EP-4739216-A1 - MEDICAL IMAGE PROCESSING METHODS AND SYSTEMS

EP4739216A1EP 4739216 A1EP4739216 A1EP 4739216A1EP-4739216-A1

Abstract

Medical image processing methods and systems are provided. The methond is implemented on a PET-CT system including a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device. The method may comprise obtaining PET data of a target object within a field of view (FOV) of the PET imaging device. The method may comprise obtaining CT data of the target object within a FOV of the CT imaging device. The method may comprise generating an attenuation image based on a target registration parameter and the CT data, wherein the target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target registration parameter is obtained based on background coincidence event data from the PET imaging device. The method may further comprise reconstructing a target PET image based on the PET data and the attenuation image.

Inventors

TANG, Songsong
LIU, YILIN
HE, Liuchun
DONG, Yun
WU, YIFAN

Assignees

Shanghai United Imaging Healthcare Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20240809

Claims (20)

A method, performed by a computing device including at least one processor and at least one storage device, wherein the method is implemented on a PET-CT system, the PET-CT system includes a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device, and the method comprises: obtaining PET data of a target object within a field of view (FOV) of the PET imaging device; obtaining CT data of the target object within a FOV of the CT imaging device; generating an attenuation image based on a target registration parameter and the CT data, wherein the target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target registration parameter is obtained based on background coincidence event data from the PET imaging device; and reconstructing a target PET image based on the PET data and the attenuation image.
The method of claim 1, wherein the obtaining PET data comprises: obtaining corrected PET data by correcting the PET data based on a target normalization correction factor, wherein the target normalization correction factor is a normalization correction factor corresponding to a current state of the PET imaging device, and the target normalization correction factor is determined by: acquiring first background coincidence event data of a detector crystal of the PET imaging device at a predetermined time point; and determining the target normalization correction factor based on the first background coincidence event data.
The method of claim 2, wherein the determining the target normalization correction factor based on the first background coincidence event data comprises: obtaining a first background energy response based on the first background coincidence event data; and determining the target normalization correction factor based on the first background energy response.
The method of claim 3, wherein the determining the target normalization correction factor based on the first background energy response comprises: generating a variation of the first background energy response based on the first background energy response and a first reference background energy response, wherein the first reference background energy response is obtained under a first reference system state of the PET imaging device; in response to determining that the variation of the first background energy response is not greater than a variation threshold, designating a first reference normalization correction factor as the target normalization correction factor, wherein the first reference normalization correction factor is obtained based on a phantom under the first reference system state of the PET imaging device; or in response to determining that the variation of the first background energy response is greater than the variation threshold, generating a variation of a first normalization correction factor based on the variation of the first background energy response and a predetermined relationship; and determining the target normalization correction factor based on the variation of the first normalization correction factor and the first reference normalization correction factor.
The method of claim 4, wherein the predetermined relationship includes a relationship between the variation of the background energy response and the variation of the normalization correction factor, and the relationship is obtained by: collecting second reference background coincidence event data and reference phantom coincidence event data in a second reference system state of the PET imaging device; obtaining a second reference background energy response based on the second reference background coincidence event data, obtaining a second reference normalization correction factor based on the reference phantom coincidence event data; obtaining a plurality groups of second background coincidence event data and a plurality groups of second phantom coincidence event data collected under a plurality of second system states of the PET imaging device, wherein each of the plurality of second system states is different from the second reference system state; and obtaining the relationship based on the plurality groups of second background coincidence event data and the plurality groups of second phantom coincidence event data.
The method of claim 5, wherein the obtaining the relationship based on the plurality groups of second background coincidence event data and the plurality groups of second phantom coincidence event data comprises: obtaining a plurality of second background energy responses based on the plurality groups of second background coincidence event data, obtaining a plurality of second normalization correction factors based on the plurality groups of second phantom coincidence event data; and obtaining the relationship based on the plurality of second background energy responses and the plurality of second normalization correction factors.
The method of claim 6, wherein the obtaining the relationship based on the plurality of second background energy responses and the plurality of second normalization correction factors comprises: obtaining a plurality of variations of second background energy response based on the second reference background energy response and the plurality of second background energy responses; obtaining a plurality of variations of second normalization correction factor based on the second reference normalization correction factor and the plurality of second normalization correction factors; and obtaining the relationship based on the plurality of variations of second background energy response and the plurality of variations of second normalization correction factor.
The method of any of claims 2-7, wherein the normalization correction factor includes at least one of an axial profile correction factor, a detector ring pair detection efficiency correction factor, an annular profile correction factor, a crystal detection efficiency correction factor, or a time-of-flight correction factor.
