Development of Assist Strain Ratio for the Strain Ratio Measurement ...

Authorized translation from:
Baba H, et al. : Development of the FLR Assistance for the Strain Ratio Measurement in Breast Elastography. MEDIX 58, 42-45, 2013 (in Japanese) .
Technical Report
Development of Assist Strain Ratio for
the Strain Ratio Measurement in Breast Elastography
Hirotaka Baba
Kouji Waki
Naoyuki Murayama
Takashi Iimura
Yusuke Miyauchi
Hitachi Aloka Medical, Ltd. Medical Systems Engineering Division
We have developed Assist Strain Ratio. This is intended to improve the convenience and objectivity for one of the
features of a Strain Ratio measurement of elastography. And that is what is measured semi-automatically strain ratio of a
tumor area and a fat area in the mammary glandFLR : Fat Lesion Ratio. In comparison with the value measured by the
user manually, the average value is 0.87±0.06SD in terms of the radius of the overlapping ratio of the tumor ROI and the
fat ROI. We visually confirmed that the ROI on the fat layer of 93 cases in 99 cases. There was a strong positive
correlation between the semi-automatic measurements and manual measurements (r 0.84, p 0.0001). Thus, this
system is a promising tool to improve the throughput of Strain Ratio Measurement.
Key Words: Assist Strain Ratio, Strain Ratio, Fat Lesion Ratio, FLR, Real-time Tissue Elastography, Ascendus
1. Introduction
Elastography is a method that exploits the elasticity of
tissue—the softer the tissue, the more deformable it is; the
stiffer the tissue, the less deformable it is—to measure the
displacement of tissue in minute areas and visualize the
strain calculated from the measurements 1)2) . This technology,
equipped with real-time display capability and incorporated
into a practical system, is named Real-time Tissue
Elastography *1 (hereinafter referred to as elastography) 3)4) . In
general, strain depends on stress (compression velocity) and
is therefore difficult to quantify. However, by taking
advantage of the fact that strain changes linearly in a range
of 2 to 3% 5) , we have developed Strain Ratio (SR), a measure
for quantifying strain based on the ratio of strain between
different tissues 6) .
The elasticity score, a semi-quantitative assessment of
tissue elasticity in the breast region scored by the examiner,
is reported to have greater sensitivity and a higher correct
diagnosis rate than diagnosis using B-mode images 7) , and has
been widely introduced as a new tool to support
conventional diagnostic information. A method has been
developed to provide quantitative assessment of SR in the
breast region using Strain Ratio of fat to lesion, Fat Lesion
Ratio (FLR), as shown in formula (1), and FLR has been
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reported to have sufficient clinical capability for objective
diagnosis 8) .
Mean fat strain
FLR = (1)
Mean lesion strain
Generally speaking, changes in tissue elasticity due to the
estrous cycle and individual differences are considered less
pronounced in fat than in the normal mammary gland, which
is why FLR serves as an index to estimate the degree of
strain in a lesion in relation to that in fat. Since fat is soft
and the strain is large, the SR value is often greater than 1,
and the harder and less deformable the lesion is, the greater
the SR value.
2. Problems of conventional operation
Conventional procedures of SR measurement involve
visualizing an elastography image and setting regions of
interest (ROIs) manually on the mass and fat layer
respectively. When setting ROIs, visual inspection and
careful manual handling are required because the examiner
needs to identify a hypoechoic area in the B-mode image,
set a circular ROI that roughly inscribes the identified area,
and repeat the same procedure in the fat layer.
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3. Operating procedure and automated
algorithm of Assist Strain Ratio
To solve the problems of operation, we have automated
almost the entire process of operation involved in the
setting of ROIs and named this capability Assist Strain
Ratio. The operating procedure and algorithm are described
below.
3.1 Operating procedure
After the elastography image is visualized, when the user
roughly specifies the center of the area on which to set a
mass ROI, the algorithm automatically sets the mass and fat
layer ROIs, calculates the SR value, and displays it on the
screen.
3.2 Algorighm for the automated setting of mass ROIs
First, the algorithm calculates the margin of a tumor mass
visualized on a B-mode image. Then it places an ROI circle
that roughly inscribes the mass margin with its center set in
the neighborhood of the point specified by the user. We say
the ROI circle inscribes the mass margin “roughly” because
sometimes the mass margin is invisible under the shadow of
the nipple or the mass. Also, the algorithm searches for the
center of the ROI circle in the "neighborhood" of the point
specified by the user—rather than setting it exactly at that
point—to maximize the area of the ROI. We wanted to make
this process easier, thus we have the algorithm search this
way because it is difficult for the user to visually specify on
the image the optimal center that maximizes the radius of
the ROI. The user only has to roughly indicate the center of
the mass; the exact location of the center and setting of the
optimal ROI are automatically processed by the algorithm.
