CN114897861A - An image processing method and system - Google Patents

Abstract

The embodiment of the specification provides an image processing method and system, the method comprises the steps of obtaining original imaging data, wherein the original imaging data are obtained by scanning a target object through a medical device; determining scan information related to a scan of a target object; determining an image processing parameter based on the scanning information, the image processing parameter being a variable of a position in the target direction; and processing the raw imaging data based on the image processing parameters to generate target imaging data.

Description

An image processing method and system

technical field

The present specification relates to the technical field of image processing, and in particular, to a method and system for determining image processing parameters.

Background technique

Medical imaging can be applied to various medical treatments and/or diagnoses. In some medical imaging procedures, multiple body part scans or whole body scans of a target object (eg, a patient) are required along the axis of the medical device (eg, a long-axis PET device). Due to some factors (eg, the sensitivity of the detector of the medical device, the tracer activity of the target object, the physical characteristics of the target object, etc.) there are variations in the axial direction of the medical device, resulting in different regions in the axial direction. The quality of the imaging data (for example, PET scan data) varies significantly, resulting in a large difference in image quality of the reconstructed image in the axial direction of the medical device, which brings many unfavorable factors to the subsequent diagnosis and treatment of the disease. Therefore, it is desirable to provide an image processing method and system, which can flexibly control the image quality of the image in the axial direction of the medical device.

SUMMARY OF THE INVENTION

One of the embodiments of this specification provides an image processing method. The image processing method includes: acquiring original imaging data obtained by scanning a target object with a medical device; determining scan information related to the scan of the target object; and determining an image based on the scan information processing parameters, the image processing parameters being variables of position in the target direction; and processing the raw imaging data based on the image processing parameters to generate target imaging data.

In some embodiments, the scan information includes at least one of: information about the medical device, information about the raw imaging data, or information about the target object.

In some embodiments, the medical device includes a positron emission tomography device, and the information related to the medical device includes the sensitivity of a detector of the medical device, a gap between adjacent detectors, and a detection efficiency of the detector. At least one of the information related to the original imaging data includes at least one of coincident event count information and tracer activity, and the information related to the target object includes feature information of the target object and historical imaging data. at least one of.

In some embodiments, processing the raw imaging data based on the image processing parameters includes multiple rounds of iterations, wherein at least one iteration includes: acquiring updated imaging data generated in a previous round of iterations; processing based on the image parameters process the updated imaging data to generate processed imaging data; determine whether the iteration satisfies a termination condition; and in response to determining that the iteration satisfies the termination condition, determine the processed imaging data as the target imaging data.

In some embodiments, processing the updated imaging data based on the image processing parameters to generate processed imaging data comprises: based on the updated imaging data and the scan information, updating the image processing parameters to generating updated image processing parameters; and processing the updated imaging data based on the updated image processing parameters to generate processed imaging data.

In some embodiments, the processing comprises image reconstruction processing, and the processing the updated imaging data based on the image processing parameters to generate processed imaging data comprises: obtaining an iterative update factor; and based on the iterative The update factor and the image processing parameters are reconstructed on the updated imaging data to generate a reconstructed image.

In some embodiments, the acquiring the iterative update factor includes: performing an orthographic operation on the updated imaging data to determine first projection data; determining second projection data based on the original imaging data; and based on the first projection data and the second projection data, determine third projection data; perform a back-projection operation on the third projection data to determine back-projection data; and according to the first projection data, the back-projection data and normalization A matrix determines the iterative update factor.

In some embodiments, the processing includes at least one of the following: image smoothing processing, image enhancement processing, image fusion processing, and image beautification processing.

One of the embodiments of this specification provides an image processing system. The image processing system includes at least one processor and at least one memory. The at least one memory is used to store computer instructions. The at least one processor is configured to execute at least part of the computer instructions to implement the image processing method described in this specification.

One of the embodiments of this specification provides an image processing system. The image processing system includes an acquisition module, a scan information determination module, a processing parameter determination module and a processing module. The acquisition module is used for acquiring original imaging data, where the original imaging data is acquired by scanning a target object with a medical device. The scan information determination module is configured to determine scan information related to the scan of the target object. The processing parameter determination module is configured to determine an image processing parameter based on the scanning information, where the image processing parameter is a variable of the position in the target direction. The processing module is configured to process the original imaging data based on the image processing parameters to generate target imaging data.

Description of drawings

The present specification will be further described by way of example embodiments, which will be described in detail with reference to the accompanying drawings. These examples are not limiting, and in these examples, the same numbers refer to the same structures, wherein:

1 is a schematic diagram of an application scenario of an image processing system according to some embodiments of this specification;

FIG. 2 is an exemplary flowchart of generating target imaging data according to some embodiments of the present specification;

3 is an exemplary flowchart of generating target imaging data according to some embodiments of the present specification;

FIG. 4 is an exemplary flowchart of generating a reconstructed image of a target according to some embodiments of the present specification;

FIG. 5 is an exemplary flowchart of obtaining an iterative update factor according to some embodiments of the present specification;

FIG. 6 is an exemplary block diagram of a processing device according to some embodiments of the present specification;

7 is a graph of the relationship between image smoothing parameters and instant coincidence event counts according to some embodiments of the present specification;

8 is a graph of the relationship between image smoothing parameters and instant coincidence event counts according to some embodiments of the present specification;

FIG. 9 is a schematic diagram of a reconstructed image according to some embodiments of the present specification.

Detailed ways

In order to illustrate the technical solutions of the embodiments of the present specification more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present specification. For those of ordinary skill in the art, without creative efforts, the present specification can also be applied to the present specification according to these drawings. other similar situations. Unless obvious from the locale or otherwise specified, the same reference numbers in the figures represent the same structure or operation.

It is to be understood that "system", "device", "unit" and/or "module" as used herein is a method used to distinguish different components, elements, parts, parts or assemblies at different levels. However, other words may be replaced by other expressions if they serve the same purpose.

As shown in the specification and claims, unless the context clearly dictates otherwise, the words "a", "an", "an" and/or "the" are not intended to be specific in the singular and may include the plural. Generally speaking, the terms "comprising" and "comprising" only imply that the clearly identified steps and elements are included, and these steps and elements do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

Flowcharts are used in this specification to illustrate operations performed by a system according to an embodiment of this specification. It should be understood that the preceding or following operations are not necessarily performed in the exact order. Instead, the various steps can be processed in reverse order or simultaneously. At the same time, other actions can be added to these procedures, or a step or steps can be removed from these procedures.

FIG. 1 is a schematic diagram of an application scenario of an image processing system according to some embodiments of this specification. The image processing system 100 may include a processing device 110 , a medical device 120 , one or more terminals 130 , a network 140 and a storage device 150 .

Components in image processing system 100 may be connected in various ways. For example only, the medical device 120 may be connected to the processing device 110 directly (as shown by the dashed bidirectional arrow connecting the medical device 120 and the processing device 110 ) or through the network 140 . As yet another example, storage device 150 may be connected to medical device 120 directly (as shown by the dashed double-headed arrow connecting storage device 150 and medical device 120 ) or through network 140 . As yet another example, the terminal 130 may be connected to the processing device 110 directly (as shown by the dashed bidirectional arrow connecting the terminal 130 and the processing device 110 ) or through the network 140 .