The method of any of claims 3-7, obtaining a background energy response based on the background coincidence event data includes: each of a plurality of background coincidence events corresponding to the background coincidence event data including a first background single event and a second background single event, the first background single event corresponding to first energy and a first crystal, the second background single event corresponding to second energy and a second crystal, and the first background single event and the second background single event being not generated on a same crystal; for the background coincidence event, determining whether a time of the second background single event precedes a time of the first background single event; in response to determining that the time of the second background single event precedes the time of the first background single event, in an energy response corresponding to the first crystal, incrementing a first energy bin corresponding to the first energy, wherein the first energy bin is an energy range of the first energy; obtaining a background energy response of the first crystal based on energy information, in the first energy bin, corresponding to a plurality of first background single events of the plurality of background coincidence events; obtaining a background energy response of the second crystal based on energy information, in the second energy bin, corresponding to a plurality of second background single events of the plurality of background coincidence events; and determining the background energy response corresponding to the background coincidence event data based on the background energy response of the first crystal and the background energy response of the second crystal.
The method of claim 9, further comprising: obtaining the plurality of background coincidence events based on arrival energies and arrival times of βrays and γ rays in the background coincidence event data.
The method of any of claims 1-10, wherein the target registration parameter is obtained by: obtaining a CT image of a phantom by placing the phantom in the FOV of the CT imaging device, the phantom not containing a radiation source; collecting background coincidence event data of the phantom by placing the phantom in the FOV of the PET imaging device; acquiring a first attenuation image in a coordinate system of the CT imaging device based on the CT image; and determining the target registration parameter based on target background coincidence event data, and the first attenuation image, wherein the target background coincidence event data is obtained based on the background coincidence event data of the phantom.
The method of claim 11, wherein the target background coincidence event data is obtained by: obtaining background coincidence event data after noise reduction by performing noise reduction on the background coincidence event data of the phantom, and designating the background coincidence event data after noise reduction as the target background coincidence event data.
The method of claim 12, wherein the performing noise reduction on the background coincidence event data includes: merging at least two target response lines in original response lines in the background coincidence event data of the phantom into one combined response line, and obtaining the background coincidence event data after noise reduction based on background coincidence event data received by at least one combined response line.
The method of claim 12, wherein the performing noise reduction on the background coincidence event data includes: obtaining the background coincidence event data after noise reduction by smoothing or using AI noise reduction algorithm for processing background coincidence event data received by original response lines in the background coincidence event data of the phantom.
The method of any of claims 11-14, wherein the determining the target registration parameter based on target background coincidence event data, and the first attenuation image includes: iteratively updating, based on the target background coincidence event data, the first attenuation image, and a predetermined registration parameter, the predetermined registration parameter using a maximum likelihood estimation (MLE) function until a preset end condition is satisfied; and designating the predetermined registration parameter corresponding to a maximum value of the maximum likelihood estimation function as the target registration parameter.
A system, comprising: at least one storage device including a set of instructions; and at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor causes the system to perform operations on a PET-CT system, the PET-CT system includes a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device, the operations include: obtaining PET data of a target object within a field of view (FOV) of the PET imaging device; obtaining CT data of the target object within a FOV of the CT imaging device; generating an attenuation image based on a target registration parameter and the CT data, wherein the target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target registration parameter is obtained based on background coincidence event data from the PET imaging device; and reconstructing a target PET image based on the PET data and the attenuation image.
A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, wherein the method is implemented on a PET-CT system, the PET-CT system includes a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device, and the method comprises: obtaining PET data of a target object within a field of view (FOV) of the PET imaging device; obtaining CT data of the target object within a FOV of the CT imaging device; generating an attenuation image based on a target registration parameter and the CT data, wherein the target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target registration parameter is obtained based on background coincidence event data from the PET imaging device; and reconstructing a target PET image based on the PET data and the attenuation image.
A method, performed by a computing device including at least one processor and at least one storage device, wherein the method comprises: obtaining PET data of a target object within a field of view (FOV) of a positron emission computed tomography (PET) imaging device; obtaining corrected PET data by correcting the PET data based on a target normalization correction factor, wherein the target normalization correction factor is a normalization correction factor corresponding to a current state of the PET imaging device, and the target normalization correction factor is determined by: acquiring first background coincidence event data of a detector crystal of the PET imaging device at a predetermined time point; determining the target normalization correction factor based on the first background coincidence event data; and reconstructing a target PET image based on the corrected PET data.
The method of claim 18, wherein the determining the target normalization correction factor based on the first background coincidence event data comprises: obtaining a first background energy response based on the first background coincidence event data; and determining the target normalization correction factor based on the first background energy response.
The method of claim 18, wherein the determining the target normalization correction factor based on the first background energy response comprises: generating a variation of the first background energy response based on the first background energy response and a first reference background energy response, wherein the first reference background energy response is obtained under a first reference system state of the PET imaging device; in response to determining that the variation of the first background energy response is not greater than a variation threshold, designating a first reference normalization correction factor as the target normalization correction factor, wherein the first reference normalization correction factor is obtained based on a phantom under the first reference system state of the PET imaging device; or in response to determining that the variation of the first background energy response is greater than the variation threshold, generating a variation of a first normalization correction factor based on the variation of the first background energy response and a predetermined relationship; and determining the target normalization correction factor based on the variation of the first normalization correction factor and the first reference normalization correction factor.

