# US 11,076,835 B2 · Model-based control of a dynamic range of an ultrasound image

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- Number: US 11,076,835 B2
- Document ID: US11076835B2
- Status: Granted
- Kind: B2
- Application number: 15/966,405
- Filed: 2018-04-30
- Published: 2019-10-31
- Granted: 2021-08-03
- Earliest claimed: 2018-04-30
- Assignees: BK Medical Aps
- Applicants: B-K Medical Aps
- Inventors: Jens Mose Pedersen; Svetoslav Ivanov Nikolov
- Web page: https://patentgrep.com/patent/US11076835B2

## CPC

- A61B 8/5269: {involving detection or reduction of artifacts}
- G01S 7/52046: {Techniques for image enhancement involving transmitter or receiver (image enhancement by image data processing G06T5/00)}
- A61B 8/145: {characterised by scanning multiple planes}
- A61B 8/5207: {involving processing of raw data to produce diagnostic data, e.g. for generating an image}
- A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)}
- A61B 8/54: {Control of the diagnostic device}
- A61B 8/58: {Testing, adjusting or calibrating the diagnostic device}
- A61B 8/585: {Automatic set-up of the device}
- G01S 7/52033: {Gain control of receivers (for seismic signals G01V1/245)}
- G01S 7/5205: {Means for monitoring or calibrating}
- G01S 15/8915: {using a transducer array}
- G06T 5/70: Denoising; Smoothing
- G06T 5/92: based on global image properties
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G06T 2207/10132: Ultrasound image
- G06T 2207/20208: High dynamic range [HDR] image processing
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A system includes a transducer array configured to produce a signal indicative of a received echo wave, receive circuitry configured to amplify and digitize the signal, sub-systems configured to process the digitized signal to generate an image including electronic noise from the system, and a system controller. The system controller is configured to retrieve a pre-determined noise level of an analog front end of the receive circuitry, configure the sub-systems, wherein each of sub-systems includes multiple processing blocks, and the configuring determines which of the processing blocks are employed and which of the processing blocks are bypassed for a scan, and determine a noise level for each of the employed sub-systems based on a corresponding noise model. The sub-systems include a processor configured to adaptively vary at least one of a gain and a dynamic range of the image as a function of depth based on the noise levels.

## Claims

### Claim 1

An ultrasound imaging system, comprising:
a transducer array configured to produce a signal indicative of a received echo wave;
receive circuitry configured to amplify and digitize the signal;
processing sub-systems configured to process the digitized signal to generate an image including electronic noise from the ultrasound imaging system; and
a system controller configured to:
determine and store a noise level of an analog front end of the receive circuitry as a pre-determined noise level;
retrieve the pre-determined noise level of the analog front end of the receive circuitry
configure the processing sub-systems, wherein each of processing sub-systems includes a set of different processing blocks, and the configuring determines which of the processing blocks are employed and which of the processing blocks are bypassed for a scan; and
determine a noise level for each of the processing sub-systems comprising the employed processing blocks based on a corresponding noise model;
wherein the noise level of the analog front end is based on:
νN=νATGC+TGC·νLNA,
where νN is a root-mean-square voltage of the noise level at an output of the analog front end, νATGC is a root-mean-square voltage of a thermal noise of the analog front end, νLNA is a root-mean-square voltage of the thermal noise at the output of the analog front end, and TGC is a value of gain at a given depth,
wherein the processing sub-systems includes a processor configured to adaptively vary at least one of a gain and a dynamic range of the image as a function of depth based on the noise levels of the processing sub-systems.

### Claim 2 (depends on 1)

The ultrasound imaging system of claim 1, wherein the system controller is further configured to determine the noise level of the analog front end through a system calibration.

### Claim 3 (depends on 2)

The ultrasound imaging system of claim 2, wherein the system controller performs the system calibration upon at least one of first use of the ultrasound imaging system and first use of the ultrasound imaging system after a system update.

### Claim 4 (depends on 2)

The ultrasound imaging system of claim 2, wherein the system controller is configured to perform the system calibration prior to retrieving the pre-determined noise level by:
for each channel of the receive circuitry, generating a signal in absence of a received echo with all of the processing sub-systems for all supported configuration of the receive circuitry;
for each configuration, maintaining a first gain for a first depth range, linearly increasing the first gain to a second gain over a second depth range, and maintaining the second gain for a third depth range;
for each channel and for each configuration, recording an output of each of the processing sub-systems; and
determining the pre-determined noise level of the analog front end from the recorded output.

