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Prostate cancer is the most prevalent form of cancer in western men. An accurate early localization of prostate cancer, permitting efficient use of modern focal therapies, is currently hampered by a lack of imaging methods. Several methods have aimed at detecting microvascular changes associated with prostate cancer with limited success by quantitative imaging of blood perfusion. Differently, we propose contrast-ultrasound diffusion imaging, based on the hypothesis that the complexity of microvascular changes is better reflected by diffusion than by perfusion characteristics. Quantification of local, intravascular diffusion is performed after transrectal ultrasound imaging of an intravenously injected ultrasound contrast agent bolus. Indicator dilution curves are measured with the ultrasound scanner resolution and fitted by a modified local density random walk model, which, being a solution of the convective diffusion equation, enables the estimation of a local, diffusion-related parameter. Diffusion parametric images obtained from five datasets of four patients were compared with histology data on a pixel basis. The resulting receiver operating characteristic (curve area = 0.91) was superior to that of any perfusion-related parameter proposed in the literature. Contrast-ultrasound diffusion imaging seems therefore to be a promising method for prostate cancer localization, encouraging further research to assess the clinical reliability.
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Contrast-Ultrasound Diffusion Imaging
for Localization of Prostate Cancer
Maarten P. J. Kuenen*, Massimo Mischi, and Hessel Wijkstra
Abstract—Prostate cancer is the most prevalent form of cancer
in western men. An accurate early localization of prostate cancer,
permitting efcient use of modern focal therapies, is currently
hampered by a lack of imaging methods. Several methods have
aimed at detecting microvascular changes associated with prostate
cancer with limited success by quantitative imaging of blood
perfusion. Differently, we propose contrast-ultrasound diffu-
sion imaging, based on the hypothesis that the complexity of
microvascular changes is better reected by diffusion than by
perfusion characteristics. Quantication of local, intravascular
diffusion is performed after transrectal ultrasound imaging of an
intravenously injected ultrasound contrast agent bolus. Indicator
dilution curves are measured with the ultrasound scanner reso-
lution and tted by a modied local density random walk model,
which, being a solution of the convective diffusion equation,
enables the estimation of a local, diffusion-related parameter.
Diffusion parametric images obtained from ve datasets of four
patients were compared with histology data on a pixel basis. The
resulting receiver operating characteristic (curve area )
was superior to that of any perfusion-related parameter proposed
in the literature. Contrast-ultrasound diffusion imaging seems
sing method for prostate cancer localiza-
tion, encouraging further research to assess the clinical reliability.
Index Terms—Biomedical imaging, blood vessels, cancer, pa-
rameter estimation, ultrasonography.
PROSTATE cancer is the most prevalent form of cancer
in men in western countries. It accounts for 25% and
10% of all cancer diagnoses and deaths, respectively [1], [2].
Nowadays, a variety of focal therapies such as cryoablation,
brachytherapy, and high-intensity focused ultrasound, are avail-
able to efciently treat early detected and localized prostate
cancer [3]. This may prevent a radical treatment as for example
radical prostatectomy, with the associated risks of the patient
Manuscript received January 12, 2011; revised February 24, 2011; accepted
February 27, 2011. Date of publication March 10, 2011; date of current ver-
sion August 03, 2011. This work was supported by the Dutch Organization for
Scientic Research (NWO) and the Technology Foundation (STW). Asterisk
indicates corresponding author.
*M. P. J. Kuenen is with the Department of Electrical Engineering, Eind-
hoven University of Technology, 5600 MB Eindhoven, The Netherlands and
also with the Department of Urology, AMC University Hospital, 1100 DD Am-
sterdam, The Netherlands (e-mail:
M. Mischi is with the Department of Electrical Engineering, Eindhoven Uni-
versity of Technology, 5600 MB Eindhoven, The Netherlands.
H. Wijkstra is with the Department of Urology, AMC University Hospital,
1100 DD Amsterdam, The Netherlands, and also with the Department of Elec-
trical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven,
The Netherlands.
Color versions of one or more of the gures in this paper are available online
Digital Object Identier 10.1109/TMI.2011.2125981
becoming incontinent or impotent [4]. However, the limited
reliability of the available noninvasive diagnostic methods
hampers an efcient use of focal therapies.
The main noninvasive diagnostic method, assessingthe
serum prostate-specic antigen (PSA) levelinblood,hasa
high false-positive rate (about 76%) [5]. Therefore, PSA does
not enable an efcient mass screening [5] andisonlyusedfor
patient stratication prior to biopsy investigation [6]. This inva-
sive and painful investigation commonly involves taking 6–12
spatially distributed samples of the prostatewithacoreneedle.
Although the biopsy investigation is currently the most reliable
diagnostic method, it is often repeated to achieve sufcient
sensitivity [5], [6]. The limited cancer localization is another
drawback. Furthermore, a considerable fraction of all detected
carcinomas will not develop into a life-threatening disease [4].
Therefore, the risk of overdiagnosis and overtreatment, with a
related loss in quality of life, represents a major issue in current
prostate cancer care [4],[5].
These problems motivate the search for better noninvasive
methods for an early detection and localization of life-threat-
ening forms of prostate cancer. In particular, imaging methods
may reduce the number of biopsies by accurate targeting and
permit an efcient application of focal treatments.
Several imaging modalities are being evaluated for early
prostate cancer detection. While computerized tomography
(CT) seems unsuitable for diagnostic prostate imaging [6],
cancer detection sensitivity (on a patient basis) with mag-
netic resonance imaging (MRI) techniques such as diffusion
weighted imaging (73%–89%), contrast-enhanced MRI
(69%–95%), and MR spectroscopy (59%–94%) is promising
[7]. Transrectal ultrasound (TRUS) techniques are however
equally promising, and they are more suitable than MRI in
terms of cost, time, resolution, and guidance of biopsies and
focal therapies [6], [8]. Therefore, TRUS improvements could
be of great value for early prostate cancer localization.
For imaging purposes, a key indicator for prostate cancer is
angiogenesis, i.e., the formation of a dense microvascular net-
work characterized by an increased microvessel density (MVD)
[9]–[12]. Angiogenesis, which is required for cancer growth
beyond 1 mm,correlates with prostate cancer aggressiveness
(i.e., risks of extracapsular growth and development of metas-
tases) [10]–[12]. Therefore, imaging methods based on angio-
genesis detection may help to identify life-threatening forms of
prostate cancer at an early stage.
Hypothesizing a correlation between MVD and perfusion,
i.e., blood ow per tissue volume, the use of ultrasound contrast
agents (UCAs) for quantitative TRUS imaging of microvascular
perfusion has gained interest [6], [13], [14]. UCAs are dis-
persions of coated gas microbubbles that backscatter acoustic
0278-0062/$26.00 © 2011 IEEE
energy when hit by ultrasound waves [15], [16]. Despite an
improvement in biopsy targeting, contrast-enhanced TRUS
methods based on intermittent imaging and destruction-re-
plenishment techniques have however not proven sufciently
reliable to replace systematic biopsies [17]–[19].