Description

MEDICAL IMAGE PROCESSING METHODS AND SYSTEMS CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202311011137.2, filed on August 10, 2023, and Chinese Patent Application No. 202311377624.0, filed October 23, 2023, the entire contents of each of which are hereby incorporated by reference. TECHNICAL FIELD The present disclosure relates to the field of medical technology, and in particular, to methods and systems for processing medical images. BACKGROUND Positron Emission Computed Tomography-Computer Tomography (PET-CT) imaging can obtain PET images with functional information and CT images with fine anatomical structure information. By fusing PET images and CT images, it is possible to pinpoint the location of lesions and improve the accuracy of disease diagnosis. In PET-CT imaging, PET data needs to be corrected to obtain quantitatively accurate and artifact-free PET reconstructed images. The correction includes attenuation correction, normalization correction, etc. Existing correction manners have various issues, such as being complicated to operate, requiring high standards for personnel and equipment, posing health risks to operators, affecting imaging quality, etc. For example, in the attenuation correction, a CT image is typically converted and registered to obtain an attenuation image. The registration process requires an operator (e.g., a service engineer, a technician, a physician, etc. ) to create a phantom (e.g., a water phantom, etc. ) containing a radiation source or use a solid registration source. The phantom is scanned using both CT and PET imaging devices and the corresponding images are reconstructed. Subsequently, the CT reconstructed image and the PET reconstructed image are registered to obtain a mapping relationship between a CT image reconstructed coordinate system and a PET image reconstructed coordinate system. The process of creating a phantom containing a radiation source is time-consuming, labor-intensive, inefficient, and costly. Moreover, the radiation source poses health risks to the operators. Additionally, the registration process demands high standards for both the operators and the PET imaging device. For example, the registration process requires experienced operators. As another example, during the normalization correction, due to changes in the system state, normalization correction factors need to be updated regularly. If the update cycle is too long, the mismatch between the normalization correction factors and the system state may gradually increase, making it challenging to ensure the effectiveness of the normalization correction and leading to a decline in the quality of the scanned image. Therefore, it is desired to provide a method and a medical image processing system to improve the efficiency and quality of PET data correction. SUMMARY An aspect of the present disclosure relates to a method for medical image processing. The method may be performed by a computing device including at least one processor and at least one storage device, wherein the method is implemented on a PET-CT system, the PET-CT system includes a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device. The method may include obtaining PET data of a target object within a field of view (FOV) of the PET imaging device. The method may include obtaining CT data of the target object within a FOV of the CT imaging device. The method may include generating an attenuation image based on a target registration parameter and the CT data. The target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target registration parameter is obtained based on background coincidence event data from the PET imaging device. The method may further include reconstructing a target PET image based on the PET data and the attenuation image. Another aspect of the present disclosure relates to a system. The system may include at least one storage device including a set of instructions and at least one processor in communication with the at least one storage device. When executing the set of instructions, the at least one processor may be directed to cause the system to implement operations on a PET-CT system, the PET-CT system includes a computed tomography (CT) imaging device and a positron emission computed tomography (PET) imaging device. The operations may include obtaining PET data of a target object within a field of view (FOV) of the PET imaging device. The operations may include obtaining CT data of the target object within a FOV of the CT imaging device. The operations may include generating an attenuation image based on a target registration parameter and the CT data. The target registration parameter characterizes a mapping relationship registering the CT coordinate system and the PET coordinate system, and the target r