6,045 research outputs found
Acoustic tomography imaging for atmospheric temperature and wind velocity field reconstruction
Owing to its non-invasive nature, fast imaging speed, low equipment cost,
scalability for a variety of measurement ranges, and ability to simultaneously
monitor both temperature and wind velocity fields, acoustic tomography has
attracted considerable interest in the field of atmospheric imaging. This thesis
aims to improve the reconstruction quality of the acoustic tomography system
for temperature and wind velocity field imaging. Focusing on this goal, the
contribution of the thesis can be summarised from the perspectives of data
collection system development, robust and accurate TOF estimation method,
and high-quality scalar and vector tomographic image reconstruction methods
for temperature and wind velocity fields respectively. Details are given below.
Firstly, in order to facilitate the experimental study of acoustic tomography
imaging, the design and evaluation of the data collection system and TOF
estimation method was presented. The evaluation results indicate that the
presented data acquisition system and TOF estimation method has good
quantitative accuracy in the lab-scale experiments.
The temporal resolution is of great significance for the real-time monitoring of
the fast-changing temperature field. To improve the temporal resolution, a
novel online time-resolved reconstruction (OTRR) method is presented, which
can reconstruct high quality time-resolved images by using fewer TOFs per
frame. Compared to state-of-the-art dynamic reconstruction algorithms such
as the Kalman filter reconstruction, the proposed algorithm demonstrated
superior spatial resolution and preferable quantitative accuracy in the
reconstructed images. These features are necessary for the real-time
monitoring of the fast-changing temperature field.
The forward modelling of most acoustic tomography problems is based on a
straight ray model, which may result in large modelling errors due to the
refraction effect under a large gradient temperature field. In order to reduce
the inaccuracy of using the straight ray model, a bent ray model and nonlinear
reconstruction algorithm is applied, which allows the sound propagation ray
paths and temperature distribution to be reconstructed iteratively from the
TOFs.
Using acoustic tomography to reconstruct large-scale temperature and wind
velocity fields, a fully parallel TOF measurement scheme is necessary. To
achieve this goal, a set of orthogonal acoustic waveforms based on the filtered
and modulated Kasami sequence is designed and a cross-correlation based
TOF estimation method is used for data collection. Besides, to overcome the
invisible field problem and improve the image quality of the wind velocity
reconstruction, a divergence-free regularised vector tomographic
reconstruction algorithm is studied. The proposed method is able to provide
accurate tomographic reconstruction of the 2D horizontal wind velocity field
from the TOF measurements.
In summary, this thesis focuses on the improvement of acoustic tomography
techniques for temperature and wind velocity fields, including the phase
corrected Akaike information criterion (AIC) TOF estimation for accurate and
robust TOF estimation, the online time-resolved reconstruction method for
real-time monitoring of the fast changing temperature field, the nonlinear
reconstruction based on the bent ray model to reconstruct the temperature
field with a large gradient, and the divergence-free regularised reconstruction
method to visualise the 2D horizontal wind velocity field
On the Adjoint Operator in Photoacoustic Tomography
Photoacoustic Tomography (PAT) is an emerging biomedical "imaging from
coupled physics" technique, in which the image contrast is due to optical
absorption, but the information is carried to the surface of the tissue as
ultrasound pulses. Many algorithms and formulae for PAT image reconstruction
have been proposed for the case when a complete data set is available. In many
practical imaging scenarios, however, it is not possible to obtain the full
data, or the data may be sub-sampled for faster data acquisition. In such
cases, image reconstruction algorithms that can incorporate prior knowledge to
ameliorate the loss of data are required. Hence, recently there has been an
increased interest in using variational image reconstruction. A crucial
ingredient for the application of these techniques is the adjoint of the PAT
forward operator, which is described in this article from physical, theoretical
and numerical perspectives. First, a simple mathematical derivation of the
adjoint of the PAT forward operator in the continuous framework is presented.
Then, an efficient numerical implementation of the adjoint using a k-space time
domain wave propagation model is described and illustrated in the context of
variational PAT image reconstruction, on both 2D and 3D examples including
inhomogeneous sound speed. The principal advantage of this analytical adjoint
over an algebraic adjoint (obtained by taking the direct adjoint of the
particular numerical forward scheme used) is that it can be implemented using
currently available fast wave propagation solvers.Comment: submitted to "Inverse Problems
A method for delineation of bone surfaces in photoacoustic computed tomography of the finger
Photoacoustic imaging of interphalangeal peripheral joints is of interest in
the context of using the synovial membrane as a surrogate marker of rheumatoid
arthritis. Previous work has shown that ultrasound produced by absorption of
light at the epidermis reflects on the bone surfaces within the finger. When
the reflected signals are backprojected in the region of interest, artifacts
are produced, confounding interpretation of the images. In this work, we
present an approach where the photoacoustic signals known to originate from the
epidermis, are treated as virtual ultrasound transmitters, and a separate
reconstruction is performed as in ultrasound reflection imaging. This allows us
to identify the bone surfaces. Further, the identification of the joint space
is important as this provides a landmark to localize a region-of-interest in
seeking the inflamed synovial membrane. The ability to delineate bone surfaces
allows us not only to identify the artifacts, but also to identify the
interphalangeal joint space without recourse to new US hardware or a new
measurement. We test the approach on phantoms and on a healthy human finger
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
Calibration Using Matrix Completion with Application to Ultrasound Tomography
We study the calibration process in circular ultrasound tomography devices
where the sensor positions deviate from the circumference of a perfect circle.
This problem arises in a variety of applications in signal processing ranging
from breast imaging to sensor network localization. We introduce a novel method
of calibration/localization based on the time-of-flight (ToF) measurements
between sensors when the enclosed medium is homogeneous. In the presence of all
the pairwise ToFs, one can easily estimate the sensor positions using
multi-dimensional scaling (MDS) method. In practice however, due to the
transitional behaviour of the sensors and the beam form of the transducers, the
ToF measurements for close-by sensors are unavailable. Further, random
malfunctioning of the sensors leads to random missing ToF measurements. On top
of the missing entries, in practice an unknown time delay is also added to the
measurements. In this work, we incorporate the fact that a matrix defined from
all the ToF measurements is of rank at most four. In order to estimate the
missing ToFs, we apply a state-of-the-art low-rank matrix completion algorithm,
OPTSPACE . To find the correct positions of the sensors (our ultimate goal) we
then apply MDS. We show analytic bounds on the overall error of the whole
process in the presence of noise and hence deduce its robustness. Finally, we
confirm the functionality of our method in practice by simulations mimicking
the measurements of a circular ultrasound tomography device.Comment: submitted to IEEE Transaction on Signal Processin
Reflection mode photoacoustic measurement of speed of sound
We present a method to determine the speed of sound in tissue using a double-ring photoacoustic sensor working in reflection mode. This method uses the cross-correlation between the laser-induced ultrasound waves detected by two concentric ring shaped sensors, while a priori information about the depth-position of the photoacoustic source is not required. We demonstrate the concept by estimating the speed of sound in water as a function of temperature. Comparison of the estimated speed with values reported in literature shows an average systematic error of 0.1% and a standard deviation of 0.1%. Furthermore, we demonstrate that the method can be applied to layered media. The method has application in the correction of photoacoustic and ultrasound images afflicted by local speed variations in tissue. Additionally, the concept shows promise in monitoring temperature changes which are reflected in speed of sound changes in tissue.\ud
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