CN116138796A - Dynamic positron emission tomography technical parameter imaging method, system and medium - Google Patents
Dynamic positron emission tomography technical parameter imaging method, system and medium Download PDFInfo
- Publication number
- CN116138796A CN116138796A CN202211099136.3A CN202211099136A CN116138796A CN 116138796 A CN116138796 A CN 116138796A CN 202211099136 A CN202211099136 A CN 202211099136A CN 116138796 A CN116138796 A CN 116138796A
- Authority
- CN
- China
- Prior art keywords
- image
- dynamic
- scanning
- registration
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003384 imaging method Methods 0.000 title claims abstract description 66
- 238000002600 positron emission tomography Methods 0.000 title claims abstract description 66
- 238000012905 input function Methods 0.000 claims abstract description 82
- 238000000034 method Methods 0.000 claims abstract description 53
- 238000011158 quantitative evaluation Methods 0.000 claims abstract description 48
- 239000000700 radioactive tracer Substances 0.000 claims description 22
- 238000009432 framing Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 10
- 238000002591 computed tomography Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 5
- 238000012636 positron electron tomography Methods 0.000 description 26
- 239000003814 drug Substances 0.000 description 13
- 229940079593 drug Drugs 0.000 description 13
- 230000008569 process Effects 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 9
- 206010028980 Neoplasm Diseases 0.000 description 7
- 230000000694 effects Effects 0.000 description 7
- 201000011510 cancer Diseases 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 5
- 210000002376 aorta thoracic Anatomy 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 238000003759 clinical diagnosis Methods 0.000 description 3
- 238000001727 in vivo Methods 0.000 description 3
- 230000003902 lesion Effects 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 230000036210 malignancy Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- AOYNUTHNTBLRMT-SLPGGIOYSA-N 2-deoxy-2-fluoro-aldehydo-D-glucose Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](F)C=O AOYNUTHNTBLRMT-SLPGGIOYSA-N 0.000 description 2
- 238000012879 PET imaging Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 230000002757 inflammatory effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 238000012831 peritoneal equilibrium test Methods 0.000 description 2
- 238000012877 positron emission topography Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000014113 dietary fatty acids Nutrition 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 229930195729 fatty acid Natural products 0.000 description 1
- 239000000194 fatty acid Substances 0.000 description 1
- 150000004665 fatty acids Chemical class 0.000 description 1
- 238000007306 functionalization reaction Methods 0.000 description 1
- 239000008103 glucose Substances 0.000 description 1
- 230000004153 glucose metabolism Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000008338 local blood flow Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 230000037323 metabolic rate Effects 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 102000005962 receptors Human genes 0.000 description 1
- 108020003175 receptors Proteins 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/037—Emission tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/48—Diagnostic techniques
- A61B6/481—Diagnostic techniques involving the use of contrast agents
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Radiology & Medical Imaging (AREA)
- Surgery (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Optics & Photonics (AREA)
- Pathology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- High Energy & Nuclear Physics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Nuclear Medicine (AREA)
Abstract
The invention discloses a dynamic positron emission tomography technical parameter imaging method, a system and a medium, which are applied to the technical field of positron emission tomography and can shorten the imaging scanning time of the dynamic positron emission tomography technical parameter and improve the imaging quality. The method comprises the steps of collecting scanning data of a first preset duration to obtain first dynamic data; determining a second preset time interval, and collecting scanning data of a third preset time interval to obtain second dynamic data; performing image reconstruction according to the first dynamic data to obtain a first scanning image; performing image reconstruction according to the second dynamic data to obtain a second scanning image; performing image registration according to the first scanning image and the second scanning image to obtain a registration image; constructing a dynamic image input function according to the registration image; carrying out parameter imaging through a graph method according to a dynamic image input function to obtain a quantitative evaluation index image; denoising the quantitative evaluation index image by using an unsupervised denoising algorithm to obtain a target parameter image.
Description
Technical Field
The present invention relates to the field of positron emission tomography, and in particular, to a method, a system, and a medium for dynamic positron emission tomography.