### Claim 5 (depends on 1)

The ultrasound imaging system of claim 1, wherein the system controller is configured to:
determine a first value for νN at the first gain;
determine a second value for νN at the second gain; and
determine νATGC and νLNA based on the first and second values of νN and νN=νATGC+TGC·νLNA.

### Claim 6 (depends on 1)

The ultrasound imaging system of claim 1, wherein the system controller is configured to determine an alternating current contribution of the noise level for linear systems of the processing sub-systems based on:
G
AC
=
∑
i
=
0
N
-
1
⁢
h
i
2
,
where hi are filter coefficients and N is a number of filter coefficients, and the system controller is configured to determine a direct current contribution of the noise level for the linear systems of the processing sub-systems based on:
G
D
⁢
C
=
∑
i
=
0
N
-
1
⁢
h
i
.
and the system controller determines a root-mean-square value of the noise level for a respective linear system of the linear systems
νo=√{square root over (σo2+μo2)},
where σo=GAC·σi and is a standard deviation of noise at the output of the respective linear system, μo=GDC·μi and is a mean value of the noise at the output of the respective linear system, σi is a standard deviation of noise at an input of the respective linear system, and μi is a mean value the noise at the input of the respective linear system.

### Claim 7 (depends on 6)

The ultrasound imaging system of claim 6, wherein the linear systems include at least one of a beamformer, a coherent beam processor, and a non-coherent beam processor.

### Claim 8 (depends on 1)

The ultrasound imaging system of claim 1, wherein the system controller is configured to determine a noise level of an output of non-linear processing systems of the processing sub-systems based on:
E
⁢
{
y
q
}
=
∫
a
b
⁢
(
F
⁡
(
x
)
)
q
⁢
w
⁡
(
x
)
⁢
dx
∫
a
b
⁢
w
⁡
(
x
)
⁢
dx
,
wherein E{yq} is an expectation of a qth moment of an output value y, F(x) is a transfer function, and w(x) is a probability density function of a noise x at an input of a processing sub-system, wherein a mean of the output noise is μy=E{y1}, and a standard deviation of the output noise is σy=√{square root over (E{y2}−(E{y1})2)}.

### Claim 9 (depends on 1)

The ultrasound imaging system of claim 1, wherein the system controller is configured to determine a noise level of an output of non-linear processing systems of the processing sub-systems, and wherein the non-linear processing systems include at least one of an envelope detector and a dynamic range compressor.

### Claim 10

A method, comprising:
retrieving a pre-determined noise level of an analog front end of receive circuitry of an ultrasound imaging system;
configuring processing sub-systems of the ultrasound imaging system, wherein each of processing sub-systems includes multiple processing blocks including no identical and interchangeable processing blocks, and the configuring determines which of the processing blocks are employed and which of the processing blocks are bypassed for a scan;
determining a noise level for each of the processing sub-systems comprising the employed processing blocks corresponding noise model, including determining an alternating current contribution of the noise level for linear systems of the processing sub-systems based on:
G
AC
=
∑
i
=
0
N
-
1
⁢
h
i
2
,
where hi are filter coefficients and N is a number of filter coefficients;
determining a direct current contribution of the noise level for the linear systems of the processing sub-systems based on:
GDC=Σi=0N-1hi; and
determining a root-mean-square value of the noise level for a respective linear system of the linear systems as:
νo=√{square root over (σo2+μo2)},
where σo=GAC·σi and is a standard deviation of noise at the output of the respective linear system, μo=GDC·μi and is a mean value of the noise at the output of the respective linear system, σi is a standard deviation of noise at an input of the respective linear system, and μi is a mean value of the noise at the input of the respective linear system; and
adaptively varying at least one of a gain and a dynamic range of an ultrasound image as a function of depth based on the noise levels of the processing sub-systems to visually present an electrical noise compensated image.

### Claim 11 (depends on 10)

The method of claim 10, further comprising, prior to retrieving the pre-determined noise level:
for each channel of the receive circuitry, generating a signal in absence of a received echo with all of the processing sub-systems for all supported configuration of the receive circuitry;
for each configuration, maintaining a first gain for a first depth range, increasing the first gain to a second gain over a second depth range, and maintaining the second gain for a third depth range;
for each channel and for each configuration, recording an output of each of the processing sub-systems; and
determining the pre-determined noise level of the analog front end from the recorded output.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
determining and storing a noise level of the analog front end as the pre-determined noise level of the analog front end, wherein determining the noise level of the analog front end is based on:
νN=νATGC+TGC·νLNA,
where νN is a root-mean-square voltage of the noise level at an output of the analog front end, νATGC is a root-mean-square voltage of a thermal noise of the analog front end, νLNA is a root-mean-square voltage of the thermal noise at the output of the analog front end, and TGC is a value of gain at a given depth, by:
determining a first value for νN at the first gain;
determining a second value for νN at the second gain; and
determining νATGC and νLNA based on the first and second values of νN and tνN=νATGC+TGC·νLNA.

