# US 2021/0279919 A1 · Systems and methods for evaluating image quality

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- Number: US 2021/0279919 A1
- Document ID: US20210279919A1
- Status: Published application
- Kind: A1
- Application number: 17/326,374
- Filed: 2021-05-21
- Published: 2021-09-09
- Earliest claimed: 2018-06-11
- Assignees: Shanghai United Imaging Healthcare Co., Ltd.
- Applicants: Shanghai United Imaging Healthcare Co., Ltd.
- Inventors: Yi WANG
- Web page: https://patentgrep.com/patent/US20210279919A1

## CPC

- G06T 12/30: Image post-processing, e.g. metal artefact correction
- A61B 5/489: {Blood vessels}
- A61B 6/032: {Transmission computed tomography [CT]}
- A61B 6/503: {for diagnosis of the heart}
- A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography}
- A61B 6/5264: {due to motion}
- G06T 5/20: using local operators
- G06T 5/50: using two or more images, e.g. averaging or subtraction
- G06T 5/73: Deblurring; Sharpening
- G06T 7/0014: {using an image reference approach}
- G06T 7/11: Region-based segmentation
- G06T 7/136: involving thresholding
- G06T 7/90: Determination of colour characteristics
- G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction
- A61B 6/037: {Emission tomography}
- A61B 6/481: {involving the use of contrast agents}
- A61B 6/5288: {involving retrospective matching to a physiological signal}
- A61B 6/541: {involving acquisition triggered by a physiological signal}
- G06T 2207/10072: Tomographic images
- G06T 2207/20201: Motion blur correction
- G06T 2207/30048: Heart; Cardiac
- G06T 2207/30101: Blood vessel; Artery; Vein; Vascular
- G06T 2207/30168: Image quality inspection
- G06T 2211/404: Angiography
- G06T 2211/412: Dynamic
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

## Claims

### Claim 1

A method implemented on at least one machine each of which has at least one processor and at least one storage device for evaluating image quality, the method comprising:
obtaining an image;
determining one or more sub-images of a region of interest by segmenting the image; and
determining, based on the one or more sub-images of the region of interest, a quality index for the image, wherein the quality index is associated with a regularity degree of the image and a sharpness degree of the image.

### Claim 2 (depends on 1)

The method of claim 1, wherein the image includes a plurality of elements, each element of the elements is a pixel or voxel, and each element has a gray level.

### Claim 3 (depends on 2)

The method of claim 2, wherein determining one or more sub-images of a region of interest by segmenting the image comprises:
determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; and
determining the one or more sub-images of the region of interest by segmenting, based on the one or more thresholds, the image.

### Claim 4 (depends on 1)

The method of claim 1, wherein the determining, based on the one or more sub-images of the region of interest, a quality index for the image comprises:
determining, based on the one or more sub-images of the region of interest, the regularity degree of the image;
determining, based on the one or more sub-images of the region of interest, the sharpness degree of the image; and
determining, based on the regularity degree and the sharpness degree, the quality index for the image.

### Claim 5 (depends on 4)

The method of claim 4, wherein the determining, based on the regularity degree and the sharpness degree, the quality index for the image comprises:
designating a weighted sum of the regularity degree and the sharpness degree as the quality index for the image.

### Claim 6 (depends on 4)

The method of claim 4, wherein the determining, based on the one or more sub-images of the region of interest, a quality index for the image further comprises:
identifying one or more connected domains in the one or more sub-images of the image;
determining a count of the identified connected domains in the image; and
in response to a determination that the count of the identified connected domains in the image is zero,
designating the regularity degree of the image as zero; and
designating the sharpness degree of the image as zero.

### Claim 7 (depends on 6)

The method of claim 6, wherein the determining, based on the one or more sub-images of the region of interest, a regularity degree of the image comprises:
determining a perimeter and an area of each connected domain of the identified connected domains in the image; and
determining, based on the one or more perimeters and the one or more areas, a regularity degree for the image.