Background
Positron emission tomography (Positron emission tomography, PET) technology is widely used as a diagnostic tool for important imaging in medical research. The quantitative method in PET clinical diagnosis includes standard uptake value (Standard Uptake Value, SUV) using semi-quantitative technique and affine pharmacokinetic model and related parameter estimation analysis using absolute quantitative technique. The tracer of static PET also aggregates at inflammatory lesions, causing false positives, while at the same time presenting false negatives or in some tumors of low malignancy. In the dynamic PET imaging mode, functional parameters of each tissue and organ can be obtained through the application of a dynamic model, and the probability of diagnosing false positive and false negative can be effectively reduced. However, in the related art, dynamic PET imaging mode mainly performs dynamic parameter estimation by using an image method, and it is required to scan after the distribution of the drug in the body reaches an equilibrium state, so that the dynamic scanning time is required to be longer. It is difficult for the subject to remain stationary for a long period of time, and movement to varying degrees during scanning can result in poor imaging and subsequent diagnostic results.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present invention provides a method, a system and a medium for imaging dynamic positron emission tomography technical parameters, which can effectively shorten the scanning time of imaging the dynamic positron emission tomography technical parameters and effectively improve the imaging quality.
In one aspect, an embodiment of the present invention provides a method for imaging a dynamic positron emission tomography technical parameter, including the steps of:
collecting scanning data of a first preset duration to obtain first dynamic data; wherein the scanning data are data obtained by scanning the injected tracer;
determining a second preset time interval, and collecting the scanning data of a third preset time interval to obtain second dynamic data;
performing image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image;
carrying out dynamic framing reconstruction on the image according to the second dynamic data to obtain a second scanning image; wherein the first scan image and the second scan image are positron emission tomography images;
performing image registration according to the first scanning image and the second scanning image to obtain a registration image;
constructing a dynamic image input function according to the registration image;
carrying out parameter imaging on the registration image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image;
and denoising the quantitative evaluation index image through an unsupervised denoising algorithm to obtain a target parameter image.
The imaging method of the dynamic positron emission tomography technical parameter according to the embodiment of the invention has at least the following beneficial effects: according to the embodiment, first dynamic data and second dynamic data are obtained by means of segmented acquisition of the scanning data, so that the scanning time of the middle part is shortened, namely the scanning time of a second preset time length is shortened, and the scanning time of dynamic positron emission tomography technical parameter imaging is effectively shortened. Then, in this embodiment, image dynamic frame reconstruction is performed on the first dynamic data and the second dynamic data, so as to obtain corresponding positron emission tomography images, i.e., a first scan image and a second scan image. Then, the embodiment performs image registration on the first scanning image and the second scanning image in an image registration manner, so as to alleviate the problem of spatial position deviation of the first scanning image and the second scanning image in the construction process of the dynamic image input function. Further, in the embodiment, the registration image is subjected to parameter imaging in a graphic method according to the constructed dynamic image input function to obtain a quantitative evaluation index image, and then the quantitative evaluation index image is subjected to denoising in an unsupervised denoising algorithm to alleviate the problem of large image noise caused by shortened scanning time, so that the imaging quality is effectively improved.
According to some embodiments of the invention, before performing the step of constructing a dynamic image input function from the registered images, the method further comprises:
acquiring a plurality of third scanning images; the third scanning image is the positron emission tomography image obtained by scanning for a fourth preset time period after the tracer is injected;
and constructing an input function template according to the third scanning images.
According to some embodiments of the invention, the constructing a dynamic image input function from the registered image includes:
constructing a first input function according to the registration image;
and constructing and obtaining the dynamic image input function according to the first input function and the input function template.
According to some embodiments of the invention, the performing image dynamic framing reconstruction according to the first dynamic data to obtain a first scanned image includes:
framing the first dynamic data according to time to obtain a plurality of frames of third dynamic data;
and carrying out image reconstruction according to the third dynamic data of the frames to obtain the first scanning images of the frames.
According to some embodiments of the invention, denoising the quantitative evaluation index image by an unsupervised denoising algorithm to obtain a target parameter image, including:
constructing a depth image prior network;
according to a preset priori image and the quantitative evaluation index image, adjusting network parameters of the depth image priori network to obtain a target priori network;
and inputting the quantitative evaluation index image into the target prior network to obtain the target parameter image.
According to some embodiments of the invention, the preset prior image includes a random noise image, an electron computer tomography image, and a positron emission tomography overlay image; wherein the positron emission tomography superposition image is obtained by superposition of a plurality of positron emission tomography images;
the adjusting the network parameters of the depth image prior network according to the preset prior image and the quantitative evaluation index image comprises the following steps:
and calculating the network parameter according to one of the random noise image, the electronic computer tomography image and the positron emission tomography superposition image and combining the quantitative evaluation index image.