An alternative method involves dynamic TRUS imaging of
the passage of an intravenously injected UCA bolus [6], [8],
[14]. Up until now, only few quantitative studies have been car-
ried out. These studies quantify perfusion by extraction of time
and intensity features from the measured acoustic time-inten-
sity curves [20]–[23]. However, time features do not represent
the local hemodynamic characteristics since they generally de-
pend on the entire bolus history [24], whereas intensity features
are affected by scanner settings and nonlinear ultrasound prop-
agation [25].
The reasons that the developments in quantitative perfusion
imaginghavenotresultedinreliable prostate cancer localization
may be various. In addition to limitations in the ow sensitivity,
important reasons may be linked to the complex and contradic-
tory effects of angiogenesis on perfusion [13], [22], [26], [27].
A lack of vasomotor control and the presence of arteriovenous
shunts cause a low ow resistance [13], [27], but this can be
counterbalanced by the small microvessel diameter and an in-
creased interstitial pressure, due to extravascular leakage [13],
[27]. MVD characterization by quantication of perfusion may
therefore be unreliable.
In this paper we propose contrast-ultrasound diffusion
imaging (CUDI) as an alternative noninvasive prostate cancer
localization method. CUDI is based on the hypothesis that
angiogenesis-induced changes in the microvascular architec-
ture correlate better with diffusion than with perfusion. In this
context, diffusion refers to the intravascular UCA spreading by
apparent diffusion, due to concentration gradient and ow pro-
le, and by convective dispersion, due to multipath trajectories
through the microvasculature [28]–[30]. The microvascular
architecture of a solid tumor can be viewed as a distributed
network [31], in which ow can be modeled as ow through
a porous medium [32]. Structural characteristics of porous
media determine the diffusion [29], [30]. Therefore, we hy-
pothesize intravascular UCA diffusion to be correlated with the
microvascular structure and, therefore, with angiogenesis.
Based on the UCA bolus injection technique, CUDI is a new
method for quantication of diffusion from time-density curves
(TDCs). These curves measure the image gray level versus time
at all pixels covering the prostate. By modeling the local in-
travascular UCA transport by the local density random walk
model, we provide a novel, theoretical framework to extract a
local, diffusion-related parameter from measured TDCs. CUDI
was evaluated in vivo by comparing ve diffusion parametric
images from four patients, obtained by TDC tting at each pixel,
with histology data.
A. Data Acquisition
The data acquisition was performed at the AMC University
Hospital (Amsterdam, The Netherlands), after approval was
Fig. 1. TRUS power modulation imaging of the prostate after intravenous in-
jection of a UCA bolus. The displayed frames are recorded before UCA appear-
ance (top left), at initial wash-in (top right), at peak concentration (bottom left),
and at wash-out (bottom right).
granted by the local ethics committee. Written informed con-
sent was obtained from all patients prior to their participating
in this study.
A 2.4 mL SonoVue (Bracco, Milan, Italy) UCA bolus was
injected intravenously in the patient arm. SonoVue is a dis-
persion of microbubbles coated by a phospholipid shell,
whose mean diameter is 2.5 [33]. TRUS imaging was per-
formed using an iU22 ultrasound scanner (Philips Healthcare,
Bothell, WA) equipped with a C8-4v probe. The adopted con-
trast-specic imaging mode was power modulation, at a fre-
quency of 3.5 MHz. The effective pulse length of two cycles
provided an axial resolution of 0.43 mm, while a low mechan-
ical index (MI) of 0.06 minimized microbubble disruption [15],
[16]. The compression was set to C38 and the gain was adjusted
to prevent truncation or saturation of the 8-bit gray level. All ac-
quired B-mode videos were stored in DICOM (Digital Imaging
and Communication in Medicine) format, which can be directly
input to the analysis software that we implemented in Matlab
(The MathWorks, Natick, MA). Four B-mode frames recorded
in power modulation mode are shown in Fig. 1.
B. Calibration
An accurate quantication of the UCA diffusion dynamics
based on TRUS B-mode video data requires knowledge about
how the measured gray level relates to the UCA concentra-
tion. To this end, we investigated the relation between UCA
concentration and the backscattered acoustic intensity .We
also studied the measurement and conversion of into a gray
level .
For low concentrations and MI, a linear relation between
and has been reported [34]. We performed new measurements
at the Catharina hospital (Eindhoven, The Netherlands) to verify
this relationship for the current equipment, settings, and UCA.
The setup was similar to the static calibration setup reported
in [34]. The UCA dispersions were contained in polyurethane
bags that were submerged into a water-lled basin. The basin
walls were covered by acoustic absorbers to minimize the
Fig. 2. In vitro measurement results. The gray error bars depict the acoustic
intensity measured in a xed ROI by their mean and standard deviation, whereas
the black line shows the linear approximation for SonoVue concentrations up to
1.0 mg/L.
acoustic reections from the wall. To reproduce the clinical
conditions, the ultrasound probe was positioned about 1 cm
away from the UCA dispersion. For each concentration, three
measurements were performed, from three different SonoVue
vials. The mean acoustic intensity was evaluated in a xed
region of interest (ROI) of the recorded B-mode images, with
QLAB (Philips Healthcare) acoustic quantication software.
The results are shown in Fig. 2. For SonoVue concentrations
up to 1.0 mg/L, and are linearly related ( )as
where denes the sensitivity and is the background inten-
sity due to backscatter from tissue and blood. The exact pa-
rameter values are not relevant for this study, since a linearly
related measure of is sufcient for a complete description
of the UCA diffusion dynamics. With the injected dose, the in
vivo measurements are performed within the linear calibration
range that was estimated in vitro ( Fig. 2). In fact, by consid-
ering a simple system of two mixing chambers representing the
right and left ventricles (100 and 110 mL), the concentration
would not overtake 0.84% of the injected concentration (5.0
g/mL) [35]. Taking into account a blood volume fraction in the
prostate of 2% [36], the concentration in the prostate would re-
main below 0.84 mg/L, i.e., within the linear range.
The ultrasound transducer converts the backscattered ultra-
sound waves into an electrical voltage, proportional to .After
amplication and demodulation, a compression of the signal dy-
namic range yields the quantization level ,andthe
gray mapping renders the displayed gray level [37].
This mapping is displayed on the B-mode image and can be
easily extracted and compensated for. The compression func-
tion, typically a logarithmic-like function [34], [37], is estimated
by comparing QLAB acoustic quantication results to the quan-
tization level in single-pixel ROIs. The results
show a linear relationship ( ) between and
( Fig. 3). This implies a logarithmic compression function
,inwhich is determined by the dy-
namic range of the compression function as
Fig. 3. Quantization level versus QLAB normalized acoustic intensity ,
obtained from single-pixel ROIs. The black line represents the tted logarithmic
compression function.