### Claim 8 (depends on 6)

The method of claim 6, wherein the determining, based on the one or more sub-images of the region of interest, a sharpness degree of the image comprises:
for each connected domain of the identified connected domains in the image,
determining an edge of the each connected domain; and
determining a gradient map of the each connected domain; and
determining, based on the one or more edges and the gradient maps, the sharpness degree of the image.

### Claim 9 (depends on 3)

The method of claim 3, wherein the determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image comprises:
designating the maximum gray level multiplied by one or more predetermined multiples as the one or more thresholds for segmenting the image.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
obtaining a second image, the second image including a plurality of second elements, each element of the plurality of second elements being a pixel or voxel, each second element having a gray level;
determining, based on a maximum gray level of the plurality of second elements, one or more second thresholds for segmenting the second image;
determining one or more second sub-images of a second region of interest by segmenting, based on the one or more second thresholds, the second image;
determining, based on the one or more second sub-images of the region of interest, a second quality index for the second image;
comparing the quality index and the second quality index; and
designating one of the image or the second image that has a larger quality index as a target image.

### Claim 11 (depends on 1)

The method of claim 1, wherein the region of interest includes one or more blood vessels.

### Claim 12 (depends on 1)

The method of claim 1, wherein the obtaining the image comprising:
obtaining projection data;
reconstructing a plurality of initial images based on the projection data;
determining a mean phase based on the plurality of initial images;
selecting one or more candidate cardiac motion phases in a preset phase range, the preset phase range including the mean phase;
obtaining an image of a region of interest by extracting the region of interest in each initial image of the preset phase range;
identifying a blood vessel centerline associated with the plurality of images of the region of interest; and
obtaining the image by segmenting, based on a preset region including the blood vessel centerline, one of the plurality of images of the region of interest.

### Claim 13 (depends on 1)

The method of claim 1, further comprising:
improving a resolution of the image using a two-dimensional image interpolation algorithm.

### Claim 14

A system for evaluating image quality, comprising:
at least one storage device storing a set of instructions; and
at least one processor in communication with the storage device, wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including:
obtaining an image;
determining one or more sub-images of a region of interest by segmenting the image; and
determining, based on the one or more sub-images of the region of interest, a quality index for the image, wherein the quality index is associated with a regularity degree of the image and a sharpness degree of the image.

### Claim 15 (depends on 14)

The system of claim 14, wherein the image includes a plurality of elements, each element of the elements is a pixel or voxel, and each element has a gray level.

### Claim 16 (depends on 15)

The system of claim 15, wherein to determine one or more sub-images of a region of interest by segmenting the image, the at least one processor is configured to cause the system to perform operations including:
determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; and
determining the one or more sub-images of the region of interest by segmenting, based on the one or more thresholds, the image.

### Claim 17 (depends on 14)

The system of claim 14, wherein to determine, based on the one or more sub-images of the region of interest, a quality index for the image, the at least one processor is configured to cause the system to perform operations including:
determining, based on the one or more sub-images of the region of interest, the regularity degree of the image;
determining, based on the one or more sub-images of the region of interest, the sharpness degree of the image; and
determining, based on the regularity degree and the sharpness degree, the quality index for the image.

### Claim 18 (depends on 17)

The system of claim 17, wherein to determine, based on the regularity degree and the sharpness degree, the quality index for the image, the at least one processor is configured to cause the system to perform operations including:
designating a weighted sum of the regularity degree and the sharpness degree as the quality index for the image.

### Claim 19 (depends on 17)

The system of claim 17, wherein to determine, based on the one or more sub-images of the region of interest, a quality index for the image, the at least one processor is further configured to cause the system to perform operations including:
identifying one or more connected domains in the one or more sub-images of the image;
determining a count of the identified connected domains in the image; and
in response to a determination that the count of the identified connected domains in the image is zero,
designating the regularity degree of the image as zero; and
designating the sharpness degree of the image as zero.

### Claim 20

A non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising:
obtaining an image;
determining one or more sub-images of a region of interest by segmenting the image; and
determining, based on the one or more sub-images of the region of interest, a quality index for the image, wherein the quality index is associated with a regularity degree of the image and a sharpness degree of the image.