The processing device 110 may process data and/or information obtained from the medical device 120 , the terminal 130 and/or the storage device 150 . For example, the processing device 110 may obtain raw imaging data of the target object from the medical device 120 . For another example, the processing device 110 may obtain scan information related to the scan of the target object from the storage device 150 . As another example, the processing device 110 may determine image processing parameters based on the scan information. For another example, the processing device 110 may process the raw imaging data based on the image processing parameters to generate target imaging data. In some embodiments, processing device 110 may be a single server or a group of servers. Server groups can be centralized or distributed. In some embodiments, processing device 110 may be a local component or a remote component with respect to one or more other components of image processing system 100 . For example, processing device 110 may access information and/or data stored in medical device 120 , terminal 130 and/or storage device 150 via network 140 .

The medical device 120 may scan the target object to obtain scan data (eg, raw imaging data) of the target object. In some embodiments, target objects may include biological objects and/or non-biological objects. For example, the target object may include a specific part of the human body, such as the head, chest, abdomen, etc., or a combination thereof. As another example, the target object may be a patient to be scanned by the medical device 120 . In some embodiments, scan data related to the target object may include biological data collected by the medical device 120 (eg, projection data of the target object), one or more scan images, and the like.

In some embodiments, the medical device 120 may be a non-invasive medical imaging device used for disease diagnosis or research purposes. For example, the medical device 120 may include a single-modality scanning device and/or a multi-modality scanning device. Single-modality scanning equipment may include ultrasound scanning equipment, X-ray scanning equipment, computed tomography (CT) scanning equipment, magnetic resonance imaging (MRI) scanning equipment, ultrasound examination equipment, positron emission tomography (PET) scanning equipment, optical Coherence tomography (OCT) scanning equipment, ultrasound (US) scanning equipment, intravascular ultrasound (IVUS) scanning equipment, near infrared spectroscopy (NIRS) scanning equipment, far infrared (FIR) scanning equipment, etc. or any combination thereof. Multimodality scanning devices may include X-ray imaging-magnetic resonance imaging (X-ray-MRI) scanning devices, positron emission tomography-X-ray imaging (PET-X-ray) scanning devices, single-photon emission computed tomography-magnetic resonance imaging Imaging (SPECT-MRI) scanning equipment, positron emission tomography-computed tomography (PET-CT) scanning equipment, digital subtraction angiography-magnetic resonance imaging (DSA-MRI) scanning equipment, etc. The medical device 120 is provided above for illustrative purposes only and is not intended to limit the scope of the present application. As used in this specification, the term "imaging modality" or "modality" refers to an imaging method or technique that collects, generates, processes and/or analyzes imaging information of a target object.

In some embodiments, the medical device 120 (eg, a PET device) may include a gantry, a detector, and a couch. The rack can support the detector. The target object may be placed on the scan bed and moved in an axial direction of the medical device 120 (eg, the Z-axis direction shown in FIG. 1 ) to scan the target object. In some embodiments, the detector may include one or more detector units. Detectors may include scintillation detectors (eg, cesium iodide detectors), gas detectors, and the like.

In some embodiments, the medical device 120 may be a long-axis PET device. The axial length of the long-axis PET device (eg, the length in the Z-axis direction shown in Figure 1) is usually large (eg, greater than or equal to 0.75 m), so the long-axis PET device usually has a longer axial scanning field of view, Multiple parts of the target object can be scanned and imaged at the same time. For example, the scanning field of view of a long-axis PET device can cover the entire body of the target object, that is, a single bed can cover the entire body of the target object during the scanning process, thereby ensuring that the radioactive tracer drug anywhere in the target object can be covered and detected. To this, the whole body imaging of the target object can be completed in a single scan.

Terminal 130 may include mobile device 130-1, tablet computer 130-2, laptop computer 130-3, etc., or any combination thereof. In some embodiments, one or more terminals 130 may be part of processing device 110 .

Network 140 may include any suitable network that may facilitate the exchange of information and/or data for image processing system 100 . In some embodiments, one or more components of image processing system 100 (eg, medical device 120 , terminal 130 , processing device 110 , storage device 150 ) may communicate information via network 140 with one or more other components of image processing system 100 and/or data. For example, processing device 110 may obtain imaging data from medical device 120 via network 140 . As another example, the processing device 110 may obtain user instructions from the terminal 130 via the network 140 .

Storage device 150 may store data, instructions, and/or any other information. In some embodiments, storage device 150 may store data obtained from terminal 130 and/or processing device 110 . In some embodiments, storage device 150 may store data and/or instructions that processing device 110 may execute or use to execute the example methods described in this specification.

In some embodiments, storage device 150 may be connected to network 140 to communicate with one or more other components of image processing system 100 (eg, processing device 110, terminal 130). One or more components of image processing system 100 may access data or instructions stored in storage device 150 via network 140 . In some embodiments, storage device 150 may be directly connected to or in communication with one or more other components of image processing system 100 (eg, processing device 110, terminal 130). In some embodiments, storage device 150 may be part of processing device 110 .

In some embodiments, location information in image processing system 100 may be represented in coordinate system 160 as shown in FIG. 1 . The coordinate system 160 may include an X-axis, a Y-axis, and a Z-axis. As shown in FIG. 1 , the positive X-axis direction may be a direction from the left side to the right side of the scanning bed when viewed from a direction facing the front side of the medical device 120 . The positive Y-axis direction may be a direction from the lower portion of the medical device 120 to the upper portion of the medical device 120 . The positive Z-axis direction may be the direction in which the scan table moves from the inside to the outside of the medical device 120 . In some embodiments, the Z-axis direction may also be referred to as the axial direction of the medical device 120 .

It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of the present application. Numerous changes and modifications may be made to those of ordinary skill in the art under the guidance of the contents of this application. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. However, such changes and modifications do not depart from the scope of this application.

FIG. 2 is an exemplary flowchart of generating target imaging data according to some embodiments of the present specification. In some embodiments, process 200 may be performed by processing device 110 . As shown in FIG. 2, the process 200 includes the following steps.

Step 210, acquiring raw imaging data. In some embodiments, step 210 may be performed by acquisition module 610 .

Raw imaging data may refer to data to be subjected to image processing. The raw imaging data may be data obtained by scanning a target object with a medical device (eg, the medical device 120 ). Medical equipment may include PET equipment, SPECT equipment, MRI equipment, CT equipment, and the like. The raw imaging data may include raw data acquired in a scan of the target object and/or scan images generated based on the raw data. For example, raw imaging data may include PET data, SPECT data, MRI data, CT data, and the like. For example only, the raw imaging data may be PET projection data obtained by scanning a target object with a PET device. PET projection data may include list mode data or sinogram data.

