IEEE Signal Processing - March 2018 - 170

that (9) gives a sensitivity factor of
~0.05%/ ° (percent shift in resonance).

Real-time implementation of UST
The rtUST algorithm implementation is
illustrated in Figure 1, which shows a
GPU/CUDA-enabled multistream architecture in (a). This system is representative of the state of the art in rtUST in that
it supports parallel tasks on sequences
of RF frames to meet the real-time constraints. For example, in each stream processor there are three threads that handle
interprocess messaging and data processing, respectively. A stream processor can
be assigned to any GPU in the system
and is fully reconfigurable on the fly using shared memory. Any application
which supports calling C functions (e.g.,
MATLAB, Octave, or NumPy) can interact with the pipeline and act as an external engine.
Figure 1(b) shows the tracking pipeline that operates on the RF echo data
(from an imaging scanner or the External Engine). The RF data is optionally
prefiltered before speckle tracking is
performed, followed by 2-D separable
FIR filtering to generate thermography
imaging frames (temperature data). The
tracking pipeline optionally connects
to a real-time, closed-loop temperature
control at relatively high frame rates.
The current system supports 1,000 fps.
The benefits of high frame rates are twofold: 1) minimize frame-to-frame echo
decorrelation and maintain the quality
of the echo-shift estimates and 2) capture the tissue motion and deformation
components together with the temperature-induced component at high rate to
allow signal separation algorithms.

Experimental validation
in tissue media
Tissue-mimicking phantom results
Numerous reports on UST and temperature imaging used tissue-mimicking
phantoms to provide initial validation of
the algorithms used [2]-[7]. Tissue-mimicking phantoms can be made homogeneous to provide validation of the imaging
equations in a controlled, well-characterized environment. They can also be made
with different levels of contrast for certain
170

acoustic and thermal parameters to answer basic questions regarding robustness
of the estimation algorithms. In [2] and
[4], carefully planned and executed experiments using tissue-mimicking phantoms provided clear validation for the
d -ESF approach for small temperature
changes and identified its limitations.
Similar validation results from the RESF
were shown in [3].

UST in inhomogeneous tissue media
While US is currently the leading imageguidance modality of radio-frequency
(RF) ablation, it is primarily used to provide echographic B-mode images for visualization purposes. Several groups have
proposed the use of UST and other semiquantitative methods such as elastography. Example results from controlled RF
heating experiments are presented here
to illustrate the promise of rsUST in this
important medical application. They also
illustrate the current challenges facing
UST when the target tissue is inhomogeneous and the size of the heated region is
relatively large.
Figure 2(a) illustrates the basic setup
with a helical coil used as a heating
source. The active segment of the coil
is colored in red while the blue is insulated. The beamformed RF frames were
acquired from a plane orthogonal to the
coil axis approximately at the center of
the active segment. A thermocouple was
used to provide temperature feedback for
closed-loop control at the center of the
target volume. The controlled heating
procedure was as follows:
■ Data collection was started at 0 s
acquiring one frame every 2 s, i.e.,
0.5 fps. Each frame was composed of
N L = 128 image lines (38 mm laterally) with N S = 1, 900 samples of
echo data from 3.85 to 76.96 mm
axially.
■ Heating began at 20 s, just after frame
10. Baseline temperature was 18.7 °C.
■ Closed-loop control engaged at 80 s
with a set point of 40 °C.
■ Heating was completed at 320 s, just
after frame 160.
■ US data collection stopped at 400 s,
just after frame 200.
■ RF frames were saved in MATLAB
format for offline processing.
IEEE Signal Processing Magazine

March 2018

Frame-to-frame displacements were
computed using the GPU-enabled speckle tracking algorithms described in [7]. A
GTX770 (NVIDIA) was used to process the 1, 900 # 128 # 201 data set to
produce a displacement data set of size
1, 900 # 128 # 200, i.e., a displacement
field sampled at the same level as the
RF data. MATLAB served as the
external engine calling the speckle tracking engine in Figure 1(a) to produce the
displacement fields with average execution time of 0.8684 s or . 230 fps.
The RESF was applied to the data in
MATLAB followed by five iterations of
the POCS algorithm (8) for each frame
(typically converged in three iterations).
Two-dimensional temperature profiles
at time Ti were computed as i (x, z, Ti)
= i (x, z, Ti - 1) + di (x, z, Ti) and overlayed on the gray-scale B-mode image
(acquired at Ti ). Example B-mode image
and corresponding 2-D temperature map
are shown in Figure 2(b) and (c). The "+"
marker just above a visible segment of the
helical coil in B-mode. The estimated temporal temperature profile at marker location is shown in the top of (d). It reflects the
dynamics of the controlled heating experiment, i.e., start of heating, start of control,
and cessation of heating. Furthermore,
the estimated dT at the set point was
close to the true DT for this experiment.
The bottom plot of Figure 2(d) illustrates the echo-shift phenomenon that
was utilized in estimating the temperature change. It shows a 2-mm segment
of the echo data centered axially at the
"+" marker in the B-mode image. At this
location, the echo gradually shifts toward
the transducer by approximately 250 nm
in about 114 s. The frame-to-frame correlation between the echo segments at
this location remained high throughout
the experiment despite the relatively low
frame rate. In general, however, the echo
locations as well as the echogenicity
change significantly over the course of
the experiment.
Figure 3 illustrates the advantages
and limitations of the UST algorithm
described previously. Both (a) and (b)
show a montage of ten frames of estimated 2-D temperature before, during,
and after the controlled RF heating.
Figure 3(a) shows the 2-D temperature

Table of Contents for the Digital Edition of IEEE Signal Processing - March 2018