According to some embodiments of the invention, the registration image comprises a first registration scan image and a second registration scan image; the first registration scanning image is an image obtained by performing image registration on the first scanning image, and the second registration scanning image is an image obtained by performing image registration on the second scanning image;
the step of performing parameter imaging on the registration image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image comprises the following steps:
and carrying out parameter imaging on the second registration scanning image through a graphic method according to the dynamic image input function to obtain the quantitative evaluation index image.
In another aspect, an embodiment of the present invention further provides a dynamic positron emission tomography technical parameter imaging system, including:
the first acquisition module is used for acquiring scanning data of a first preset duration to obtain first dynamic data; wherein the scanning data are data obtained by scanning the injected tracer;
the second acquisition module is used for determining a second preset time interval, acquiring the scanning data of a third preset time interval and obtaining second dynamic data;
the first reconstruction module is used for carrying out image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image;
the second reconstruction module is used for carrying out the dynamic frame-division reconstruction of the image according to the second dynamic data to obtain a second scanning image; wherein the first scan image and the second scan image are positron emission tomography images;
the first registration module is used for carrying out image registration according to the first scanning image and the second scanning image to obtain a registration image;
the function construction module is used for constructing a dynamic image input function according to the registration image;
the parameter imaging module is used for carrying out parameter imaging on the registration image through a graphic method according to the dynamic image input function to obtain a quantitative evaluation index image;
and the denoising module is used for denoising the quantitative evaluation index image through an unsupervised denoising algorithm to obtain a target parameter image.
In another aspect, an embodiment of the present invention further provides a dynamic positron emission tomography technical parameter imaging system, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the dynamic positron emission tomography technical parameter imaging method as described in the above embodiment.
In another aspect, embodiments of the present invention further provide a computer storage medium having stored therein a program executable by a processor, where the program executable by the processor is configured to implement the dynamic positron emission tomography technical parameter imaging method as described in the above embodiments.
Drawings
FIG. 1 is a flow chart of a method for imaging dynamic positron emission tomography technical parameters provided by an embodiment of the invention;
fig. 2 is a schematic block diagram of a dynamic positron emission tomography technical parameter imaging system provided by an embodiment of the invention.
Detailed Description
The embodiments described in the present application should not be construed as limitations on the present application, but rather as many other embodiments as possible without inventive faculty to those skilled in the art, are intended to be within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Before describing embodiments of the present application, related terms referred to in the present application will be first described.
Positron emission tomography (Positron Emission Tomography, PET): is an important imaging diagnosis tool in medical research, such as tumor, cardiovascular and cerebrovascular diseases, case study and the like, and can be used for auxiliary diagnosis through PET technology. In clinical imaging, the PET technology marks short-lived radionuclides on materials such as glucose, proteins, nucleic acids, fatty acids and the like which are necessary for life metabolism, and the radionuclides are used as tracers and injected into the body of a person to be detected, so that the person to be detected can be imaged within the effective scanning range of PET.
At present, the main substance used in PET technology is fluorodeoxyglucose @ 18 F-FDG). By utilizing the different metabolic states of different tissues of a human body, glucose metabolism is vigorous and more gathers in high-metabolism malignant tumor tissues. The quantitative method of PET technology in clinical diagnosis includes a radiopharmacy model and associated parameter estimation analysis using standard uptake values (Standard Uptake Value, SUV) of semi-quantitative technology and absolute quantitative technology. SUV values are typically sized to identify malignant and benign lesions, and suggest a malignancy of the tumor, e.g., the greater the SUV value, the greater the malignancy of the tumor. The tracer of the static PET scan also aggregates in inflammatory lesions, resulting in false positives, and in some less malignant tumors, false negatives can occur. In the dynamic PET scanning imaging mode, through the application of a dynamic model, the functional parameters such as local blood flow, substance transfer rate, metabolic rate and receptor binding rate of each tissue and organ can be obtained, so that the problems of false positive diagnosis and false negative diagnosis are effectively relieved. However, in the related art, in the dynamic PET scanning imaging mode, since the drug is required to be scanned after reaching an equilibrium state in vivo, the time required for scanning is long, for example, by 18 When F-FDG is used for PET scanning imaging, the time of dynamic scanning is required to be one hour. For the personnel to be detected, the maintenance of the time of one hour is difficult, and the movement of different degrees easily occurs in the scanning process, so that the imaging effect is poor and the subsequent diagnosis effect is influenced.