In some embodiments, the raw imaging data may include scanned images. For example, the processing device 110 may generate a PET image based on the PET projection data. In some embodiments, the processing device 110 may generate a PET image based on the PET projection data according to an image reconstruction algorithm. The PET image can present the uptake of the tracer by the subject. Exemplary image reconstruction algorithms may include iterative algorithms, analytical algorithms, and the like. Iterative algorithms may include maximum likelihood estimation (MLE) algorithms, ordered subset expectation maximization (OSEM), and the like. Analysis algorithms may include Filtered Back Projection (FBP) algorithms and the like.

In some embodiments, processing device 110 may obtain raw imaging data via network 140 from one or more components of image processing system 100 (eg, medical device 120, terminal 130, storage device 150) or external memory. For example, the medical device 120 may transmit the acquired raw imaging data to a storage device (eg, the storage device 150, an external storage device) for storage. The processing device 110 may obtain raw imaging data from a storage device. As another example, the processing device 110 may obtain raw imaging data directly from the medical device 120 .

In step 220, scan information related to the scan of the target object is determined. In some embodiments, step 220 may be performed by scan information determination module 620 .

The scan information may refer to information about parameters that vary in the direction of the target. For example only, the target direction may be the axial direction of the medical device (eg, the Z-axis direction shown in FIG. 1 ). In some embodiments, the scan information may include information related to the medical device, information related to the raw imaging data , information about the target object, etc., or any combination thereof. For illustrative purposes, the types of scan information are described below using a PET device as an example. Example information related to the medical device may include the sensitivity of the detectors, the gap between adjacent detectors, the detection efficiency of the detectors, etc., or any combination thereof.

During a PET scan, a tracer is first injected into the target subject. Tracers can undergo positron emission decay and emit positrons. Positrons and electrons have the same mass and opposite charges, and when the two particles collide, positrons and electrons (the electrons are abundant in the body of the object) can annihilate (also known as "annihilation events" or "coincidence events"). Electron-positron annihilation can cause two particles (eg, two 511 keV gamma photons) to start traveling in opposite directions from each other. In a PET scan of a target object, the particles produced by the annihilation event reach the detector of the PET device and are detected by the detector.

The sensitivity of the detector refers to the detection efficiency of the detector. Taking PET equipment as an example, the sensitivity of the detector can refer to the number of coincident events detected by the detector under the condition of unit radiation dose per unit time. The higher the sensitivity of the detector, the more signals are detected for the same activity of the radioactive source, and the higher the image quality is obtained accordingly. The detection efficiency of a detector refers to the probability that when a gamma photon passes through the detector crystal, it can be recorded. The higher the detection efficiency of the detector, the higher the quality of the image obtained. In some embodiments, a medical device may include a detector array composed of a plurality of detectors (eg, LSO crystals, LYSO crystals, BGO crystals, LaBr crystals), with detectors between adjacent detectors (eg, adjacent crystals) The smaller the gap, the higher the detection efficiency of the detector. Larger gaps between adjacent detectors (eg, adjacent crystals) result in more missing PET data acquired, affecting image quality.

Information related to the raw imaging data may include coincidence event count information, tracer activity, etc., or any combination thereof. In some embodiments, coincidence events may include instant coincidence events, true coincidence events, random events, and scatter events. All compliance events detected by the detector are called instant compliance events. When a pair of detector units detects two incident photons (also called coincident photons) from the same annihilation event within a certain time window, it is called a true coincidence event. When a pair of detector units detects two incident photons from two annihilation events within a certain time window, it is called a random event. In some embodiments, photons generated by an annihilation event may experience Compton scattering while passing through a target object, when at least one of the two incident photons detected within a certain time window undergoes before it reaches the detector unit When at least one Compton scattering occurs, it is called a scattering event.

The tracer activity can reflect the biological activity information of the target object. For example, one or more atoms of the tracer can be chemically bound to a biologically active molecule in the target subject. Active molecules can be concentrated in tissues of interest within the target subject. Tracers may include [ ¹⁵ O]H ₂ O, [ ¹⁵ O]butanol, [ ¹¹ C]butanol, [ ¹⁸ F]fluorodeoxyglucose (FDG), [ ⁶⁴ Cu]diacetyl-bis( ⁶⁴ Cu -ATSM), [ ¹⁸ F]fluoride, 3'-deoxy-3'-[ ¹⁸ F]fluorothymidine (FLT), [ ¹⁸ F]-flumididazole (FMISO), gallium, thallium, etc. or their any combination.

The information related to the target object may include characteristic information of the target object, historical imaging data, etc., or any combination thereof. The characteristic information of the target object may include the target object's body type (eg, height, body width, body thickness), weight, proportion information of various body parts, body disease information, physiological information (eg, blood glucose concentration), anatomical structure information, etc. or any combination thereof. For example, elevated blood glucose concentrations in the target subject can promote insulin secretion, which accelerates glucose uptake (eg, FDG) by insulin-sensitive tissues (eg, cardiac, adipose, skeletal muscle), whereas tumor and brain tissue uptake correspondingly decrease, resulting in changes in the distribution of FDG in the target subject. The anatomical structure information of the target object may include any combination of positions, shapes, etc. of the organs or tissues of the target object. The historical imaging data of the target object may include historical scan data (eg, PET scan data, CT scan data, MRI scan data) acquired in historical scans (eg, PET scans, CT scans, MRI scans) of the target object.

In some embodiments, processing device 110 may obtain scan information (eg, medical device 120 , terminal 130 , storage device 150 ) or external memory related to the scan of the target object from one or more components of image processing system 100 (eg, medical device 120 , terminal 130 , storage device 150 ) , information related to medical equipment, information related to target objects). In some embodiments, the processing device 110 may determine scan information (eg, coincidence event count information, tracer activity) related to the scan of the target object from the raw imaging data. For example, the processing device 110 may determine the activity value (or concentration value) of the tracer within the target object based on pixel values (or voxel values) of the PET image of the target object. In some embodiments, the processing device 110 may determine feature information and/or anatomical structure information of the target object from historical imaging data of the target object.

Step 230: Determine image processing parameters based on the scan information. In some embodiments, step 230 may be performed by process parameter determination module 630 .

Image processing parameters may be used to process raw imaging data (eg, image reconstruction processing, image smoothing processing, image enhancement processing, image fusion processing, image beautification processing). In some embodiments, image processing parameters may include image reconstruction parameters, image smoothing parameters (eg, regularization iterative smoothing parameters, artificial intelligence iterative smoothing parameters, post-filtering smoothing parameters), image enhancement parameters, image fusion parameters, image beautification parameters etc. or any combination thereof. In some embodiments, the image processing parameters may include parameters related to image processing algorithms (eg, image reconstruction processing algorithms, image smoothing processing algorithms, image enhancement processing algorithms, image fusion processing algorithms, image beautification processing algorithms).

