15,311 research outputs found
Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
An optimized ultrasound detector for photoacoustic breast tomography
Photoacoustic imaging has proven to be able to detect vascularization-driven
optical absorption contrast associated with tumors. In order to detect breast
tumors located a few centimeter deep in tissue, a sensitive ultrasound detector
is of crucial importance for photoacoustic mammography. Further, because the
expected photoacoustic frequency bandwidth (a few MHz to tens of kHz) is
inversely proportional to the dimensions of light absorbing structures (0.5 to
10+ mm), proper choices of materials and their geometries, and proper
considerations in design have to be made for optimal photoacoustic detectors.
In this study, we design and evaluate a specialized ultrasound detector for
photoacoustic mammography. Based on the required detector sensitivity and its
frequency response, a selection of active material and matching layers and
their geometries is made leading to a functional detector models. By iteration
between simulation of detector performances, fabrication and experimental
characterization of functional models an optimized implementation is made and
evaluated. The experimental results of the designed first and second functional
detectors matched with the simulations. In subsequent bare piezoelectric
samples the effect of lateral resonances was addressed and their influence
minimized by sub-dicing the samples. Consequently, using simulations, the final
optimized detector could be designed, with a center frequency of 1 MHz and a -6
dB bandwidth of ~80%. The minimum detectable pressure was measured to be 0.5
Pa, which will facilitate deeper imaging compared to the currrent systems. The
detector should be capable of detecting vascularized tumors with resolution of
1-2 mm. Further improvements by proper electrical grounding and shielding and
implementation of this design into an arrayed detector will pave the way for
clinical applications of photoacoustic mammography.Comment: Accepted for publication in Medical Physics (American Association of
Physicists in Medicine
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
A crucial limitation of current high-resolution 3D photoacoustic tomography
(PAT) devices that employ sequential scanning is their long acquisition time.
In previous work, we demonstrated how to use compressed sensing techniques to
improve upon this: images with good spatial resolution and contrast can be
obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total
variation regularization are used. Now, we show how a further increase of image
quality can be achieved for imaging dynamic processes in living tissue (4D
PAT). The key idea is to exploit the additional temporal redundancy of the data
by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a
two-dimensional numerical phantom will be used to illustrate the main
properties of this recently developed
joint-image-reconstruction-and-motion-estimation framework, measured data from
a dynamic experimental phantom will also be used to demonstrate their potential
for challenging, large-scale, real-world, three-dimensional scenarios. The
latter only becomes feasible if a carefully designed combination of tailored
optimization schemes is employed, which we describe and examine in more detail
High frame-rate cardiac ultrasound imaging with deep learning
Cardiac ultrasound imaging requires a high frame rate in order to capture
rapid motion. This can be achieved by multi-line acquisition (MLA), where
several narrow-focused received lines are obtained from each wide-focused
transmitted line. This shortens the acquisition time at the expense of
introducing block artifacts. In this paper, we propose a data-driven
learning-based approach to improve the MLA image quality. We train an
end-to-end convolutional neural network on pairs of real ultrasound cardiac
data, acquired through MLA and the corresponding single-line acquisition (SLA).
The network achieves a significant improvement in image quality for both
and line MLA resulting in a decorrelation measure similar to that of SLA
while having the frame rate of MLA.Comment: To appear in the Proceedings of MICCAI, 201
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Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
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