For the estimated dynamic range (45.73 dB), equals 24.22.
This dynamic range is sufciently large to enable an accurate
TDC quantication [37].
Combining all relations, the function that maps UCA concen-
tration to gray level can be written as
In (3), the baseline equals the quantization level
for , i.e., before the UCA appearance in the prostate.
C. Diffusion Modeling
Physical modeling of the intravascular UCA transport is re-
quired to analyze diffusion. Our analysis is based on the local
density random walk (LDRW) model [38]–[40]. This model can
provide a physical interpretation of the diffusion process, and it
accurately ts UCA indicator dilution curves (IDCs) [23], [34].
IDCs measure the UCA concentration in a xed sample volume
as function of time and can thus be obtained from TDCs via (3).
After a general introduction to the LDRW model, the local as-
pects of the diffusion process by this model are discussed.
The LDRW model characterizes the UCA transport in a
straight, innitely long tube of constant section ,inwhich
a carrier uid ows with a constant velocity ,asshownin
Fig. 4. The model assumes a Brownian motion of microbub-
bles. The concentration dynamics is then given by the
mono-dimensional convective diffusion equation as
in which and represent the distance along the tube’s main axis
and the time variable, respectively. The diffusion coefcient
is assumed constant. The boundary conditions, representing a
rapid bolus injection and the UCA mass conservation, are given
In (5), is the total injected UCA mass dose, and and
are the bolus injection time and site, respectively. The solution
is a normal distribution in space that translates at the
Fig. 4. UCA concentration dynamics by the LDRW model in an innitely-long
straight tube, with the lower curves describing the UCA concentration prole
in space for increasing time.
carrier velocity and has a variance that increases linearly with
time, as shown in Fig. 4. The LDRW formulation for the IDC
is obtained by sampling at an arbitrary detection
site ()[39][41]
The parameters ,and are dened as
where is the distance between the injection and
detection sites. The parameter equals the IDC integral, and
is the mean transit time (MTT), i.e., the average time a mi-
crobubble takes to travel the distance [39], [41]. The param-
eter is proportional to the Péclet number, which equals the
ratio of the diffusive time and the convective time [41].
In the appendix, we provide an analytical relation between and
the statistical skewness of the IDC.
By its relation to , the parameter is interesting for the char-
acterization of diffusion. However, also depends on the length
, which cannot be measured in our clinical application. As
a consequence, does not characterize diffusion locally. This
would require the denition of a local diffusion-related param-
eter that is independent of .
To describe local aspects of the UCA diffusion process, we
consider a short segment of the innitely-long tube without
making assumptions about the bolus injection. The boundary
condition (5a) representing the bolus injection is replaced by a
local boundary condition describing the spatial UCA concen-
tration prole at time , just before the bolus passage at the
detection site . In line with the LDRW model, we assume a
normally distributed initial spatial concentration prole given as
Fig. 5. Assumed UCA concentration prole in space at , i.e., just before
the bolus passage at the detection site .
with mean and variance . By adopting
the boundary condition of (8) instead of (5a), we can obtain an
analytical solution for if we assume locally con-
stant hemodynamic parameters, i.e., and
for . This interval covers the tube segment
containing the bulk of the UCA bolus at (see Fig. 5). For
,and are not relevant and may have any value.
In particular, if for , the bolus injection
time can be estimated as
The estimate is a theoretical estimate that cannot be interpreted
as the true injection time, since only holds for
. However, (9) can be used to represent the IDC as
in (6), although with a different parametrization
Using the parametrization in (10), we dene a new parameter ,
dependent on and only
After combining (11) and (6), the IDC can be expressed as
Being dependent on and only, is the local, diffusion-re-
lated parameter that we have adopted for characterization of the
microvascular structure. The parameter can be interpreted as
the local ratio between the diffusive time and the squared convec-
tive time. For low values of , the UCA concentration prole
hardly spreads while passing . This leads to a symmetric IDC,
characterized by high values of . On the other hand, high values
Fig. 6. IDC shape for various values of with .
Fig. 7. Signal conditioning and tting of a single-pixel TDC in the logarithmic
of lead to a skewed IDC, represented by small values of .
D. Parameter Estimation
Local diffusion can be estimated from measured TDCs using
the modied LDRW IDC formalization in (12) and the relation
between UCA concentration and gray level in (3).
The accuracy of the parameter estimation is determined
by the temporal characteristics of IDC noise, i.e., all signals
that the model function (6) cannot explain. Typical ultrasound
noise sources such as speckle are less signicant here: sta-
tionary noise affects only the IDC baseline and signals from
moving linear scatterers (e.g., red blood cells) are effectively
suppressed by contrast-specic imaging techniques [16]. IDC
noise is therefore mainly related to microbubbles and might
be caused by random microbubble movement into and out of
the sample volume, represented by a pixel. Such movement
produces a noise component whose variance relates directly
to the microbubble concentration, satisfying the multiplicative
character of IDC noise that was previously measured [34].
We evaluated the multiplicative character of IDC noise by
analyzing over 50 000 curves measured in vivo. Signals at fre-
quencies above 0.5 Hz were considered as noise, because the
spectrum of (6) is restricted to frequencies lower than 0.5 Hz for
a realistic range of (0.1–1.5 )and (10–50 s). We com-
pared the high-frequency noise power with the low-frequency
signal magnitude using a short-time Fourier transform with a
Hamming window of 3.2 s. For IDCs in the real domain, the
average correlation coefcient was , compared with
for log-domain TDCs. The relatively strong corre-
lation in the real domain indicates multiplicative noise. More-
over, frequencies above 0.5 Hz contained 54% of the total signal
power in the real domain, compared with 6% in the logarithmic
domain. For these reasons, parameter estimation is performed
in the logarithmic domain [42]. Although the logarithmic com-
pression affects the error metrics for TDC tting, the effects on
the estimation of and are negligible [34].
The modied LDRW TDC formalization is obtained by com-
bining (12) and (3). After compensating for the gray mapping,
and estimating and subtracting the baseline , this expression is
given as
In (13), the factor is included in , since only a relative
measure of the UCA concentration is required.
The accuracy of the parameter estimation is improved by
low-pass ltering the TDCs both in space and time (see Fig. 7).
The spatial lter design is based on the size of the smallest
microvascular networks for which local diffusion must be es-
timated. As angiogenesis is required for cancer to grow beyond
1mm [11], a reliable analysis of image regions with a radius
as small as 0.62 mm is necessary. To maintain sufcient res-
olution for accurate characterization at this scale, we adopted
a Gaussian lter with mm, whose 3-dB value is at
0.59 mm. The loss of information due to spatial ltering is lim-
ited, because the axial scanner resolution (0.43 mm) is inferior
to the B-mode pixel resolution (0.15 mm). The nonuniform spa-
tial TRUS statistics, generally due to a lower lateral resolution
at larger depths, are compensated by spatial ltering; after l-
tering, the average correlation coefcient between neighboring
pixel IDCs is independent of the scanning depth, suggesting a
uniform spatial resolution. After spatial ltering, downsampling
in both spatial dimensions by a factor three permits reducing the
computation time by 89%.
