental measurements of electrical activity
in porcine hearts. Combining these tools with the development of
MRI-compatible electrophysiology systems holds the promise of guiding
ablation therapy to disrupt the arrhythmogenic substrate, yielding
more effective solutions for patients at risk of life-threatening
events.
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