In some embodiments, the image processing parameter may be a variable of position in the direction of the target. In this specification, if there are at least two different positions in the axial direction and the corresponding image processing parameters are different, the image processing parameters can be considered as variables of the positions in the target direction. For example only, the target direction may be the axial direction of the medical device (eg, the Z-axis direction shown in FIG. 1 ). If there are two or more positions with different coordinates in the axial direction and the corresponding image processing parameters are different (for example, the image processing parameters corresponding to the head and abdomen of the target object are different), the image parameters can be considered as the axial position. variable. In some embodiments, when a medical device (for example, a long-axis PET device) is used to scan a target object, due to some factors that affect the imaging quality, there are changes in the target direction, resulting in scans corresponding to different positions in the target direction. The data quality is different, so that the quality of the parts corresponding to different axial positions in the reconstructed image is different, which affects the subsequent image-based disease diagnosis and treatment. For example, at different axial positions, the detector sensitivity of the medical device, the organ or tissue being scanned, coincident event count rate, tracer activity, etc., may vary, affecting the quality of scan data and reconstructed images.

In the embodiments in this specification, by setting different image processing parameters for different positions in the target direction, the image quality (eg, spatial resolution, density resolution, signal-to-noise ratio) of the image in the target direction can be controlled. For example, by setting different image processing parameters for different positions in the target direction, the difference in image quality at different positions in the target direction can be reduced. For another example, specific image processing parameters may be set for a specific organ (eg, head and chest) of the target object in the target direction, so that the organ in the reconstructed image has a specific optimization effect. For another example, for a position on the edge in the target direction, the noise of the corresponding area in the reconstructed image can be reduced by setting specific image processing parameters.

In some embodiments, processing device 110 may determine image processing parameters based on the scan information. In some embodiments, the processing device 110 may determine an image processing parameter corresponding to a specific position based on scan information corresponding to a specific position in the target direction. For example, the processing device 110 may be based on information related to the medical device corresponding to a specific position in the target direction (eg, the sensitivity of the detector corresponding to the specific position, the gap between adjacent detectors corresponding to the specific position, the detection efficiency of the detector), information related to raw image data (e.g., coincident event count information corresponding to a specific location, tracer activity corresponding to a specific location), and/or information related to the target object (e.g., specific location The type and structure of the organs and tissues of the corresponding target object), and the image processing parameters corresponding to the specific position are determined. For example only, the processing device 110 may determine image processing parameters corresponding to the patient's chest region based on scan information (eg, compliance event count information, tracer activity) corresponding to the patient's chest region. The processing device 110 may determine image processing parameters corresponding to the patient's abdominal region based on scan information (eg, coincidence event count information, tracer activity) corresponding to the patient's abdominal region.

In some embodiments, the processing device 110 may determine the image processing parameter corresponding to the specific position based on the scanning information related to the specific position in the target direction and the reference position of the position. In some embodiments, a reference location for a particular location may include locations that are adjacent to the location (eg, locations that are less than a certain threshold distance from the location). For example, the processing device 110 may determine image processing parameters corresponding to the patient's chest region based on scan information corresponding to the patient's chest region, scan information corresponding to the neck region, and scan information corresponding to the abdomen region. By using the specific position and the scan information corresponding to the specific position reference position to determine the image processing parameter corresponding to the specific position, the determination of the image processing parameter can be made more reasonable and accurate. In some embodiments, a reference location for a particular location may include locations that are not adjacent to the location (eg, locations that are more than a certain threshold distance from the location). For example, the processing device 110 may determine the image processing parameters corresponding to the scan information corresponding to the patient's brain region based on the scan information corresponding to the patient's brain region and the scan information corresponding to the liver region. In some embodiments, when the scan information of a specific location is difficult to obtain, the image processing parameters corresponding to the specific location can be determined by acquiring the scan information of the reference location. For example, since blood vessels in the human body are interconnected, image processing parameters corresponding to a specific blood vessel area may be determined based on scan information (eg, tracer activity) of one or more reference blood vessel areas. In some embodiments, the processing device 110 may determine the image processing parameters corresponding to the specific position based on the scanning information related to the reference position of the specific position in the target direction; Scan the information to determine the image processing parameters corresponding to the reference position of the specific position. For example, the processing device 110 can determine the scan information corresponding to the patient's liver region based on the scan information corresponding to the patient's brain region; the processing device 110 can determine the scan information corresponding to the patient's brain region based on the scan information corresponding to the patient's liver region.

In some embodiments, a user of the image processing system 100 (eg, doctor, technician) can empirically determine the relationship between scan information and image processing parameters. In some embodiments, the relationship between scan information and image processing parameters can be determined by performing a simulated scan experiment on a phantom. As an example only, the image smoothing parameters of the PET image can be determined according to formula (1):

λ _Z = k·(promptCounts _Z ) ⁿ (1),

Among them, λ _Z represents the image smoothing parameter corresponding to the position Z on the axis; k and n represent fixed constants, which can be set according to experience; and promptCounts _Z represents the corresponding instant coincidence event count at the position Z on the axis.

It can be seen from the above formula (1) that there is a correlation between the image smoothing parameter and the instant coincidence event count. Since the instant coincidence event count is a variable of the position on the axis, the image smoothing parameter is also a variable of the position on the axis. The instant coincidence event counts corresponding to different positions on the axis may be different, so the image smoothing parameters corresponding to different positions on the axis may also be different. In some embodiments, the relationship between the image smoothing parameter and the instant coincidence event count may be represented by the curve 700 of FIG. 7 or the curve 800 of FIG. 8 . As shown in Figure 7, there can be a non-linear relationship between image smoothing parameters and instant coincidence event counts. It can be seen from Figure 7 that when the instant coincidence time count is less than 1×10 ⁵ , the image processing parameters decrease rapidly as the instant coincidence event count increases gradually. When the instant coincidence time count reaches 1×10 ⁵ , the As the event counts increase gradually, the image processing parameters slowly decrease. As shown in Figure 8, there can be a linear relationship between the image smoothing parameter and the instant coincidence event count. It can be seen from Figure 8 that the image processing parameters decrease linearly as the instant coincidence event count increases gradually.

In some embodiments, the processing device 110 may separately model various types of scan information, and the processing device 110 may multiply the modeling functions to determine the image processing parameters. For example, processing device 110 may process information related to medical devices (eg, sensitivity distribution of detectors, gaps between adjacent detectors, detection efficiency of detectors), information related to raw imaging data (eg, coincidence events count information) and information about the target object (eg, historical imaging data) to determine image processing parameters.

In some embodiments, the processing device 110 may determine the cause of the variance in noise in the PET images. For example, in the application scenario of two-dimensional PET scanning, assuming that PET scanning is performed on a uniform water model, the true coincidence count at a certain point on the reconstructed image is t _e , which means that the mean value of the current point is mean=t _e , and the current point generated Noise (Variance) can be determined by summing the effects of PET counts at different angles on it, ie variance = ∑ _m VAR _e , where VAR _e is equal to the samples from each of the m projection angles that contribute to the image element The sum of the weighted variances of , the signal-to-noise ratio can be determined according to formula (2):

Among them, SNR represents the signal-to-noise ratio; c represents a constant; mean represents the mean value of the current point; and variance represents the noise of the current point. For a certain response line sampling, assuming that the scatter count and random count are both correctly estimated, the expectation of the immediate count is:

E(p)=T _p +S _p +R _p (3),

Among them, T _p represents true coincidence counts; _{Sp represents scattering counts; R p represents} random counts.

