High Frequency Ultrasound Monitoring of
Structural Changes in Cells and Tissue
Adam Tunis1,2, Anoja Giles2, David McAlduff1,2, David Spurrell1,2, Mehrdad Hariri2,
Rama Khokha1,2,3, John Hunt1,2, Gregory Czarnota2,4, Michael Sherar1,2 and Michael Kolios1,4
1 Department
Day 3
Day 0
1. Objective
3. In-Vitro Results
Our lab has previously shown it possible to differentiate between pellets of apoptotic
and normal cells in-vitro using the integrated backscatter (IB) of high frequency
ultrasound (HFUS). While this technique is effective in a cell model, in a more
complicated tissue model making this distinction is more difficult as the IB can be
affected by many variables. As a possible complementary technique we are
investigating the use of the statistics of the envelope of the backscatter to detect
changes occurring in cells during cell death. This technique is evaluated using an invitro cell model and an in-vivo tissue model.
As can be seen from the histology (Fig. 3), there is a visible increase in the number of
cells with structural changes to the nuclei. The B-scans show a large increase in the
backscattered intensity as the percentage of treated cells increases. This is reflected
in the changes to the histograms of the data (Fig. 4a). Both the GG and Rayleigh PDFs
provide reasonable fits to the data based on the KS test for all pellets. The GG
distribution fit parameters show sensitivity to the percent of treated cells in the
pellet. There is good agreement between the parameters obtained from the
experimental data and the those from the simulated data (Fig. 4b-d).
Day 4
Digital
Photo
b)
a)
H&E
Stain
B-scan
40 MHz
a)
b)
f)
1.5
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.
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%
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2 4.5
Intensity [A.U.]
0
Intensity [A.U.]
Gamma c Parameter - Mixture of 24hr Treated and Untreated %AML
Gamma
a pellet[A.U.]
[A.U.]
a (pellet)
Gamma
Number of Counts [A.U.]
2
0.08
0.06
0.04
0.02
1.8
25
1.2
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15
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Intensity [A.U.]
60
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90
Intensity [A.U.]
1.6
20
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60
Percent Treated
80
100
0
120
4
0
- Pellet
%
- Simulation
6
0
%
b)
Kolmogorov-Smirnov Goodness of Fit - Mouse
Mammary Involution
Kolmogorov-Smirnov
Goodness
of Fit
0.35
7
0.3
6
- Rayleigh
- Gen. Gamma
- Significance Level
0.25
Gamma
C Average
Involution
Gamma
a, -cMouse
& vMammary
Parameters
1.8
- Gamma a
- Gamma v
- Gamma c
1.6
1.4
0.2
0.15
0.1
0.05
5
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Day
Day of Involution
1
2
3
4
5
6
7
Day ofDayInvolution
Figure 6: Kolmogorov-Smirnov goodness of fit values (a) and Generalized Gamma fit parameters with
95% confidence intervals (b) for HFUS data from mouse mammary tissue. The goodness of fit shows
the data being most Rayleigh-distributed at day 2, agreeing with the fit parameters which show a
trend in the GG towards the Rayleigh distribution between days 2-3.
Percent Treated
d)
Gamma v Parameter - Mixture of 24hr Treated and Untreated AML
Gamma v Parameter
5. Conclusions and Future Work
1.3
1.2
1.1
1
0.9
0.8
0
0
- Pellet
- Simulation
The results demonstrate that signal statistics are affected by structural changes
during cell death. Data from the cell pellets agreed well with theoretical
simulations. This information may thus be useful to isolate which changes in cells are
causing the changes in signal statistics. Data collected from the mouse mammary
tissue show a trend toward Rayleigh statistics at day 2 of involution, demonstrating
that HFUS signal statistics can be used to monitor cell death in in-vivo tissue models.
0.7
1
0.6
0
0
20
40
60
80
100
120
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20
40
60
80
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PercentTreated
Treated
Percent
Percent
Treated
Percent
Treated
0
%
Figure 4: Histograms of the HFUS backscatter
data from pellets show a trend towards a broader peak
1.4
Figure 1: Simulated data generated to model the effect of ultrasound backscatter from treated and
untreated cells. Schematic diagram of the nuclear condensation. Each nucleus was modelled as a set
of 16 point scatterers. To simulate the cellular changes the nuclear volume shrinks, then fragments
into groups of four, then 16 individual scatterers (a). The modelled signal increases in intensity as the
level of disorder of the scatterers increases (b). The histograms of the simulated signals show large
differences between the two simulated populations (c).
b)
2
1.4
1.4
2.4
2.2
30
0.4
Gamma
[A.U.]
v v[A.U.]
Gamma
- Treated
- Untreated
Gamma
c [A.U.]
c [A.U.]
Gamma
Number of Counts [A.U.]
b)
2.6
35
1.6
5
Gamma c Parameter
2.8
- Simulation
1.8
- 0%
0
- 2.5%
- 20%
%
- 40%
- 60%
- 100%
Histogramsfor
for Simulated
HFUSSignals
Signal
Histograms
Simulated
0.1
40
- Pellet
2.5
0.12
Gamma a Parameter
2
3
c)
a)
Gamma A Parameter - Mixture of 24hr Treated and Untreated AML
b)
Histograms for Data from Pellets
Histograms - Mixture of 24hr Treated and Untreated AML
0.5
l)
k)
h)
g)
Figure 3: H&E staining (a-d) and corresponding B-scan images (FOV 8mm x 8mm) (e-h) of pellets of
mixtures of Cisplatin treated and untreated AML cells, labelled as percent treated. The portion of
structurally modified cells increases noticeably as the percentage of treated cells increases. This
results in a visible increase in the intensity of the ultrasound backscatter from the pellet.
