# US 11,308,590 B2 · Computational ultrasound for improved liver and kidney cancer diagnosis

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- Number: US 11,308,590 B2
- Document ID: US11308590B2
- Status: Granted
- Kind: B2
- Application number: 16/489,151
- Filed: 2018-02-27
- Published: 2020-02-27
- Granted: 2022-04-19
- Earliest claimed: 2017-02-27
- Assignees: Rutgers, The State University of New Jersey
- Applicants: Rutgers, The State University of New Jersey
- Inventors: John Nosher; Ilker Hacihaliloglu
- Web page: https://patentgrep.com/patent/US11308590B2

## CPC

- G06T 5/73: Deblurring; Sharpening
- A61B 8/5207: {involving processing of raw data to produce diagnostic data, e.g. for generating an image}
- G06T 5/10: using non-spatial domain filtering
- G06T 5/20: using local operators
- G06T 5/94: based on local image properties, e.g. for local contrast enhancement
- G06V 10/443: {by matching or filtering}
- G06T 2207/10132: Ultrasound image
- G06T 2207/30056: Liver; Hepatic
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

This disclosure provides a system and method for generating an ultrasound image. The method includes receiving a raw ultrasound signal output from an ultrasound device and performing operations to transform the raw ultrasound signal into an enhanced ultrasound image. The enhanced ultrasound image may be further processed by radial symmetry filtering to generate a radial symmetry image. Both the enhanced image and the radial symmetry image can be by a medical practitioner to make a liver or kidney cancer diagnosis exclusively based on ultrasound data.

## Claims

### Claim 1

A system for generating a liver ultrasound image, comprising:
a non-transitory, computer readable memory;
one or more processors; and
a computer-readable medium containing programming instructions that, when executed by the one or more processors, cause the system to:
acquire a raw liver ultrasound signal output from an ultrasound device;
generate filtered liver ultrasound image data by filtering the raw liver ultrasound signal to transform information from a signal domain to a frequency domain;
extract image phase and energy features from the filtered liver ultrasound image data by an alpha scale filtering that is configured to extract the image phase features;
generate a filtered liver ultrasound signal by transforming the image phase and energy features from the frequency domain to the signal domain;
produce a transmission map estimation based on a backscattered liver ultrasound signal from a tissue interface; and
enhance the image phase and energy features of the filtered liver ultrasound signal based on the transmission map estimation so that portions of a soft liver tissue associated therewith are made darker or lighter in an enhanced liver ultrasound image.

### Claim 2 (depends on 1)

The system of claim 1, further comprising programming instructions, when executed by the one or more processors, causing the system to
perform radial symmetry filtering on the enhanced liver ultrasound image by analyzing dark spherical shapes in the enhanced liver ultrasound image; and
generate a radial symmetry image to identify regions of interest in the enhanced liver ultrasound image.

### Claim 3 (depends on 2)

The system of claim 2, further comprising programming instructions, when executed by the one or more processors, causing the system to output a surface topography map based on the radial symmetry image.

### Claim 4 (depends on 1)

The system of claim 1, wherein the alpha scale filtering is performed in a frequency domain.

### Claim 5 (depends on 4)

The system of claim 4, wherein the alpha scale filtering is defined by expression
A
⁢
⁢
S
⁢
⁢
F
⁡
(
ω
)
=
{
n
c
⁢
ω
a
⁢
exp
⁡
(
-
(
σ
⁢
⁢
ω
)
2
⁢
⁢
α
)
ω
>
0
0
otherwise
where a is constant derivative parameter, σ is a filter alpha-scale parameter, and nc is a unit normalization constant calculated from filter α.

### Claim 6 (depends on 5)

The system of claim 5, wherein nc is computed in accordance with mathematical equation
n
c
=
2
⁢
π
⁢
⁢
α
⁢
2
2
⁢
⁢
a
+
1
4
⁢
⁢
α
⁢
s
a
+
0.5
Γ
⁡
(
2
⁢
⁢
a
+
1
2
⁢
⁢
α
)
where s is a scale parameter and α is a derivative parameter.

### Claim 7 (depends on 1)

The system of claim 1, wherein the transmission map estimation comprises combining scattering and attenuation effects in soft tissue based on equation
US(x,y)=USA(x,y)USE(x,y)+(1−USA(x,y))α
where US(x,y) is a local energy image, USA(x, y) is a signal transmission map, USE(x, y) is an enhanced ultrasound image, and α is a constant value representative of echogenicity in the soft tissue in a local region.

### Claim 8 (depends on 7)

The system of claim 7, wherein USE(x, y) is extracted by estimating the signal transmission map USA(x, y) using a Beer-Lambert law which models an attenuation function as a function of imaging depth.