Based on the above, an embodiment of the present invention provides a method, a system, and a medium for imaging a dynamic positron emission tomography technical parameter, which can effectively shorten a scanning time of imaging the dynamic positron emission tomography technical parameter, and effectively improve imaging quality. Referring to fig. 1, the method of the embodiment of the present invention includes, but is not limited to, step S110, step S120, step S130, step S140, step S150, step S160, step S170, and step S180.
Specifically, the method application process of the embodiment of the invention includes, but is not limited to, the following steps:
s110: and collecting scanning data of a first preset duration to obtain first dynamic data. The scanning data are obtained by scanning the injected tracer.
S120: determining a second preset time interval, and collecting scanning data of a third preset time interval to obtain second dynamic data.
S130: and carrying out image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image.
S140: and carrying out image dynamic framing reconstruction according to the second dynamic data to obtain a second scanning image. Wherein the first scan image and the second scan image are positron emission tomography images.
S150: and performing image registration according to the first scanning image and the second scanning image to obtain a registration image.
S160: a dynamic image input function is constructed from the registered images.
S170: and carrying out parameter imaging on the registration image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image.
S180: denoising the quantitative evaluation index image by using an unsupervised denoising algorithm to obtain a target parameter image.
In the working process of the embodiment, the embodiment firstly collects the scanning data of the first preset time length, and then performs the second sampling after determining that the second preset time length is spaced, namely collects the scanning data of the third preset time length, so as to obtain the second dynamic data. Specifically, the scan data is data obtained by scanning the tracer in the body of the person to be detected after the tracer is injected. Illustratively, the present embodiment performs t after the person to be tested injects the tracer 1 Minute PET dynamic scans, e.g. acquisition five to tenAnd scanning data in minutes to obtain corresponding first dynamic data. Then suspending scanning to let the person to be detected rest for a period of time. Rest t 2 After the minute, i.e. after determining the interval for a second preset period of time, a second scan is performed. In this embodiment, after determining the interval for the second preset period of time, at t 3 A minute PET dynamic scan, for example, acquires ten to twenty minutes of scan data, resulting in corresponding second dynamic data. Then, in this embodiment, image dynamic framing reconstruction is performed according to the first dynamic data to obtain a first scan image, and image dynamic framing reconstruction is performed according to the second dynamic data to obtain a second scan image. Specifically, in this embodiment, image dynamic frame reconstruction is performed on two scanned portions of scanned data, i.e., the first dynamic data and the second dynamic data, respectively, so as to obtain a corresponding PET image, i.e., a positron emission tomography image. Illustratively, the present embodiment obtains t after performing the segment scan 1 First dynamic data sum of minutes t 3 And second dynamic data of minutes, and performing image dynamic framing reconstruction on the first dynamic data to obtain a PET scanning image in the time period, namely a first scanning image. And simultaneously, carrying out image dynamic framing reconstruction on the second dynamic data so as to obtain a corresponding second scanning image. Further, since the embodiment performs the segmented scanning during the scanning process, the PET image reconstructed according to the first dynamic data and the second dynamic data may have a problem of spatial position deviation in space, which affects the construction of the subsequent input function. In this embodiment, the first scan image and the second scan image obtained by reconstruction are subjected to image registration, so as to alleviate the problem that spatial position deviation exists between the first scan image and the second scan image. Illustratively, the present embodiment employs a non-rigid registration method for image registration of the first and second scanned images, such as Free-deformation (Free-Form Deformations). It should be noted that, in some embodiments of the present invention, other registration methods may also be used to perform image registration on the first scan image and the second scan image.