Detection of the mass margin is achieved by function f as
shown in formula (2). Function f takes up the points that
have gradient directions (A) going away from the
user-specified point (U) and returns the points that have
sharp and large gradient lengths (L) as the legion margin (R).
The gradient direction (A) is the direction of the change in
brightness (formula (3)), and the gradient length (L)
represents the sharpness of the change in brightness
(formula (4)). The gradient of brightness (G) is a vector
consisting of x and y components, which is calculated by
partial differentiation of the brightness of the B-mode image
(I) (formula (5)). An example of the mass margin calculated
from a phantom image is shown in Figure 1.
Figure 1: Example of the calculated mass margin
B-mode image of a phantom (top) and gradient vectors (middle, blue
arrows); Gradient length (middle, monotone image); Mass margin
(bottom, blue dots)
The detection of the mass margin (R) is followed by the
determination of the optimal center of the ROI (C) from the
pixels (c) neighboring the user-specified point. The
algorithm proceeds as follows. First, it calculates the
distance from each (c k
) of the neighboring pixels (c) to all
the pixels at the mass margin, and determines the minimum
distances (J k
) of all the distances (formula (6)). Then it
selects the maximum of the minimum distances as index K
(formula (7)), and has the central location (c K
) serve as the
center of the ROI (formula (8)). An example of the calculated
ROI is shown in Figure 2.
Minimum distance J k = argmin(|Rj – c k |)
Index K, the maximum of the minimum distances = augmax(J k
k
Calculated center of the ROI = cK
(6)
(7)
(8)
R = f ( U, A, L )
(2)
A = angle ( G )
(3)
L = | G |
(4)
G
=
∂I
∂I
+ i
(5)
∂x
∂y
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Figure 2: Example of the calculated ROI
User-specified point (U) (red point) and calculated ROI (yellow circle and
yellow point); Pixels neighboring the user-specified point (c) (green dots);
Minimum distance (Jk) (red line)
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3.3 Algorighm for the automated setting of a fat layer ROI
Since it is difficult with the current technology to identify a
fat layer, we took the approach of not setting an ROI in an
area that is obviously not a fat layer. The procedure consists
of the following 3 steps:
1) Calculate distributions in which areas that are
obviously not a fat layer are given low values
(hereinafter referred to as “possibility distribution” ).
2) Have the area with the highest value of possibility
distribution serve as the tentative center of a fat layer ROI.
Draw an ROI circle that has a radius extending from the
area neighboring the tentative center to the margin of
3) the fat layer in the same manner used for drawing a
mass ROI.
An explanation of this important concept of possibility
distribution follows. Possibility distribution has the same
field as the elastography image. Each pixel in this field is
given a value of 0 if it is “obviously not a fat layer,” and a
value greater than 0 but not greater than 1 if it is not
“obviously not a fat layer.” In other words, an area likely to
be a fat layer has a value closer to 1, and an area that is
unlikely to be a fat layer has a value closer to 0. Since these
values are difficult to determine based on a single condition,
they are determined based on multiple conditions.
Possibility distribution (P) is calculated as the product of
distributions P1, P2 and P3 as shown in formula (9).
Examples of these distributions are shown in Figure 3, and
each type of distribution is explained as follows:
-
-
-
P1: distribution in which areas with low brightness of a
B-mode image (I) are given a possibility value of 0, and
the others are given 1 (formula (10))
P2: distribution in which the higher the gradient length
(L), the lower the possibility (formula (11))
P3: distribution in which areas with low strain
distribution (E) are given a possibility value of 0, and
the others are given 1 (formula (12))
Possibility distribution P = P 1 ∙ P 2 ∙ P3
Non-low-intensity distribution P 1 =
B-image intensity (I) >
Low-intensity decision threshold
Non-structure distribution P 2 = 1 -
Non-deformation distribution P3 =
Gradient length (L)
Max (gradient length (L))
Strain distribution (E) >
Strain threshold
(9)
(10)
(11)
(12)
3.4 Assessment method
Among patients who underwent the SR measurements by
means of the HI VISION Ascendus diagnostic ultrasound
scanner *2 in a breast sonography examination at Kawasaki
Hospital and Tsukuba Medical Center Hospital, 99 patients
with a mass image-forming mass were subjected to
assessment. The assessment involved a quantitative
determination of how closely the user-set ROIs and the
measured SR values were approximated by those determined
semi-automatically by the algorithm with the input of the
user-specified centers of ROIs. Mass ROIs were evaluated
by radius-converted overlap rate. Fat layer ROIs were
visually examined to determine whether the ROI set by the
algorithm was located on the fat layer. An overall evaluation
was made using the correlation coefficients of the SR values
measured by the user and those measured semi-automatically.