Low-pass ltering may also be performed in time, given the
scanning frame rate (about 10 Hz) as compared to the maximum
TDC frequency of 0.5 Hz. We adopted a nite impulse response
lter of 100 coefcients and a cutoff frequency of 0.5 Hz. A zero
phase shift is obtained by ltering in both forward and backward
Fig. 8. In vivo CUDI parametric image obtained from the same data as shown in Fig. 1 with histology. On the left, the diffusion parametric image is overlaid on
the ultrasound power modulation image. The parameter is displayed as a color coding; uncolored pixels are associated with t failure. The manually selected
white contour represents the prostate boundary. The corresponding fundamental ultrasound image (middle) is also shown, in which the red and green polygons
represent the adopted ROIs for cancerous and healthy tissue, respectively. Three corresponding histology slices, all showing cancer in the right peripheral zone,
are shown on the right.
A well-known issue in IDC analysis is recirculation, i.e., the
subsequent bolus passages through a selected ROI that mask
the last segment of the rst-pass IDC tail [35], [39], [43]. In
cardiovascular applications, IDC parameter estimation is often
restricted to the interval where recirculation does not
occur. The recirculation time is commonly dened as the time
when the IDC decays to 30% of its peak value [34], [43]. An ef-
fect similar to recirculation occurs also in the prostate, where
additional UCA passages may also be due to multiple feeding
arteries [44]. The effects on measured curves are, however, less
evident than in cardiovascular applications (see Fig. 7). There-
fore,weadoptamorespecic approach to dene .TheIDC
decay during an interval is given as
where is assumed without loss of generality. For large
, the IDC decays approximately exponentially, which corre-
sponds to a linear TDC decay in the logarithmic domain. There-
fore, is determined such that the linear TDC approximation
has the highest on the interval between the peak time and
. The choice of is restricted to the interval where the IDC
amplitude, in the real domain, decays to between 20% and 50%
of the peak amplitude. The inuence of TDC noise on the deter-
mination of is reduced by additional low-pass ltering with
cutoff frequency at 0.1 Hz.
LDRW model tting can be performed with a linear regres-
sion algorithm as described in [34]. However, the computational
complexity of this algorithm is high as both the parameter
and the regression interval are iteratively determined. Given the
large number of TDCs (about for each dataset, after down-
sampling), we have adopted a different method, based on the
statistical IDC moments [45]. We have extended this method by
inclusion of the estimation of (see Appendix), which makes
the method non-iterative. However, the computation of the sta-
tistical IDC moments requires the complete rst-pass IDC. For
the interval , the IDC is approximated by an exponen-
tial decay, corresponding to the linear TDC approximation. As
anal step, the obtained parameter estimates are used as ini-
tialization to t (13) to the TDC for using the Leven-
berg–Marquardt iteration [46], to increase the tting accuracy.
Up to ve iterations were used; a higher number of iterations
did not signicantly improve the results. On a Windows-based
workstation with an Intel Core2 Duo processor running at 3.16
A parametric image is produced by displaying the estimates
of for all pixels covering the prostate, as a color coding over-
laid on the B-mode image, see Fig. 8. The parametric image is
ltered with the same Gaussian lter ( mm) as the one
adopted for spatial ltering, to emphasize local diffusion at the
scale where microvascular networks can be associated with the
presence of cancer.
In some cases, shadowing effects or a lack of perfusion may
result in t failure. Then, the estimated parameters do not rep-
resent the UCA transport dynamics. If the determination coef-
cient of the TDC t in the logarithmic domain is lower than
0.75, the t is not accepted and no color is displayed.
E. Method Validation
To validate whether the diffusion-related parameter can be
estimated independently of the bolus history, the convective dif-
fusion (4) was simulated by a nite difference approximation.
The adopted boundary conditions were (5b) and (8). For various
, a large number of IDCs were obtained for increasing .We
analyzed the dependency of the diffusion-related parameters
and on both and ,bytting the IDCs with the pro-
posed algorithm.
The performance of the parameter estimation algorithm was
then evaluated by tting simulated TDCs, for
and , with additive white noise sequences. By
adding these noise sequences to the TDCs, we reproduced the
multiplicative noise character in the real domain [34]. The noise
level was 12 dB was lower than the signal power, representing
the noise level encountered in vivo.
A preliminary clinical validation of the method was also per-
formed by analysis of ve datasets registered from four pa-
tients referred for radical prostatectomy at the AMC University
Hospital (Amsterdam, the Netherlands). After radical prostate-
ctomy, the prostate was cut in slices of 4 mm thickness and a
pathologist marked the presence of cancer by histology anal-
ysis based on the level of cell differentiation [47]. We selected
the histology slice(s) corresponding to the ultrasound imaging
plane and compared them with theCUDIresults.Fig.8shows
an example parametric diffusion image with the corresponding
histology. A quantitative comparison was performed for each
dataset by selecting two ROIs containing healthy and cancerous
tissue, based on the histology. Because the histology analysis
was not specically aimed at a high-resolution validation, we
limited the ROI selection to areas larger than 50 mm that did
not show signicant variation across subsequent slices. We con-
sidered only the peripheral zone of the prostate [8], since about
70% to 80% of all cancers are found in this anatomical zone [8],
[14]. From the histogram of in each ROI, the mean value and
standard deviation of each specic class (healthy and cancerous
tissue) were used to determine the optimal tissue-classication
threshold by Bayes inference [48]. This threshold (based on all
datasets) was used to derive the optimal sensitivity and speci-
city for pixel classication. In addition, we evaluated the re-
ceiver operating characteristic (ROC) curve on a pixel basis.
A comparison was performed with different IDC parameters
proposed in the literature [14], [20]–[23], by repeating the
same tissue classication procedure. We extracted the peak
value (PV), the peak time (PT), the appearance time (AT, the
time at which the IDC achieves 5% of PV), the full-width
at half-maximum (FWHM, the time duration while the IDC
exceeds [22], [23]), the wash-in time (WIT, the time
it takes for the IDC to rise from 5% to 95% of its peak value
[23]), the area under the IDC (AUC, the LDRW parameter ),
the MTT (the LDRW parameter ) and the LDRW parameter
. The parameters PT and AT were computed with respect to
the estimated theoretical injection time . All parameters were
extracted from linearized ts to ensure that the comparison is
not affected by differences in preprocessing.
The parameters and , estimated from IDCs obtained by
simulations of the convective diffusion (4), conrmed the theo-
1%. This result conrms that ,incontrastto , is independent
of and , i.e., independent of the detection site and the his-
tory of the bolus, respectively.