Further, the present embodiment constructs a moving image input function from the registered images. Specifically, the present embodiment calculates a corresponding input function, i.e., a dynamic image input function, from the registered images. Wherein the value of the dynamic image input function is obtained from pixel values at the descending aorta in the reconstructed PET scan image, which can be used to describe the activity of the tracer in the plasma. Then, the embodiment performs parameter imaging on the registration image through a graph method according to a dynamic image input function to obtain a quantitative evaluation index image. The parametric imaging is illustratively performed by the Patlak method in this embodiment. The embodiment firstly constructs a corresponding parameter imaging function according to a dynamic image input function and a Patlak method. And then, carrying out parameter imaging on the registration image through the parameter imaging function, so as to obtain an image representing the tissue uptake rate constant, namely, quantitatively evaluating the index image. It should be noted that the tissue uptake rate constant indicates the ratio of the solubility of the drug in the tissue to the plasma after the flow reaches equilibrium, and can be used as a quantitative evaluation index. Further, in the process of collecting the scan data, the embodiment adopts the sectional scan, so that the scan data of the first preset duration and the third preset duration are collected, that is, the scan data of the second preset duration of the middle part is deleted. Therefore, in the subsequent imaging process, the introduced noise is larger, clinical diagnosis is easily affected, and the signal-to-noise ratio of the image needs to be improved. In the embodiment, the quantitative evaluation index image is denoised by introducing an unsupervised denoising algorithm to obtain a target parameter image. According to the method, the signal-to-noise ratio of the quantitative evaluation index image is improved through an unsupervised denoising algorithm, and the problem that the noise of the reconstructed image is large due to the reduction of the acquired dynamic data is solved.
In some embodiments of the present invention, prior to performing the step of constructing a dynamic image input function from the registered images, the dynamic positron emission tomography technical parameter imaging method provided by this embodiment further includes, but is not limited to:
a plurality of third scan images are acquired. The third scanning image is a positron emission tomography image obtained by scanning for a fourth preset time period after the tracer is injected.
And constructing an input function template according to the plurality of third scanning images.
In this embodiment, before the dynamic image input function is constructed, a plurality of third scan images are acquired, and an input function template is constructed according to the plurality of third scan images. Specifically, since the second preset time period is set apart in the scanning process, the scanning data of the part of the time period is deleted. In the parametric imaging process, an input function of the whole process of acquiring the dynamic image, namely, the dynamic image input function is required, so that the input function of the missing part is required to be complemented. In this embodiment, first, PET scanning is performed on a plurality of people to be detected who are injected with the tracer according to normal scanning time, so as to obtain a plurality of corresponding third scanning images. Illustratively, when the injected tracer is 18 F-FDG (fluorodeoxyglucose), normal complete PET scan time is one hour. In this embodiment, a plurality of positron emission tomography images, that is, a plurality of third scan images, are obtained by first obtaining a plurality of complete scans for one hour, that is, a fourth preset duration. Then, the embodiment constructs an input function template according to the obtained third scanning images. In this embodiment, a plurality of third scan images are used as prior approximate image data, and corresponding input functions are calculated for the third scan images, so that an input function template is finally constructed. The input function template is a more general input function constructed by a plurality of completely scanned positron emission tomography images, and the more general input function template is fitted in a big data mode, so that the problem of input function deletion of middle part scanning data caused by middle part scanning data deletion due to shortened scanning time is solved.
In some embodiments of the present invention, a dynamic image input function is constructed from the registered images, including but not limited to:
a first input function is constructed from the registered images.
And constructing and obtaining a dynamic image input function according to the first input function and the input function template.
In this embodiment, the first input function is first constructed according to the registered image, and then the dynamic image input function is constructed through the first input function and the input function template. Specifically, the present embodiment first calculates its corresponding input function, i.e., the first input function, from the registered first scan image and second scan image. The present embodiment obtains the value of the input function from the pixel values at the descending aorta in the first scan image and the second scan image after image registration, respectively, to describe the activity of the tracer in the plasma. Next, in this embodiment, a dynamic image input function is constructed according to the first input function and an input function template constructed by a plurality of third scan images, and the specific formula is shown in the following formula (1):
wherein C in the formula 1 (t) is an input function constructed based on the first scanned image, C 2 And (t) is an input function constructed based on the second scanned image. C (C) p0 (t) is an input function constructed based on a plurality of third scan images, and λ and γ are proportionality constants which function to make
And +.>
t 1 And t 2 Representing a time interval.
It should be noted that, in some embodiments of the present invention, the input function template may also be downloaded through some preset websites. For example, a large number of human dynamic PET scan experiments were conducted in some authoritative institutes to construct well-fitting input function templates. In some embodiments, the corresponding input function templates may also be obtained directly from some more reliable data sources, so as to construct and obtain the dynamic image input function.
In some embodiments of the present invention, image dynamic framing reconstruction is performed according to the first dynamic data to obtain a first scanned image, including but not limited to:
and framing the first dynamic data according to time to obtain a plurality of frames of third dynamic data.
And carrying out image reconstruction according to the third dynamic data of the frames to obtain the first scanning images of the frames.