According to the principle of Poisson distribution, variance is equal to expectation, we can get:

variance=∑ _m w _m (T _p +S _p +R _p )=∑ _m w _m T _p (1+α _sp +α _rp ) (4),

分别表示散射计数和随机计数与真实计数的比例。Among them, w _m is the noise weight of the mth angle response line;

represent the ratio of scatter counts and random counts to true counts, respectively.

Assuming that the signal-to-noise ratio of the center point of the image is obtained, due to the principle of symmetry, the weight (w) of its noise in all directions is the same, then the noise of each angle is:

VAR _e =w(Dt _e a _c /d)(1+α _sp +α _rp ) (5),

Among them, D is the diameter of the uniform water model; d is the number of pixels; a _c is the attenuation coefficient and dead time correction; w is the weight of the noise in each direction.

It can be further concluded that:

t _e =avg( _ac )T/πD ² /4d ² ) (6),

where T is the total true hit count.

Therefore, the signal-to-noise ratio can be determined by equation (7):

The noise equivalent count rate (NEC) can be determined by equation (8):

NEC=T/(1+ _αsp + _αrp )(8),

The single-layer NEC count can be determined by equation (9):

NEC=T ² /(T+S+R) (9),

Due to the different probability of detecting the emitted photons at each position in the system, the sensitivity of the system at different positions is different, and the SNR of the non-image center point and the SNR distribution of the center point are different. Therefore, the number of detectors for different positions in the system is different.

In some embodiments, assuming that there are two positions x and y, their mean and noise are:

mean _x = Sns _x t _e (10),

variance _x =E(Sns _x t _e )=Sns _x t _e (11),

mean _y = _{Sns y} ·te (12),

variance _y =E(Sns _y ·t _e )=Sns _y ·t _e (13),

Among them, Sns _x and Sns _y represent the system sensitivity.

Therefore, it can be deduced that:

成正比，因此NEC可以成为衡量图像噪声的依据。SNR与物体的直径有关系，因此病人体积可以成为衡量图像噪声的依据。SNR与系统灵敏度有关系，因此系统灵敏度可以成为衡量图像噪声的依据。From the above derivation, it can be seen that the SNR and

is proportional to, so NEC can be the basis for measuring image noise. SNR is related to the diameter of the object, so patient volume can be a measure of image noise. SNR is related to system sensitivity, so system sensitivity can be the basis for measuring image noise.

Similarly, in the application scenario of 3D PET scanning, the NEC of the target direction area, the patient volume in the target direction, and the system sensitivity in the target direction can be used to estimate the noise in the target direction. For example, for each area in the target direction, the NEC is marked as NEC _z , the object volume is marked as Volume _z , and the system sensitivity is marked as Sns _z , then the modeling of counting information based on image processing parameters is:

Among them, k is a constant; NEC _z , Volume _z and Sns _z are the variables of the position in the target direction, respectively.

Step 240 , based on the image processing parameters, process the original imaging data to generate target imaging data. In some embodiments, step 240 may be performed by processing module 640 .

The target imaging data may refer to data after processing the original imaging data. In some embodiments, processing the original imaging data may include image reconstruction processing, image smoothing processing, image enhancement processing, image fusion processing, image beautification processing, etc., or any combination thereof, on the original imaging data.

In some embodiments, the processing device 110 may process the raw imaging data to generate target imaging data according to an iterative image processing algorithm (eg, an iterative reconstruction algorithm) based on the image processing parameters. Iterative reconstruction algorithms may include maximum likelihood estimation (MLE) algorithms, ordered subset expectation maximization (OSEM), and the like. For example, the processing device 110 may generate target imaging data by performing multiple rounds of iterative operations on the raw imaging data based on the image processing parameters. For more description on generating target imaging data, see the related description of FIG. 3 . As another example, the processing device 110 may obtain an iterative update factor. The processing device 110 may generate the target reconstructed image by performing multiple rounds of iterative operations on the initialization image based on the iterative update factor and the image processing parameter. For more description about generating the reconstructed image of the target, please refer to the related description of FIG. 4 .

时的投影

的一维立叶变换是f(x,y0的二维傅立叶变换的一个经过原点的切片，因此根据中心切片定理推导出的重建图像I(x,y)的公式如下：In some embodiments, the processing device 110 may process the raw imaging data to generate target imaging data according to a non-iterative image processing algorithm (eg, a non-iterative reconstruction algorithm) based on the image processing parameters. Non-iterative reconstruction algorithms may include filtered backprojection reconstruction (FBP) algorithms and the like. For example, in the filtered back projection reconstruction (FBP) algorithm, according to the central slice theorem, it can be known that an image f(x, y0 has a viewing angle of

projection of time

The one-dimensional Liye transform of is a slice through the origin of the two-dimensional Fourier transform of f(x, y0, so the formula of the reconstructed image I(x, y) derived from the central slice theorem is as follows:

where h(s) represents the filter function. In some embodiments, the filter coefficient of the filter function may be adjusted according to different scan information corresponding to different positions in the target direction. For example, compared to using the same h(s) at different positions in the target direction in the traditional FBP reconstruction algorithm, in some embodiments of the present application, the filter function can be a variable of the position in the target direction, that is, the filter function can be:

In some embodiments, the processing device 110 may process the raw imaging data according to various image processing joint algorithms based on the image processing parameters to generate the target imaging data. For example, the processing device 110 may process the raw imaging data according to an image reconstruction algorithm (eg, a row processing maximum likelihood algorithm (RAMLA) algorithm) and a filtering algorithm based on image processing parameters to generate target imaging data. The process of image reconstruction according to the RAMLA algorithm can be expressed as:

Among them, j represents the pixel index, i represents the number of iterations, b represents the projection, a represents the system matrix, l represents the subset index, λ _z represents the convergence coefficient, and Sn represents the subset sequence. After the reconstructed image is completed, the image is filtered using a post-filtering algorithm adjusted for the target orientation (eg, Gaussian post-filtering):

Among them, σ _z represents the Gaussian width at half maximum, σ _z can be a variable of the position in the target direction; and x represents the distance between the neighborhood pixel and the center pixel. According to some embodiments of the present application, the noise distribution in an image can be equalized by using an image reconstruction algorithm and a filtering algorithm in combination.

FIG. 3 is an exemplary flowchart of generating target imaging data according to some embodiments of the present specification. In some embodiments, process 300 may be performed by processing device 110 (eg, processing module 640). In some embodiments, step 240 shown in FIG. 2 may be implemented by process 300 . As shown in FIG. 3, process 300 includes one or more iterations. Each iteration can include the following steps.

Step 310: Obtain original imaging data or updated imaging data generated in the previous iteration.

If the current iteration is the first iteration, the original imaging data to be subjected to image processing (eg, image reconstruction processing, image smoothing processing, image enhancement processing, image fusion processing, and image beautification processing) may be acquired. For example, the raw imaging data may be PET projection data (eg, list mode data, sinogram data), PET reconstructed images, and the like. In some embodiments, the original imaging data may be the PET reconstructed image generated according to the process 400, and the processing device 110 may further perform other image processing operations (eg, image smoothing, image enhancement, image fusion) on the PET reconstructed image according to the process 300. processing, image beautification processing). The acquisition of the original imaging data is similar to that described in step 210, and details are not repeated here.