### Claim 9 (depends on 8)

The system of claim 8, wherein the Beer-Lambert law is defined by equation
UST(x,y)=USo(x,y)exp(−ηd(x,y))
where USo(x, y) is an initial intensity image, UST(x, y) is an attenuated intensity image, ηis an attenuation coefficient, and d(x,y) is a distance from an ultrasound transducer surface.

### Claim 10 (depends on 9)

The system of claim 9, wherein USA(x,y) is obtained by minimizing an objective function defined by equation
λ
2
⁢

US
A
⁡
(
x
,
y
)
-
US
T
⁡
(
x
,
y
)

2
2
+
∑
j
∈
ω
⁢

W
j
⁢
o
(
D
j
*
US
A
⁡
(
x
,
y
)

1
where ω is an index set, o represents element wise multiplication, * is a convolution operation, Dj is obtained using a bank of high order differential filters consisting of eight Kirsch filters and one Laplacian filter, and Wj is a weighting matrix calculated in accordance with mathematical equation
Wj=exp(−|Dj(x,y)*US(x,y)|2).

### Claim 11 (depends on 10)

The system of claim 10, wherein USE(x, y) is calculated in accordance with equation
US
E
⁡
(
x
,
y
)
=
[
US
⁡
(
x
,
y
)
-
α
[
max
⁡
(
US
A
⁡
(
x
,
y
)
,
ɛ
)
]
δ
]
+
α
.

### Claim 12 (depends on 2)

The system of claim 2, wherein the radial symmetry filtering is specified by equation
Sn=Fn*An
where “*” denotes the convolution operation; An is an isotropic Gaussian function and Fn is defined as:
F
n
⁡
(
p
)
=
M
n
⁡
(
p
)
k
n
⁢
(

⁢
(
p
)

k
n
)
⁢
α
⁢
(
p
)
=
{
O
n
⁡
(
p
)
,
O
n
⁡
(
p
)
<
k
n
k
n
,
otherwise
and a is a radial strictness parameter, and kn is a scaling factor across different radii.

### Claim 13 (depends on 2)

The system of claim 2, wherein the radial symmetry image is defined by equation
S
=
1

N

⁢
∑
n
∈
N
⁢
⁢
S
n
where S is a sum of all symmetry contributions Sn over all ranges considered, and n is the number of symmetry contributions over all radiuses considered.

### Claim 14

A method for generating a liver ultrasound image, comprising:
acquiring, by a computing device, a raw liver ultrasound signal output from an ultrasound device;
generating filtered liver ultrasound image data by filtering the raw liver ultrasound signal to transform information from a signal domain to a frequency domain;
extracting image phase and energy features from the filtered liver ultrasound image data by an alpha scale filtering that is configured to extract the image phase features;
generating a filtered liver ultrasound signal by transforming the image phase and energy features from the frequency domain to the signal domain;
producing a transmission map estimation based on a backscattered liver ultrasound signal from a tissue interface; and
enhancing the image phase and energy features of the filtered liver ultrasound signal based on the transmission map estimation so that portions of a soft liver tissue associated therewith are made darker or lighter in an enhanced liver ultrasound image.

### Claim 15 (depends on 14)

The method of claim 14, further comprising:
performing, by a computing device, radial symmetry filtering on the enhanced liver ultrasound image by analyzing dark spherical shapes in the enhanced liver ultrasound image; and
generating, by a computing device, a radial symmetry image to identify regions of interest in the enhanced liver ultrasound image.

### Claim 16 (depends on 15)

The method of claim 15, further comprising outputting a surface topography map based on the radial symmetry image.

### Claim 17 (depends on 14)

The method of claim 14, wherein the backscattered liver ultrasound signal is modulated by interactions with a liver tissue, the interactions comprising scattering and attenuation.

### Claim 18 (depends on 14)

The method of claim 14, wherein the step of generating the filtered liver ultrasound image data is performed by a Fourier transform.

### Claim 19 (depends on 14)

The method of claim 14, wherein the step of generating the filtered liver ultrasound signal is performed by the inverse Fourier transform.

### Claim 20 (depends on 14)

The method of claim 14, wherein the alpha scale filtering is defined by expression
A
⁢
S
⁢
F
⁡
(
ω
)
=
{
n
c
⁢
ω
a
⁢
exp
(
-
(
σ
⁢
ω
)
2
⁢
α
ω
>
0
0
otherwise
where α is constant derivative parameter, σ is a filter alpha-scale parameter, and nc is a unit normalization constant calculated from filter α.