The parameter estimation algorithm tted simulated TDCs
with an average and in the logarithmic
and real domains, respectively. If the complete TDC could be
used for tting, the mean relative error for was 4.33%. When
the IDC tail was excluded from the tting, as required for tting
of TDCs obtained in vivo , the mean relative error was 10.15%.
On the obtained B-mode image sequences that were com-
pared with histology, the algorithm showed an average
after ltering in space and time, which was by 18% higher
than without ltering. In these data, 89% of the pixel IDC ts
were considered sufciently accurate ( in the loga-
rithmic domain) and pixel TDC ts were included in the
comparison with histology.
In all patients, we observed that the presence of cancer was
associated with higher values of . For each parameter, the mean
value and standard deviation in healthy and cancerous tissue
are reported in Table I. We used this information to derive the
sensitivity and specicity for pixel classication, as well as the
ROC curve area. For all the considered parameters, the results
are reported in Table II.
Contrast-ultrasound diffusion imaging (CUDI) is an inno-
vative noninvasive imaging method for prostate cancer local-
ization. The passage of an intravenously injected UCA bolus
through the prostate is measured by dynamic TRUS imaging.
The TDCs obtained from all pixels covering the prostate are an-
alyzed and a parametric image, based on intravascular diffusion,
is produced.
In a preliminary clinical validation, we have compared
the cancer localization accuracy of the proposed method,
CUDI, with several quantitative indicators of perfusion. More
precisely, we compared the correspondence between several
methods for MVD characterization and the level of cell differ-
entiation, evaluated by a histology analysis.
The results show that the diffusion-related parameter has a
ROC curve that is superior to that of any other IDC parameter.
Although the sensitivity of some other parameters is higher,
is the only parameter whose sensitivity and specicity both ex-
ceed 80%. While these results are obtained on a pixel basis, the
clinical diagnosis would be based on a larger scale; currently,
carcinomas are considered clinically signicant if their size is at
least 0.5 cm [47]. Lesion classication is then based on large
amounts of pixels. A perfect pixel-based sensitivity is therefore
not strictly necessary. On the other hand, a high specicity is
essential to exclude healthy areas.
Although they are less specicthan , all IDC time parame-
ters are smaller in the cancerous areas than in healthy areas. This
is consistent with previous qualitative observations [6], [14].
Perhaps, the lower specicity of the time parameters compared
to is related to the difculty of interpreting these time parame-
ters locally at the measurement site [24]. The amplitude-related
parameters PV and AUC show relatively large variations, in
both healthy and cancerous tissue. This may be due to their de-
pendency on the amount of UCA entering the prostate or to non-
linear ultrasound propagation through UCA dispersions [25].
Imaging of intravascular diffusion is a new concept, based
on modeling the intravascular UCA transport by the convec-
tive diffusion equation. Whereas other researchers have taken
a bottom-up approach, by analyzing ow through individual
vessels and deducing the effects on vascular networks [49], we
have pursued a top-down approach, aimed at macroscopic mod-
eling of the microvascular network, similarly to [31]. In fact, we
characterize the microvascular network as similar to a porous
medium [32], whose structural characteristics are reected in
diffusion [30].
The parameter measures the ratio between diffusion and
convection. High values of , with a relatively low diffusion
with respect to convection, seem to be associated with the
presence of cancer. This relative decrease in diffusion may be
caused by an increased microvessel tortuosity. This is however
the rst study that investigates the effects of changes in the
microvascular architecture on intravascular diffusion; a better
understanding requires additional research.
The presented method for estimation of diffusion has the ad-
vantage that a local diffusion-related parameter can be es-
timated independently for each pixel. This parameter depends
only on the local, hemodynamic parameters and and does
not depend on the entire dilution history between the injection
and detection site. This novel approach is based on the assump-
tion of a Normal UCA concentration distribution in space; an
assumption that is also included in the LDRW model [38]. The
width of this distribution before the bolus passage through the
detection site, given by , determines the resolution by which
we can estimate (see Fig. 5). We have not been able to verify
the use of this assumption in an experimental in vitro setup.
However, the spatial UCA distribution in the systemic arteries is
mainly determined by the transpulmonary circulation and can be
well described by the LDRW model [38]. Moreover, the LDRW
model is reported to be the most suitable for tting IDCs mea-
sured in the microcirculation of animal models [23]. These re-
sults support the validity of the LDRW model assumptions.
The proposed method focuses on the temporal characteris-
tics of the UCA diffusion dynamics. Alternative methods can
be based also on spatial diffusion characteristics and will be in-
vestigated in the future. In this study, relatively simple linear
lters were used to improve the robustness of parameter esti-
mation. More advanced ltering methods can possibly provide
additional improvements. In this context, coherence-enhancing
diffusion ltering seems an interesting method to improve the
signal quality given the anisotropy caused by the TRUS resolu-
tion and the microvascular characteristics.
An important issue concerns the validation of CUDI, as de-
termining the position of the imaging plane with respect to the
histology planes is difcult. In fact, the imaging plane often
crosses several histology planes. The presented validation was
therefore restricted to patients whose histology did not show sig-
nicant variation across subsequent slices. We are currently in-
vestigating new strategies to improve the comparison between
imaging and histology. The validation could be improved by
comparing CUDI results directly with the MVD, rather than
with the level of cell differentiation. This approach, requiring
the use of immunohistology [10]–[12], would be more accu-
rate as CUDI aims at characterizing the microvascular structure.
An additional step in the validation may also involve the zonal
anatomy of the prostate. Here, the validation was restricted to
the peripheral zone, where the majority of cancers are found
[8], [14]. As the microvascular structure varies among different
anatomical zones of the prostate, it may also be interesting to
investigate the intravascular diffusion in different zones.
In the future, three-dimensional ultrasound imaging may
offer great advantages for the proposed method. From a clinical
perspective, the entire prostate could be studied with a single
UCA bolus injection. This would resolve an important current
issue, i.e., the selection of proper TRUS imaging planes such
that any signicant carcinoma is covered. From a technical
perspective, the UCA transport could be observed in all spa-
tial dimensions, which would open up new possibilities for
spatio-temporal analysis of intravascular UCA diffusion. More-
over, the in vivo validation would be simplied as imaging and
histology results could be compared more accurately.
In conclusion, also given the additional possibilities offered
by three-dimensional ultrasound, imaging of intravascular dif-
fusion may be a promising alternative to perfusion imaging for
the localization of prostate cancer. The intravascular nature of
UCA microbubbles makes contrast-enhanced ultrasound an at-
tractive imaging modality to assess intravascular diffusion. Fur-
thermore, the use of CUDI should not be limited to prostate
cancer; the same diffusion principles also apply to many other
forms of cancer, such as breast cancer. Further clinical studies
are however required to evaluate the clinical reliability of CUDI.