If the current iteration is the second or later iteration, the updated imaging data generated in the previous iteration can be obtained.

Step 320: Process the original imaging data or the updated imaging data based on the image processing parameters to generate processed imaging data.

In some embodiments, the image processing parameters used in different iterations may be the same or different. In some embodiments, in the current iteration, the processing device 110 may update the image processing parameters based on the updated imaging data and scan information generated in the previous iteration to generate updated image processing parameters. For example, processing device 110 may determine tracer activity based on the updated imaging data (eg, updated PET images) and adjust image processing parameters based on the tracer activity to generate updated image processing parameters. Specifically, the pixel value (or voxel value) of the updated imaging data (eg, updated PET image) may represent the tracer activity distribution information in the human body, and the processing device 110 may voxel value) information, adjust the image processing parameters to generate updated image processing parameters. For another example, the value of the image processing parameter used in each iteration can be gradually decreased as the number of iterations increases, so as to optimize the convergence path and achieve a better convergence effect for image processing. The processing device 110 may process the updated imaging data based on the updated image processing parameters to generate processed imaging data. According to some embodiments of the present specification, by adjusting the image processing parameters in each iteration, the convergence speed of the iteration can be improved, thereby improving the efficiency of the iterative processing.

In some embodiments, process 300 may be used to smooth or enhance raw imaging data. At this time, the image processing parameter may be an image smoothing parameter or an image enhancement parameter. In step 320, the processing device 110 may perform image smoothing processing (or image enhancement processing) on the updated imaging data generated by the previous iteration based on the image smoothing parameters (or image enhancement parameters) and the prior function. The prior function can be used to constrain the prior distribution of the image distribution. For example, the prior function can be a Markov random field model that has a smoothing effect on the image. In some embodiments, process 300 may be used to reconstruct raw imaging data. At this time, the image processing parameters may be image reconstruction parameters. In step 320, the processing device 110 may process the updated reconstructed image generated by the previous iteration based on the image reconstruction parameters and the iterative update factor. For more description about image reconstruction, please refer to the related description of FIG. 4 .

Step 330, determine whether the iteration satisfies the termination condition.

In some embodiments, the termination condition may be related to the number of iterations performed. For example, the termination condition may be that the number of iterations performed is greater than a threshold number of times. In some embodiments, the termination condition may be related to the difference between the processed imaging data generated by two successive iterations. For example, the termination condition may be that the difference between the processed imaging data generated by two successive iterations is less than a difference threshold. In some embodiments, the difference between the first imaging data (eg, the first image) and the second imaging data (eg, the second image) may be determined by the difference between the average gray value of a pixel or voxel of the first imaging data and the A representation of the difference between the average grayscale values of the pixels or voxels of the second imaging data. In some embodiments, the termination condition may be related to the quality of the processed imaging data. For example, a termination condition may be that the quality of the processed imaging data (eg, spatial resolution, density resolution, signal-to-noise ratio) satisfies a quality threshold (eg, spatial resolution threshold, density resolution threshold, signal-to-noise ratio threshold). The count threshold and/or the difference threshold may be manually set by the user or set by one or more components of the image processing system 100 (eg, the processing device 110 ) on a case-by-case basis.

In step 340, in response to determining that the iteration satisfies the termination condition, the processed imaging data is determined as the target imaging data.

In some embodiments, in response to determining that the iteration satisfies the termination condition, the processed imaging data obtained in this round of iterations may be determined as the target imaging data. In some embodiments, in response to determining that the iteration does not meet the termination condition, the processed imaging data obtained in this round of iterations may be used as the updated imaging data, and the process returns to step 310 for the next round of iterations until a certain round of iterations is performed. The termination condition is satisfied.

It should be noted that the above description about the process is only for example and description, and does not limit the scope of application of the present specification. For those skilled in the art, various modifications and changes can be made to the procedures under the guidance of this specification. However, these corrections and changes are still within the scope of this specification.

FIG. 4 is an exemplary flowchart of generating a reconstructed image of a target according to some embodiments of the present specification. In some embodiments, process 400 may be performed by processing device 110 (eg, processing module 640). In some embodiments, step 240 shown in FIG. 2 may be implemented by process 400 . As shown in FIG. 4, process 400 includes one or more iterations. Each iteration can include the following steps.

Step 410: Obtain an initialization image or updated imaging data generated in a previous iteration.

If the current iteration is the first iteration, the initialization image can be obtained. In some embodiments, the initialization image may include at least two pixels or voxels having estimated characteristics, such as gray value, intensity, color, and the like. In some embodiments, the initialization image may be set by a user or determined by one or more components of image processing system 100 (eg, processing device 110 ). In some embodiments, the grayscale values of pixels or voxels in the initialization image may be set to different values or the same value. For example, the gray values of pixels or voxels in the initialization image may all be set to 0.

If the current iteration is the second or later iteration, the updated imaging data generated in the previous iteration can be obtained.

Step 420 , reconstructing the initialization image or the updated image data based on the iterative update factor and the image processing parameters to generate a reconstructed image.

The iterative update factor can refer to modeling multiple physical factors (e.g., particle collisions, detector efficiency, and randomness of events) based on the correlation between the number of photons emitted within the tissue and the number of photons received by the detector. Differences between the deduced reconstructed initial image and the real tissue photon distribution image. In some embodiments, an iterative update factor may be determined according to process 500 . In some embodiments, during each iteration, the processing device 110 may reconstruct the initialization image or updated imaging data generated by the previous iteration based on the image processing parameters and the iteration update factor to generate a reconstructed image. In each iteration, the image processing parameters may be the same or different, as described in step 320 in FIG. 3 .

In some embodiments, the processing device 110 may reconstruct the initialization image according to an iterative reconstruction algorithm, based on the iterative update factor and the image processing parameters, to generate a reconstructed image. In the traditional iterative reconstruction process, the image processing parameters are not variables of the position in the target direction (that is to say, different positions in the target direction use the same image processing parameters), and the iterative reconstruction process can be expressed as formula (20):

f ⁿ⁺¹ = f ⁿ +λG(f) (20),

Among them, f ⁿ⁺¹ represents the image generated by the current iteration; f ⁿ represents the image generated by the previous iteration; λ represents the image processing parameter (also called the image reconstruction parameter or iterative adjustment factor), which can represent the image reconstruction parameter Convergence step size; G(f) represents the iterative update factor. According to some embodiments of the present specification, the image processing parameter may be a variable of the position in the target direction, and the iterative reconstruction process at this time may be expressed as formula (21):

f ⁿ⁺¹ = f ⁿ +λ _z G(f) (21),

where λ _Z represents the image processing parameter at the Z position in the target direction. Just as an example, λ _Z represents an image processing parameter at a Z position in the axial direction, and the image processing parameters corresponding to different Z positions are different.

In some embodiments, formula (3) may also represent other image processing iterative processes such as image smoothing, image noise reduction, and image enhancement. For example, during image smoothing iterations (eg, regularization iterations), λ _Z represents the image smoothing parameter (eg, smoothing intensity coefficient) at the Z position in the target direction.

