IEEE Signal Processing - March 2018 - 173

temporal dynamics of the heating experiment and the appearance of the coil
segment in B-mode. In fact, the crescent
appears to be more pronounced in the
postprocessed frames compared to the
RESF-only temperature estimates.
While it produced significant improvements, POCS did not remove all
artifacts, including tail-like patterns on
both sides of the heated region. These are
reminiscent of the thermoacoustic lens
artifact reported in [2] and [6]. In addition to the thermoacoustic lens effect,
one can also see regions of localize apparent heating below the heated region.
Some of these artifacts are due to reverberations in the RF echo data, especially
in regions where the signal-to-noise ratio
(SNR) is low.
In summary, the results demonstrate
the high sensitivity of UST in detecting and spatially mapping temperature
change from minute frame-to-frame
echo shifts in heterogeneous tissue medium. In numerous similar experiments
and other settings described in the literature, rtUST was shown to capture the
dynamics of temperature change accurately. However, the specificity of UST is
compromised due to echo-shift estimation errors resulting from imaging beam
distortion due to thermal lensing and
echo reverberations in low SNR regions.

Concluding remarks
and future outlook
We now have a wealth of data supporting the models for temperature imaging using pulse-echo US. These models
need to be refined and validated in the
context of quantitative estimation of
temperature change. One possible approach is the use of extended Kalman
filtering based on the spatiotemporal
integro-differential equation derived in
[3]. The Kalman filter approach was
used to regularize the temperature fields
in MR thermometry [12].
Spatiotemporal Kalman filtering and
similar statistical signal processing tools
are also necessary for the estimation and
separation of large tissue deformation
effects in organs like the heart and liver.
The range of motion and deformation in
these organs provides significant challenge, but the advent of high frame rate

US technology (2-D and three-dimensional) could be the key to addressing
this challenge. The multistream architecture described previously supports
speckle tracking at frame rates up to of
1,000 fps, which helps to capture the
tissue deformation more accurately by
minimizing signal decorrelation effects
[8]. The design of the Kalman filter as
well as the required statistics could be
informed by rtUST.
In addition to the illustrative results
shown in this article, rtUST has been
demonstrated in vivo [3] in the presence
of tissue motion and deformation. Adaptive spatial filtering was successfully
used for motion compensation albeit in a
small animal model. These results provide further validation of the applicability of the models, especially the RESF,
to realistic in vivo conditions. However,
important challenges remain, and the
answers to these are firmly in the realm
of advanced real-time signal processing. The most significant challenge to
UST is the lack of specificity due to the
thermal lensing effect, especially when
the extent of the heated region is large.
Advanced dereverberation methods
could be used as part of the prefiltering block shown in Figure 1(b). More
importantly, pre- and postbeamforming
methods to compensate for the distortion in the imaging beam by thermal
lensing are needed. This may require
tomographic reconstruction from pulseecho data, which may or may not materialize. A statistical framework for the
errors in echo-shift estimation could
improve the performance of postprocessing algorithms such as POCS.
In this article, we emphasized the
echo-shift methods because they led to
imaging equations well suited for rtUST
using FPGA and GPU platforms. However, it was also shown that temperature
change can be measured through frequency shift estimation [11] based on
(9) or the CBE [5]. A direct comparison
between the echo and frequency shift
methods revealed that the two models produce different artifacts, which
resulted in improved temperature estimation by compounding. Multiparameter estimation methods would be needed
to improve the robustness of temperaIEEE Signal Processing Magazine

March 2018

ture estimation. The multistream architecture shown in Figure 1 was designed
with this in mind.
Addressing the large tissue deformation challenge and removing or reducing displacement estimation errors will
increase the value of UST in the guidance and monitoring of thermal therapy
procedures. This opens the door to challenging, high-impact applications such
as the monitoring of the metabolic rate
and imaging inflammation using thermometry. These are grand challenges in
medical imaging.

Acknowledgments
Emad S. Ebbini acknowledges Dr. Ralf
Seip who worked on the very first experiment to validate the approach. National Institutes of Health grant R01
NS098781 provides funding for rtUST
in the context of image-guided transcranial-focused US.

Authors
Emad S. Ebbini (emad@umn.edu)
received his B.Sc. degree in electrical
engineering and communications from
the University of Jordan in 1985 and his
M.S. and Ph.D. degrees in electrical engineering from the University of Illinois
Urbana-Champaign in 1987 and 1990,
respectively. He is a professor of electrical and computer engineering at the
University of Minnesota Twin Cities. He
is a Fellow of the IEEE.
Claudio Simon (claudio.simon@
philips.com) received his B.Sc. degree
in electrical engineering in 1990 and
his M.Sc. degree in mechanical engineering in 1993 from Universidade de
Sao Paulo, Brazil. He received his
Ph.D. degree in electrical engineering
in 1998 from the University of
Michigan Ann Arbor. He is the R&D
project management office director
of ultrasound at Philips Healthcare in
Seattle, Washington.
Dalong Liu (liuxx293@umn.edu)
received his B.Sc. and M.Sc. degrees in
biomedical engineering from Zhejiang
University, Hangzhou, China, in 2001
and 2004, respectively. He received his
Ph.D. degree in biomedical engineering
from the University of Minnesota Twin
Cities in 2010. His research interests
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Table of Contents for the Digital Edition of IEEE Signal Processing - March 2018