For a random variable with probability density function
(PDF) , the moments for are given as
For , the central moments are given as
To interpret the LDRW IDC formalization in (6) as a PDF, we
dene for and divide by its integral
[50]. The moments are then given as
The LDRW IDC moments, which for are denoted by ,
have been derived in [45] and [50]
In (18), equals the expectation of .If is known, the
moments and can be computed from measured IDCs.
Therefore, solving and from (18a) and (18b) provides a
noniterative method to estimate these parameters [45].
In the current study, is however unknown so we cannot
measure . We can only measure the moments , which de-
pend on . To estimate all LDRW parameters by measuring ,
we include in the moments analysis. The rst moment for
is by linearity of the expectation given as
This result can also be derived by substitution of in the
integrand of (17). Similarly, for can be derived. ,
and can then be solved from the obtained equations for ,
and . However, this system is very complicated and has
no analytical solution.
Alternatively, we can also measure the central moments
and derive expressions for in terms of ,and . By sub-
stituting , we observe that the central moments are
compensated for by the time shift by
In fact, the argument ensures that the resulting function
is independent of [see (6)]. Therefore, the moments
of are given by (18). By expanding ,the
central moments can be completely described in terms of
. In particular, and are given as
and can be solved from (21). To obtain the solution, we com-
pute the IDC skewness , i.e., the third standardized moment
of , which is a function of only
This result conrms the relation between and the IDC skew-
ness [39], [41], [43]. Solving from (22) provides an estimate
of that is independent of and . Subsequently, can be es-
timated from (21a) as
Finally, we use (19) to estimate from as
The fourth parameter is given directly by the IDC integral.
Since the IDC integral as well as the moments ,,and
can directly be computed from measured IDCs, all LDRW IDC
parameters can be estimated by (22)–(24).
In summary, we have obtained a method to estimate all
LDRW IDC parameters, including . Being noniterative,
this method has low computational requirements compared to
various iterative methods.
The authors would like to thank the Department of Pathology
of the AMC University Hospital in Amsterdam for the histology
data, and I. Herold (M.D.) and Prof. Dr. H. Korsten (M.D.) of
the Department of Anesthesiology of the Catharina Hospital in
Eindhoven for providing equipment and assistance during the
in vitro measurements.
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... After image acquisition, CUDI quantifies the abnormalities in contrast dispersion, based on a spatiotemporal analysis of the time intensity curves (TICs) extracted for each pixel on the CEUS image. These TICs show location-specific contrast intensity evolution over time [23][24][25][26]. The parametric maps resulting from CUDI can be used to visualize areas suspected for PCa. ...
... Additionally, it is well established that the PZ and TZ provide distinct probabilities for the presence of PCa [42,43]. Subsequent feature extraction will entail execution of modules that compute perfusion and dispersion parameters (by both temporal and spatiotemporal analysis of the extracted TICs), as well as other characteristics of the microvascular network by the fractal dimension, mutual information, and entropy of velocity fields [14,17,[23][24][25][26]38,44]. Young's modulus will be derived from SWE as a measure for tissue elasticity. ...
Full-text available
Introduction and hypothesis: The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design: This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview: Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis: The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary: This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.
... The preprocessed, pixel-based TICs were fitted to a new model that incorporates the secondwave phenomenon. This model is fundamentally based on the assumption of a convective-dispersion process of the NBs, as given by the modified local density random walk (mLDRW) model 6 , with the addition of a retention compartment, already proposed to describe the transport of targeted MBs 31 . We have extended this model by doubling its parameterization of the convective-dispersion process to also describe the second-wave phenomenon. ...
... Each wave is composed of one intravascular transport function and one additional retention function. The transport function is described by the mLDRW model as 6 where t 0 is the theoretical injection time, µ represents the mean transmit time, AUC denotes the area under the curve, and κ represents a local dispersion-related parameter given by κ = v 2 /D , with v being the NB intravascular velocity and D the NB intravascular dispersion. The retention function is modeled by the convolution between the intravascular function and an exponential function, representing the extravascular compartment 32,33 . ...
Full-text available
Investigation of nanobubble (NB) pharmacokinetics in contrast-enhanced ultrasound (CEUS) at the pixel level shows a unique phenomenon where the first pass of the contrast agent bolus is accompanied by a second wave. This effect has not been previously observed in CEUS with microbubbles. The objective of this study was to investigate this second-wave phenomenon and its potential clinical applications. Seven mice with a total of fourteen subcutaneously-implanted tumors were included in the experiments. After injecting a bolus of NBs, the NB-CEUS images were acquired to record the time-intensity curves (TICs) at each pixel. These TICs are fitted to a pharmacokinetic model which we designed to describe the observed second-wave phenomenon. The estimated model parameters are presented as parametric maps to visualize the characteristics of tumor lesions. Histological analysis was also conducted in one mouse to compare the molecular features of tumor tissue with the obtained parametric maps. The second-wave phenomenon is evidently shown in a series of pixel-based TICs extracted from either tumor or tissues. The value of two model parameters, the ratio of the peak intensities of the second over the first wave, and the decay rate of the wash-out process present large differences between malignant tumor and normal tissue (0.04 < Jessen-Shannon divergence < 0.08). The occurrence of a second wave is a unique phenomenon that we have observed in NB-CEUS imaging of both mouse tumor and tissue. As the characteristics of the second wave are different between tumor and tissue, this phenomenon has the potential to support the diagnosis of cancerous lesions.
... Most types of MBs, whose diameter is 1 -10 µm, are made of inert gas with low diffusivity encapsulated in a thin shell composed of biocompatible material, such as phospholipids or albumin [2]. The adoption of CEUS has enabled and facilitated a broad range of clinical applications, including cancer diagnosis [3], myocardial perfusion assessment [4], and drug delivery [5]. The utilization of MBs for cancer diagnosis is mostly based on the assessment of perfusion features of the abnormal microvasculature in the tumor as a result of angiogenesis [6]. ...
... One solution of (12) is the local density random walk (LDRW) model [33], which can be interpreted by a "random walk" of the NBs along the single vessel. By adopting specific boundaries conditions, the modified LDRW model (mLDRW) was further proposed to provide a local interpretation of the concentration variation at the detection site [3]: ...
Full-text available
With a typical 100 - 500 nm diameter, nanobubbles are a promising new-generation ultrasound contrast agent that paves ways for several applications, such as efficient drug delivery, molecular imaging, and assessment of vascular permeability. Due to their unique physical properties, nanobubbles exhibit distinct in vivo pharmacokinetics. We have shown that the first pass of the nanobubble bolus is usually accompanied by the appearance of a second bolus (wave) within a time range of about 15 minutes. Such phenomenon, to the best of our knowledge, has never been observed with conventional microbubbles and smaller molecular contrast agents used in MRI and CT. In a previous study, we showed the potential of this phenomenon in supporting cancer diagnosis. This study focuses on developing a new compartmental pharmacokinetic model that can be used to interpret the second-wave phenomenon. With this model, we can analyze more in-depth the roles of several physiological factors affecting the characteristics of the second-wave phenomenon.