1
j)
i)
Figure 5: Digital photos (a-d), H&E staining (e-h), and B-scan images (FOV 8mm x 8mm) (i-l) of
mouse mammary tissue on days 0, 3, 4 and 6 of involution. Photos and B-scans show the large
reduction in volume which occurs during involution. H&E stain shows the change in tissue
composition from epithelial cells to mostly fat. Arrows indicate alveolar ducts.
e)
c)
a)
d)
B-scan
20 MHz
a)
Using custom software, RF data were extracted from a relatively homogeneous region
of interest (ROI) 4-6 mm wide by 1 mm deep centered at the focus of the transducer.
The maximum likelihood method was implemented in Matlab (The MathWorks Inc.,
Natick, MA) to fit theoretical probability density functions (PDFs) to the data. The
goodness of fit of the PDFs was evaluated using the Kolmogorov-Smirnov (KS) test. For
this study the Rayleigh and Generalized Gamma (GG) distributions were investigated.
These distributions were selected as the Rayleigh PDF applies to the specific case of
many small scatterers at random locations, while the GG PDF is a more flexible
distribution with three fitting parameters.
c)
KS Value
A.U. [A.U.]
The tissue model used was mouse mammary tissue during involution, a process that
occurs over the 6 day period post-lactation. The process of restructuring that occurs
during involution includes a large amount of apoptosis, peaking between days 1-3.
Using the same equipment, B-scan images and raw RF data were collected daily from
the mammary tissue of four mice (Fig. 2).
h)
100%
Gamma
a simulation [A.U.]
[A.U.]
a (simulation)
Gamma
The cell model consists of pellets formed using a mixture of acute myeloid leukemia
(AML) cells treated for 24 hours with Cisplatin, a chemotherapy drug, and untreated
AML cells. The treated and untreated AML cells were mixed to form populations
containing 0%, 2.5%, 5%, 10%, 20%, 40%, 60%, 80% and 100% treated cells. The
mixtures were then centrifuged at 1942 g for 10 minutes to form a pellet. Following
the experiment each pellet was fixed and hematoxylin-eosin (H&E) staining
performed. For each pellet B-scan images of several slices and raw radio frequency
(RF) data from 100 independent locations were collected and stored for offline
analysis using a commercial HFUS imaging device (VisualSonics VS-40b) with a f/3
transducer (focal length 9 mm, centre frequency 40 MHz, bandwidth 95%). Simulated
RF data were generated to model the system in question (Fig. 1), the specifics of the
model are described in Hunt et al. (2002).
20%
d)
g)
f)
a & vv &[A.U.]
Gamma Gamma
a
2. Methods
a)
2.5%
c)
H&E
Stain
e)
0%
Day 6
Gamma c
c [A.U.]
Gamma
3
of Medical Biophysics, University of Toronto, Toronto, ON; 2 Ontario Cancer Institute, University Health Network, Toronto, ON;
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON; 4 Department of Mathematics, Physics and Computer Science, Ryerson University, Toronto, ON
at a higher intensity as the percent of treated cells increases (a). GG a, c and v fit parameter
estimates with 95% confidence intervals (b, c & d) for HFUS data from mixtures of Cisplatin treated
and untreated AML cells and simulated data. The a parameter increases, corresponding to increases in
the image brightness. The c and v parameters show sensitivity to even small changes in the
percentage of treated cells in the pellet.
4. In-Vivo Results
c)
1mm
2mm
Figure 2: VisualSonics VS-40b high frequency ultrasound scanner (a), used for both pellet and tissue
experiments. A magnified view of a mouse being imaged (b), the arrow points to the transducer. c)
Screen capture of opened RF data file showing a homogeneous ROI from mouse mammary tissue. The
dark region to the left of the ROI is a lymph node.
Representative histology of mouse mammary tissue (Fig. 5) shows the cellular changes
that occur during involution, as the tissue changes from mostly epithelial cells to
predominantly fat. The KS goodness of fit test reveals that the GG PDF provides a
good fit at all time points, while the Rayleigh PDF provides a much poorer fit. The
Rayleigh PDF provides the best fit at day 2 of involution, coinciding with the peak of
apoptosis in the tissue (Fig. 6a). The GG fit parameters show large changes between
days 1-3 (Fig. 6b). Over these days the GG fit parameters approach values causing the
GG PDF to resemble a Rayleigh PDF.
6. References
Kolios, M.C., et al., Ultrasonic spectral parameter characterization of apoptosis.
Ultrasound Med Biol, 2002. 28(5): p. 589-97.
Hunt, J.W., et al., A model based upon pseudo regular spacing of cells combined with the
randomisation of the nuclei can explain the significant changes in high-frequency
ultrasound signals during apoptosis. Ultrasound Med Biol, 2002. 28(2): p. 217-26.’’
7. Acknowledgements
We gratefully acknowledge the assistance of Dr. John Hunt of OCI/PMH for his assistance
with the simulations.
This work was funded by Canadian Institutes of Health Research, Natural Sciences and
Engineering Research Council of Canada and The Whitaker Foundation. The HFUS scanner
was purchased with funds from the Canada Foundation for Innovation.