Step 430, determine whether the iteration satisfies the termination condition.

Step 430 is similar to step 330 and will not be repeated here.

Step 440, in response to determining that the iteration satisfies the termination condition, determine the reconstructed image as the target reconstructed image.

In some embodiments, in response to determining that the iteration satisfies the termination condition, the reconstructed image obtained in the current iteration may be determined as the target reconstructed image. In some embodiments, in response to determining that the iteration does not meet the termination condition, the reconstructed image obtained in this round of iterations may be used as the updated imaging data, and the process returns to step 410 for the next round of iterations until the termination condition is reached in a certain round of iterations satisfied.

FIG. 5 is an exemplary flowchart of obtaining an iterative update factor according to some embodiments of the present specification. In some embodiments, process 500 may be performed by processing device 110 (eg, processing module 640). As shown in FIG. 5, the process 500 includes the following steps.

Step 510: Perform an orthographic projection operation on the initialization image or the updated imaging data to determine the first projection data.

In some embodiments, during the first round of iterations, the processing device 110 may determine the first projection data by performing an orthographic projection operation on the initialization image. In a subsequent iteration process, the processing device 110 may perform an orthographic projection operation on the updated imaging data (eg, updated reconstructed image) generated by the previous iteration to determine the first projection data.

In some embodiments, processing device 110 may determine the first projection data by projecting an initialization image (or update imaging data) onto a particular projection plane. In some embodiments, the processing device 110 may determine the first projection data based on the initialization image (or the updated imaging data) and the projection matrix. For example, the processing device 110 may determine the first projection data by multiplying the projection matrix by the initialization image. In some embodiments, the projection matrix may be set by a user or by one or more components of image processing system 100 (eg, processing device 110 ) on a case-by-case basis.

Step 520, determining second projection data based on the original imaging data.

In some embodiments, the raw imaging data may be raw projection data collected by a medical device, which may be directly used as the second projection data. Alternatively, the processing device 110 may preprocess the raw projection data (eg, PET projection data) and use the preprocessed raw projection data as the second projection data. In some embodiments, the original imaging data may be an image, and the processing device 110 may acquire original projection data corresponding to the image, and use the original projection data of the corresponding image as the second projection data. Alternatively, the processing device 110 may perform an orthographic operation on the image to determine the second projection data.

Step 530: Determine third projection data based on the first projection data and the second projection data.

The third projection data may represent the difference between the first projection data and the second projection data. In some embodiments, the third projection data may be a ratio of the first projection data to the second projection data. In some embodiments, the third projection data may be the difference between the first projection data and the second projection data. For example, the processing device 110 may determine the third projection data by subtracting the first projection data and the second projection data.

Step 540: Perform a back-projection operation on the third projection data to determine back-projection data.

In some embodiments, processing device 110 may perform a backprojection operation (eg, a filtered backprojection operation) on the third projection data to determine backprojection data.

Step 550: Determine an iterative update factor according to the first projection data, the back projection data and the normalization matrix.

In some embodiments, the processing device 110 may acquire an image with an initialization value of 1, and determine a normalization matrix based on the image with an initialization value of 1. For example, the processing device 110 may determine the normalization matrix by performing a back-projection operation on an image whose initialization value is 1.

In some embodiments, the processing module 640 may determine the iterative update factor based on the first projection data, the back projection data, and the normalization matrix.

It should be noted that the above description about the process is only for example and description, and does not limit the scope of application of the present specification. For those skilled in the art, various modifications and changes can be made to the procedures under the guidance of this specification. However, these corrections and changes are still within the scope of this specification.

FIG. 6 is an exemplary block diagram of an image processing system according to some embodiments of the present specification. In some embodiments, the processing device 110 may include an acquisition module 610 , a scan information determination module 620 , a processing parameter determination module 630 and a processing module 640 .

The acquisition module 610 may be used to acquire information and data related to the image processing system 100 . In some embodiments, acquisition module 610 may acquire raw imaging data. Further description of acquiring raw imaging data can be found elsewhere in this specification (eg, step 210 in FIG. 2 and its description).

The scan information determination module 620 may be used to determine scan information related to the scan of the target object. The scan information may include information about the medical device, information about the raw imaging data, information about the target object, etc., or any combination thereof. More descriptions of determining scan information related to scans of target objects can be found elsewhere in this specification (eg, step 220 in FIG. 2 and its description).

Processing parameter determination module 630 may be used to determine image processing parameters. In some embodiments, the processing parameter determination module 630 may determine image processing parameters based on the scan information. For example, the processing parameter determination module 630 may determine the image processing parameter corresponding to the specific position based on the scanning information corresponding to the specific position on the target direction. For another example, the processing parameter determination module 630 may determine the image processing parameter corresponding to the specific position based on the scanning information related to the specific position in the target direction and the reference position of the position. Further description of determining image processing parameters can be found elsewhere in this specification (eg, step 230 in FIG. 2 and its description).

The processing module 640 may be configured to process the raw imaging data based on the image processing parameters to generate target imaging data. In some embodiments, the processing module 640 may acquire raw imaging data or updated imaging data generated in a previous iteration. The processing module 640 may process the original imaging data or the updated imaging data based on the image processing parameters to generate processed imaging data. The processing module 640 can determine whether the iteration satisfies the termination condition. The processing module 640 may determine the processed imaging data as the target imaging data in response to determining that the iteration satisfies the termination condition. In some embodiments, the processing module 640 may obtain an initialization image or updated imaging data generated in a previous iteration. The processing module 640 may reconstruct the initialization image or update image data based on the iterative update factor and the image processing parameters to generate a reconstructed image. The processing module 640 can determine whether the iteration satisfies the termination condition. The processing module 640 may determine the reconstructed image as the target reconstructed image in response to determining that the iteration satisfies the termination condition. Further description of generating target imaging data can be found elsewhere in this specification (eg, step 240 in Figure 2, Figures 3-5 and their descriptions).

It should be noted that the above description of the processing device 110 is for illustrative purposes and not to limit the scope of the present application. For those of ordinary skill in the art, various changes and modifications can be made based on the description of the present application. However, these changes and modifications do not depart from the scope of this application. In some embodiments, one or more modules may be combined into a single module. For example, scan information determination module 620 and processing parameter determination module 630 may be combined into a single module. In some embodiments, one or more modules may be added or omitted from processing device 110 . For example, processing device 110 may also include a storage module (not shown in FIG. 6 ) configured to store data and/or information associated with image processing system 100 (eg, raw imaging data, scan information, image processing parameters, target imaging data).