... In human medicine, several papers and reviews have been published on the clinical application of CEUS in the discovery, localization, and assessment of treatment for prostate cancer [3,[59][60][61][62][63][64][65][66][67]. ...
Full-text available
Simple Summary Contrast-enhanced ultrasound (CEUS) has been widely applied for reproductive imaging in humans and animals. This structured literature review aims to analyze the usefulness of CEUS in canine reproduction. Articles from 1990 to 2022 about CEUS in canine testicles, prostate, uterus, placenta, and mammary glands were searched on PubMed and Scopus. Thirty-six total results were found. The analysis of these works enlightened the usefulness of CEUS in testicular abnormalities and neoplastic lesions, except for characterizing tumors. CEUS in dogs was studied in animal models for human prostatic cancer treatment, while in veterinary medicine it was used to study prostatic vascularization and to assess prostatic diseases, showing good specificity for adenocarcinomas. CEUS differentiated the follicular phases in ovaries. In CEH-pyometra syndrome, it differentiated endometrium and cysts, and highlighted angiogenesis. CEUS was shown to be safe in pregnant dogs and was able to assess normal and abnormal fetal–maternal blood flow and placental dysfunction. In normal mammary glands, CEUS showed vascularization only in diestrus, with differences between mammary glands. CEUS was not specific for neoplastic versus non-neoplastic masses and for benign tumors, except for complex carcinomas and neoplastic vascularization. Works on CEUS showed its usefulness in several pathologies as a non-invasive, reliable diagnostic tool. Abstract The use of contrast-enhanced ultrasound (CEUS) has been widely reported for reproductive imaging in humans and animals. This review aims to analyze the utility of CEUS in characterizing canine reproductive physiology and pathologies. In September 2022, a search for articles about CEUS in canine testicles, prostate, uterus, placenta, and mammary glands was conducted on PubMed and Scopus from 1990 to 2022, showing 36 total results. CEUS differentiated testicular abnormalities and neoplastic lesions, but it could not characterize tumors. In prostatic diseases, CEUS in dogs was widely studied in animal models for prostatic cancer treatment. In veterinary medicine, this diagnostic tool could distinguish prostatic adenocarcinomas. In ovaries, CEUS differentiated the follicular phases. In CEH-pyometra syndrome, it showed a different enhancement between endometrium and cysts, and highlighted angiogenesis. CEUS was shown to be safe in pregnant dogs and was able to assess normal and abnormal fetal–maternal blood flow and placental dysfunction. In normal mammary glands, CEUS showed vascularization only in diestrus, with differences between mammary glands. CEUS was not specific for neoplastic versus non-neoplastic masses and for benign tumors, except for complex carcinomas and neoplastic vascularization. Works on CEUS showed its usefulness in a wide spectrum of pathologies of this non-invasive, reliable diagnostic procedure.
... Here, we developed two pharmacokinetic compartmental models of PSMA-NBs in the dual-tumor mouse model with the input function directly or indirectly approximated. The first model is based on the description of the arterial input function as an mLDRW model, which has shown to reasonably describe the UCA dispersion across the microvasculature in PCa 15,36 . This model needs no additional measurement on reference tissue, but the cost is a higher risk of overfitting given the increased number of parameters. ...
Purpose: Contrast-enhanced ultrasound (CEUS) by injection of microbubbles (MBs) has shown promise as a cost-effective imaging modality for prostate cancer (PCa) detection. More recently, nanobubbles (NBs) have been proposed as novel ultrasound contrast agents. Unlike MBs, which are intravascular ultrasound contrast agents, the smaller diameter of NBs allows them to cross the vessel wall and target specific receptors on cancer cells such as the prostate-specific membrane antigen (PSMA). It has been demonstrated that PSMA-targeted NBs can bind to the receptors of PCa cells and show a prolonged retention effect in dual-tumor mice models. However, the analysis of the prolonged retention effect has so far been limited to qualitative or semi-quantitative approaches. Method: This work introduces two pharmacokinetics models for quantitative analysis of time-intensity curves (TICs) obtained from the CEUS loops. The first model is based on describing the vascular input by the modified local density random walk (mLDRW) model and independently interprets TICs from each tumor lesion. Differently, the second model is based on the reference-tissue model, previously proposed in the context of nuclear imaging, and describes the binding kinetics of an indicator in a target tissue by using a reference tissue where binding does not occur. Results: Our results show that four estimated parameters. Conclusion: These promising results encourage further quantitative analysis of targeted NBs for improved cancer diagnostics and characterization. This article is protected by copyright. All rights reserved.
... Currently, to improve the conventional two-dimensional (2D) CEUS, the computeraided quantification of contrast-ultrasound diffusion imaging (CUDI) was demonstrated. CUDI provides several parametric maps of wash-in rate generated from CEUS recordings, based on which the software can automatically estimate the heterogeneity of the enhancement and draw the areas with abnormal enhancement on a 3D model, which can be later utilized as the ROIs for targeted biopsy [78,79]. This method potentially allows a decrease in the user dependency, speed-up of reading, and improved accuracy. ...
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The purpose of this review is to present the current role of ultrasound-based techniques in the diagnostic pathway of prostate cancer (PCa). With overdiagnosis and overtreatment of a clinically insignificant PCa over the past years, multiparametric magnetic resonance imaging (mpMRI) started to be recommended for every patient suspected of PCa before performing a biopsy. It enabled targeted sampling of the suspicious prostate regions, improving the accuracy of the traditional systematic biopsy. However, mpMRI is associated with high costs, relatively low availability, long and separate procedure, or exposure to the contrast agent. The novel ultrasound modalities, such as shear wave elastography (SWE), contrast-enhanced ultrasound (CEUS), or high frequency micro-ultrasound (Mi- croUS), may be capable of maintaining the performance of mpMRI without its limitations. Moreover, the real-time lesion visualization during biopsy would significantly simplify the diagnostic process. Another value of these new techniques is the ability to enhance the performance of mpMRI by creating the image fusion of multiple modalities. Such models might be further analyzed by artificial intelligence to mark the regions of interest for investigators and help to decide about the biopsy indications. The dynamic development and promising results of new ultrasound-based techniques should encourage researchers to thoroughly study their utilization in prostate imaging.