### Claim 13 (depends on 10)

The method of claim 10, further comprising:
determining a noise level of an output of non-linear processing systems of the processing sub-systems based on:
E
⁢
{
y
q
}
=
∫
a
b
⁢
(
F
⁡
(
x
)
)
q
⁢
w
⁡
(
x
)
⁢
dx
∫
a
b
⁢
w
⁡
(
x
)
⁢
dx
,
wherein E{yq} is an expectation of a qth moment of an output value y, F(x) is a transfer function, and w(x) is a probability density function of a noise x at an input of a processing sub-system, wherein a mean of the output noise is μy=E{y1}, and a standard deviation of the output noise is σy=√{square root over (E{y2}−(E{y1})2)}.

### Claim 14

A non-transitory computer readable medium encoded with non-transitory computer executable instructions which when executed by a processor causes the processor to:
retrieve a pre-determined noise level of an analog front end of receive circuitry of an ultrasound imaging system;
configure processing sub-systems of the ultrasound imaging system, wherein each of processing sub-systems includes multiple processing blocks, and the configuring determines which of the processing blocks are employed and which of the processing blocks are bypassed for a scan;
determine a noise level for each of the processing sub-systems comprising the employed processing blocks based on a corresponding noise model, wherein the processing sub-systems comprising the employed processing blocks include at least one linear subsystem and at least one non-linear subsystem, and a noise level of an output of the at least one non-linear processing system is determined based on:
E
⁢
{
y
q
}
=
∫
a
b
⁢
(
F
⁡
(
x
)
)
q
⁢
w
⁡
(
x
)
⁢
dx
∫
a
b
⁢
w
⁡
(
x
)
⁢
dx
,
wherein E{yq} is an expectation of a qth moment of an output value y, F(x) is a transfer function, and w(x) is a probability density function of a noise x at an input of a processing sub-system, wherein a mean of the output noise is μy=E{y1}, and a standard deviation of the output noise is σy=√{square root over (E{y2}−(E{y1})2)}; and
adaptively vary at least one of a gain and a dynamic range of an ultrasound image as a function of depth based on the noise levels of the processing sub-systems to visually present an electrical noise compensated image.

### Claim 15 (depends on 14)

The non-transitory computer readable medium of claim 14, wherein the computer executable instructions further cause the processor to, prior to retrieving the pre-determined noise level:
for each channel of the receive circuitry, generate a signal in absence of a received echo with all of the processing sub-systems for all supported configuration of the receive circuitry;
for each configuration, maintain a first gain for a first depth range, linearly increase the first gain to a second gain over a second depth range, and maintain the second gain for a third depth range;
for each channel and for each configuration, record an output of each of the processing sub-systems; and
determine the pre-determined noise level of the analog front end from the recorded output.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein the computer executable instructions further cause the processor to:
determine and store a noise level of the analog front end as the pre-determined noise level, wherein the noise level of the analog front end is based on:
νN=νATGC+TGC·νLNA,
where νN is a root-mean-square voltage of the noise level at an output of the analog front end, νATGC is a root-mean-square voltage of a thermal noise of the analog front end, νLNA is a root-mean-square voltage of the thermal noise at the output of the analog front end, and TGC is a value of gain at a given depth, by:
determining a first value for νN at the first gain;
determining a second value for νN at the second gain; and
determining νATGC and νLNA based on the first and second values of νN and tνN=νATGC+TGC·νLNA.

### Claim 17 (depends on 14)

The non-transitory computer readable medium of claim 14, wherein the computer executable instructions further cause the processor to:
determine an alternating current contribution of the noise level for linear systems of the processing sub-systems based on:
G
AC
=
∑
i
=
0
N
-
1
⁢
h
i
2
,
where hi are filter coefficients and N is a number of filter coefficients;
determine a direct current contribution of the noise level for the linear systems of the processing sub-systems based on:
GDC=Σi=0N-1hi; and
determine a root-mean-square value of a noise level for a respective linear system of the linear systems as:
νo=√{square root over (σo2+μo2)},
where σo=GAC·σi and is a standard deviation of noise at the output of the respective linear system, μo=GDC·μi and is a mean value of the noise at the output of the respective linear system, σi is a standard deviation of noise at an input of the respective linear system, and μi is a mean value of the noise at the input of the respective linear system.