FIG. 9 is a schematic diagram of a reconstructed image according to some embodiments of the present specification. As shown in FIG. 9, the reconstructed image 910A and the reconstructed image 920A are reconstructed images of the target object generated according to a maximum intensity projection (MIP) algorithm. Reconstructed image 910B and reconstructed image 920B are reconstructed images corresponding to cross-sections of the target object. The reconstructed image 910A and the reconstructed image 910B are images obtained after processing based on conventional image smoothing parameters (ie, the image smoothing parameters are fixed amounts in the target direction). The reconstructed image 920A and the reconstructed image 920B are images obtained after processing based on the image smoothing parameters determined in the embodiments of the present specification (ie, the image smoothing parameters are variables of the position in the target direction). As can be seen in Figure 9, reconstructed image 920A and reconstructed image 920B are smoother and have significantly less noise (eg, image edge noise) than reconstructed image 910A and reconstructed image 910B.

The possible beneficial effects of the embodiments of this specification include, but are not limited to: (1) by setting the image processing parameter as a variable of the position in the target direction, the image quality of the image in the target direction can be flexibly controlled; (2) based on the target direction The scanning information related to the scanning of the target object in the direction determines the image processing parameters, which can reduce the image difference at different positions in the target direction, so as to obtain a relatively uniform image quality; (3) The predetermined position in the target direction can achieve the expected Image quality, for example, specific image processing parameters can be set for a specific organ region of the target object, so as to obtain a specific optimized image effect for a specific organ; for another example, for the edge layer region with large noise in the image, it can be set corresponding to the region. (4) According to the characteristic information of different patients and/or different scan-related information, different image processing parameters can be determined, so as to realize the specific image processing parameters for different patients and/or different scans. The personalized image processing parameter settings can further improve the quality of target imaging data. It should be noted that different embodiments may have different beneficial effects, and in different embodiments, the possible beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is merely an example, and does not constitute a limitation of the present specification. Although not explicitly described herein, various modifications, improvements, and corrections to this specification may occur to those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

Meanwhile, the present specification uses specific words to describe the embodiments of the present specification. Such as "one embodiment," "an embodiment," and/or "some embodiments" means a certain feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places in this specification are not necessarily referring to the same embodiment . Furthermore, certain features, structures or characteristics of the one or more embodiments of this specification may be combined as appropriate.

Furthermore, unless explicitly stated in the claims, the order of processing elements and sequences described in this specification, the use of alphanumerics, or the use of other names is not intended to limit the order of the processes and methods of this specification. While the foregoing disclosure discusses by way of various examples some embodiments of the invention that are presently believed to be useful, it is to be understood that such details are for purposes of illustration only and that the appended claims are not limited to the disclosed embodiments, but rather The requirements are intended to cover all modifications and equivalent combinations falling within the spirit and scope of the embodiments of this specification. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described systems on existing servers or mobile devices.

Similarly, it should be noted that, in order to simplify the expressions disclosed in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, various features may sometimes be combined into one embodiment, in the drawings or descriptions thereof. However, this method of disclosure does not imply that the subject matter of the description requires more features than are recited in the claims. Indeed, there are fewer features of an embodiment than all of the features of a single embodiment disclosed above.

Some examples use numbers to describe quantities of ingredients and attributes, it should be understood that such numbers used to describe the examples, in some examples, use the modifiers "about", "approximately" or "substantially" to retouch. Unless stated otherwise, "about", "approximately" or "substantially" means that a variation of ±20% is allowed for the stated number. Accordingly, in some embodiments, the numerical parameters set forth in the specification and claims are approximations that can vary depending upon the desired characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and use a general digit reservation method. Notwithstanding that the numerical fields and parameters used in some embodiments of this specification to confirm the breadth of their ranges are approximations, in specific embodiments such numerical values are set as precisely as practicable.

For each patent, patent application, patent application publication, and other material, such as article, book, specification, publication, document, etc., cited in this specification, the entire contents of which are hereby incorporated by reference into this specification are hereby incorporated by reference. Application history documents that are inconsistent with or conflict with the contents of this specification are excluded, as are documents (currently or hereafter appended to this specification) limiting the broadest scope of the claims of this specification. It should be noted that, if there is any inconsistency or conflict between the descriptions, definitions and/or use of terms in the accompanying materials of this specification and the contents of this specification, the descriptions, definitions and/or use of terms in this specification shall prevail .

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations are also possible within the scope of this specification. Accordingly, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to those expressly introduced and described in this specification.

Claims (10)

1. an image processing method, is characterized in that, described method comprises: obtaining raw imaging data, where the raw imaging data is obtained by scanning the target object with medical equipment; determining scan information related to the scan of the target object; Based on the scan information, determining image processing parameters that are variables of position in the direction of the target; and Based on the image processing parameters, the raw imaging data is processed to generate target imaging data. 2. The method of claim 1, the scan information comprising at least one of: information related to the medical device, information related to the raw imaging data, or information related to the target object Information. 3. The method of claim 2, wherein the medical device comprises a positron emission tomography device, The information related to the medical device includes at least one of the sensitivity of the detector of the medical device, the gap between adjacent detectors, and the detection efficiency of the detector, The information related to the raw imaging data includes at least one of coincident event count information and tracer activity, The information related to the target object includes at least one of feature information of the target object and historical imaging data. 4. The method of claim 1, wherein, based on the image processing parameters, processing the raw imaging data comprises multiple rounds of iterations, wherein at least one round of iterations comprises: Get the updated imaging data generated in the previous iteration; processing the updated imaging data based on the image processing parameters to generate processed imaging data; determining whether the iteration satisfies a termination condition; and In response to determining that the iteration satisfies the termination condition, the processed imaging data is determined to be the target imaging data. 5. The method of claim 4, wherein processing the updated imaging data based on the image processing parameters to generate processed imaging data comprises: updating the image processing parameters based on the updated imaging data and the scan information to generate updated image processing parameters; and The updated imaging data is processed based on the updated image processing parameters to generate processed imaging data. 6. The method according to claim 4, wherein the processing comprises image reconstruction processing, and the processing of the updated imaging data based on the image processing parameters to generate the processed imaging data comprises: get the iterative update factor; and The updated imaging data is reconstructed based on the iterative update factor and the image processing parameters to generate a reconstructed image. 7. The method according to claim 6, wherein the acquiring the iterative update factor comprises: performing an orthographic projection operation on the updated imaging data to determine first projection data; determining second projection data based on the raw imaging data; determining third projection data based on the first projection data and the second projection data; performing a back-projection operation on the third projection data to determine back-projection data; and The iterative update factor is determined from the first projection data, the back projection data and the normalization matrix. 8. The method according to claim 1, wherein the processing comprises at least one of the following: image smoothing processing, image enhancement processing, image fusion processing, and image beautification processing. 9. An image processing system, characterized in that the system comprises at least one processor and at least one memory; the at least one memory for storing computer instructions; The at least one processor is adapted to execute at least part of the computer instructions to implement the method of any one of claims 1 to 8. 10. An image processing system, characterized in that the system comprises an acquisition module, a scan information determination module, a processing parameter determination module and a processing module; The acquisition module is used to acquire original imaging data, where the original imaging data is acquired by scanning the target object with medical equipment; the scan information determination module, configured to determine scan information related to the scan of the target object; The processing parameter determination module is configured to determine an image processing parameter based on the scanning information, where the image processing parameter is a variable of the position in the target direction; The processing module is configured to process the original imaging data based on the image processing parameters to generate target imaging data.

Images