Blood flow to skeletal muscles is an important determinant for delivery of oxygen and nutrients and removal of metabolic by-products, thereby facilitating muscle health and contractile function. Reflecting this, impairments in muscle blood supply occurring with advancing age or disease can restrict functional ability and be detrimental to overall health. Multiple methods have been used to measure muscle blood flow in both clinical and research environments, each with advantages and limitations to their use. Extensively used, contrast-enhanced ultrasound is a relatively simple, minimally invasive method using intravenously injected microbubbles to directly record perfusion activity in the skeletal muscle microvasculature. However, despite the relative ease of this technique and the clear utility of information on skeletal muscle blood flow when studying the neuromuscular system, research in this space is currently limited. The development of methodologies for measuring skeletal muscle blood flow has future potential for many applications in neuromuscular research.Key wordsSkeletal muscle Contrast-enhanced ultrasound Perfusion Blood flow
The 1 mm long worm Caenorhabditis elegans is a free-living nonparasitic nematode found around the world. It offers a simple example of animal biology. The striking level of homology between C. elegans and mammals at the genetic, cellular, tissue, and organ level means that the worm offers a unique opportunity to explore various aspects of development. By working with C. elegans, it is possible to link molecular, cellular, and tissue level mechanisms with movement and behavior. This chapter aims to provide a brief introduction to C. elegans and describes the main approaches that assess the physical performance of C. elegans using methods analogous to those used in human performance assessment, such as cardiorespiratory endurance, muscle strength, and body composition. Recent advances have allowed such measurements to be obtained in nematodes, and the experiments described here allow physical performance or fitness to be measured in C. elegans as a proxy for humans, which also provides a means by which to investigate how age, diet, drugs, and the environment may affect muscle biology. This chapter provides an introductory guide to those who are not currently in the nematode field but are interested in using C. elegans to explore the structure and function of muscle with a view to elucidate human neuromuscular function, which is essential if we are to fully understand human health and disease.Key words C. elegans MitochondriaMuscleOxygen consumptionMovementBurrowingPluronic gelMobility
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Contrast-enhanced imaging has grown significantly in the past two decades. Technology has evolved from imaging based on linear principles to elaborate pulsing and microbubble-specific detection strategies. This review provides a broad overview of the research published on these topics, emphasizing the progress made, current challenges, and future research considerations. We cover the physical and conceptual underpinnings of imaging based on ultrasound contrast agents, focused on pulsing and detection strategies. The techniques proposed are categorized according to the underlying fundamental physical and signal processing principles. We revisit methods that were previously only of academic interest and may now be clinically feasible with advances in computation and hardware. We discuss unmet challenges and opportunities originating from developments in other sub-fields of ultrasound imaging to enable wider clinical adoption of contrast-enhanced ultrasound.
Cardiac output is often measured by indicator dilution techniques, usually based on dye or cold saline injections. Developments of more stable ultrasound contrast agents (UCA) are leading to new noninvasive indicator dilution methods. However, several problems concerning the interpretation of dilution curves as detected by ultrasound transducers have arisen. This paper presents a method for blood flow measurements based on UCA dilution. Dilution curves are determined by real-time densitometric analysis of the video output of an ultrasound scanner and are automatically fitted by the Local Density Random Walk model. A new fitting algorithm based on multiple linear regression is developed. Calibration, that is, the relation between videodensity and UCA concentration, is modelled by in vitro experimentation. The flow measurement system is validated by in vitro perfusion of SonoVue contrast agent. The results show an accurate dilution curve fit and flow estimation with determination coefficient larger than 0.95 and 0.99, respectively.
Angiogenesis, the formation of new blood vessels, has been suggested to provide important prognostic information in prostate cancer. The aim of this study was to investigate, whether microvessel density (MVD) at diagnosis was correlated with disease-specific survival in a non-curative treated population of prostate cancer patients. MVD was immunohistochemically (factor VIII-related antigen) quantified in archival tumours obtained at diagnosis in 221 prostate cancer patients. The maximal MVD was quantified inside a 0.25 mm 2 area of the tumour and the median MVD was 43 (range 16-151). MVD was statistically significantly correlated with clinicopathological characteristics and disease-specific survival. A multivariate analysis demonstrated that MVD was a significant predictor of disease-specific sur-vival in the entire cancer population, as well as in the clinically localized cancer population. These findings suggest that quantitation of angiogenesis reflects the spontaneous clinical outcome of prostate cancer.
The efficacy in cancer treatment of novel therapeutic agents such as monoclonal antibodies, cytokines and effector cells has been limited by their inability to reach their target in vivo in adequate quantities. Molecular and cellular biology of neoplastic cells alone has failed to explain the nonuniform uptake of these agents. This is not surprising since a solid tumour in vivo is not just a collection of cancer cells. In fact, it consists of two extracellular compartments: vascular and interstitial. Since no blood-borne molecule or cell can reach cancer cells without passing through these compartments, the vascular and interstitial physiology of tumours has received considerable attention in recent years. Three physiological factors responsible for the poor localization of macromolecules in tumours have been identified: (i) heterogeneous blood supply, (ii) elevated interstitial pressure, and (iii) large transport distances in the interstitium. The first factor limits the delivery of blood-borne agents to well-perfused regions of a tumour; the second factor reduces extravasation of fluid and macromolecules in the high interstitial pressure regions and also leads to an experimentally verifiable, radially outward convection in the tumour periphery which opposes the inward diffusion; and the third factor increases the time required for slowly moving macromolecules to reach distal regions of a tumour. Binding of the molecule to an antigen further lowers the effective diffusion rate by reducing the amount of mobile molecule. Although the effector cells are capable of active migration, peculiarities of the tumour vasculature and interstitium may also be responsible for poor delivery of lymphokine activated killer cells and tumour infiltrating lymphocytes in solid tumours. Due to micro- and macroscopic heterogeneities in tumours, the relative magnitude of each of these physiological barriers would vary from one location to another and from one day to the next in the same tumour, and from one tumour to another. If the genetically engineered macromolecules and effector cells, as well as low molecular weight cytotoxic agents, are to fulfill their clinical promise, strategies must be developed to overcome or exploit these barriers. Some of these strategies are discussed, and situations wherein these barriers may not be a problem are outlined. Finally, some therapies where the tumour vasculature of the interstitium may be a target are pointed out.
The effect of an imposed electromagnetic field on forced convection in porous media is analyzed in this work. The transient Maxwell's equations are solved to simulate the electromagnetic field inside the waveguide and within a porous medium. The Brinkman–Forchheimer extended Darcy (generalized model) equations are used to represent the flow fluid inside a porous medium. The local thermal non-equilibrium (LTNE) is taken into account by solving the two-energy equation model for fluid and solid phases. Computational domain is represented for a range of Darcy number from 10 À5 to 10 À7 and dimen-sionless electromagnetic wave power P ⁄ from 0 to 1600, and dimensionless electromagnetic wave frequency f ⁄ from 0 to 8. The effect of variations of the pertinent electromagnetic field parameters in affecting the flow and thermal fields and the Nusselt number are analyzed. This investigation provides the essential aspects for a fundamental understanding of forced convection in porous media while experiencing an applied electromagnetic field such as applications in the material-processing field.