The radius-converted overlap rate was calculated by formula
(13), where α is the radius of an ROI set by the user, β is
the radius of an ROI set semi-automatically, and γ is the
radius converted from the overlapped area. The
radius-converted overlap rate is 1 if both ROIs overlap
completely with each other, and 0 if there is no overlap.
Radius-converted overlap rate =
2γ
α + β
4. Results and Discussion
The mean radius-converted overlap rate for mass ROIs was
0.87 ± 0.06 SD. Figure 4 shows examples of
radius-converted overlap rates of 0.81, 0.87, and 0.93.
Visual inspection of the fat ROIs revealed that the ROI was
located on the fat layer in 93 of the 99 cases. The SR values
measured by the user and those measured semi-automatically
were strongly positively correlated (r=0.84, p

These results suggest that the probability is comparable to
that of the SR values determined with the conventional
manual method. Since replacing the manual measurement
with semi-automatic measurement reduces the trouble
involved, the introduction of this technology will hopefully
improve the throughput of examination. To attain this
improvement it is necessary to clearly visualize the margin of
a mass on a B-mode image and achieve good reproducibility
when obtaining elastographic images.
5. Limitations
With this technology it is difficult to detect accurate margin
information from masses that do not have a distinct tumor
shape, such as masses with ambiguous margins on B-mode
images or non-mass image-forming masses. It is also
necessary to check the validity of the set ROI and correct it
manually if it turns out to be invalid.
6. Future challenges
Further reducing the difficulty in measurement will require
automated detection of the center of a lesion ROI. If this
becomes possible, the user will be able to obtain results with
just one press of a button on the console, eliminating a
significant amount of steps in the examination and thus
contributing to improved throughput.
References
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2)
3)
4)
5)
6)
7)
8)
Shiina T, et al. : Strain Imaging Using Combined RF and
Envelope Autocorrelation Processing. Proc. of 1996
IEEE Ultrasonics Symp, 4 : 1331-1336, 1996.
Matsumura T, et al. : Development of Freehand
Ultrasound
Elasticity Imaging System and in vivo Results. First
International Conference on the Ultrasonic Measurement
and Imaging of Tissue Elasticity, 1 : 80, 2002.
Matsumura T, et al. : Development of Real-time Tissue
Elastography. MEDIX 41, 30-35, 2004.
Matsumura T, et al. : Diagnostic results for breast
disease by real-time elasticity imaging system.
Proceedings
of 2004 IEEE Ultrasonics Symposium : 1484-1487, 2004.
N.Nitta, T.Shiina : Estimation of Nonlinear Parameter
of Tissues by Ultrasound. Japanese Journal of Applied
Physics, 41, Part 1, 5B, 3572-3578, 2002.
Waki K, et al. : INVESTIGATION OF STRAIN RATIO
USING ULTRASOUND ELASTOGRAPHY
TECHNIQUE. Proc.ISICE 2007 p449-452
Itoh A, et al. : Clinical application of US elastography for
diagnosis. Radiology, 239 (2) , 341-350, 2006.
Ueno E, et al. : New quantitative method in breast
elastography : Fat Lesion Ratio (FLR). Abstracts of
RSNA 2007 ; LL-BR2123-H04, 2007.
7. Acknowledgments
The assessment of the clinical usability of this technology
during the development process—from prototype design to
integration into the practical system—has been undertaken
as a collaborative research with Dr. Kazutaka Nakashima of
Kawasaki Hospital and Dr. Ei Ueno of Tsukuba Medical
Center Hospital. We would like to thank everyone
concerned in both hospitals for their contribution to this
project.
*1 Real-time Tissue Elastography; *2 HI VISION Ascendus and
Ascendus are registered trademarks of Hitachi Medical Corporation
in Japan and other countries.
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