In this embodiment, first, the first dynamic data is framed according to time to obtain a plurality of frames of third dynamic data, so that image reconstruction is performed on the plurality of frames of third dynamic data to obtain a plurality of frames of corresponding first scan images. Specifically, since the concentration of the drug in the descending aorta increases sharply after the tracer is injected, the concentration gradually decreases, and finally the concentration becomes stable. The first preset duration scan data acquisition immediately after the tracer injection is performed, and because the concentration of the drug in the descending aorta changes rapidly, the first dynamic data is finely divided according to time in the embodiment. For example, when the first preset time period is five minutes, the first minute frames the first dynamic data for 30×2s, i.e., every two seconds for a total of 30 frames, the second minute frames the first dynamic data for 12×5s, the third minute frames the first dynamic data for 4×30s, and the fourth minute frames the first dynamic data for 1×60s, thereby dividing the first dynamic data within the first preset time period for 47 frames. Then, the first dynamic data of the 47 frames are respectively subjected to image reconstruction, so as to obtain a corresponding PET image of the 47 frames, namely a first scanning image. Accordingly, since the concentration of the drug in the body tends to be stable when the scanning is performed for the third preset period, the time-framing and time-framing is wider for the second dynamic scanning data. For example, when the third preset duration is fifteen minutes, the second dynamic data is framed into 3×60s and 6×120s, that is, three frames are divided every 60 seconds for the first three minutes of the third preset duration, then 6 frames are divided every 120 seconds for the last twelve minutes of the third preset duration, and 9 frames are divided in total for the third preset duration. And then respectively carrying out image reconstruction on the 9 frames of data to obtain 9 frames of corresponding second scanning images.
In some embodiments of the present invention, the quantitative assessment index image is denoised by an unsupervised denoising algorithm to obtain a target parameter image, including but not limited to:
and constructing a depth image prior network.
And adjusting network parameters of the depth image prior network according to the preset prior image and the quantitative evaluation index image to obtain the target prior network.
And inputting the quantitative evaluation index image into a target priori network to obtain a target parameter image.
In this particular embodiment, the unsupervised denoising algorithm comprises a depth image prior algorithm. According to the embodiment, a depth image priori network is firstly constructed, network parameters of the depth image priori network are adjusted according to a preset priori image and a quantitative evaluation index image, so that a target priori network is obtained, and the quantitative evaluation index image is input into the target priori network, and a target parameter image is obtained. Specifically, the depth image prior network comprises a convolution layer, a downsampling layer, an upsampling layer, a normalization layer and an activation function layer. The depth image prior network carries out operations such as convolution, downsampling, normalization and activation function on the image to obtain different characteristic network layers. And then up-sampling, convolution, activation function and normalization are carried out on the obtained characteristic network layer so as to restore the characteristic layer. Meanwhile, in order to alleviate the gradient loss problem caused by the too deep network, a connection layer is established between different feature layers in the embodiment. Further, the embodiment inputs the preset priori image and the quantitative evaluation index image into the constructed depth image priori network, calculates network parameters of the depth image priori network, and thus obtains the target priori network. For example, a preset priori image and a quantitative evaluation image are input into a depth image priori network to calculate corresponding loss values, so that network parameters of the depth image priori network are adjusted according to the calculated loss values to obtain a target priori network. Then, the embodiment inputs the quantitative evaluation index image into the target prior network for denoising, and a target parameter image is obtained.
In some embodiments of the invention, the preset prior image includes a random noise image, an electron computer tomography image, and a positron emission tomography overlay image. The positron emission tomography superposition image is obtained by superposing a plurality of positron emission tomography images. Accordingly, the network parameters of the depth image prior network are adjusted according to the preset prior image and the quantitative evaluation index image, including but not limited to:
network parameters are calculated from one of the random noise image, the computerized tomography image, and the positron emission tomography overlay image in combination with the quantitative assessment index image.
In this particular embodiment, the preset prior image includes a random noise image, an electron computer tomography image, and a positron emission tomography overlay image. Specifically, the positron emission tomography superposition image is obtained by superposing a plurality of positron emission tomography images. For example, a frame of dynamic PET image, i.e., a positron emission tomography superimposed image, is synthesized by accumulating several dynamic PET images. Then, in this embodiment, one of a random noise image, an electronic computed tomography image (CT image), and a positron emission tomography superimposed image is used as a network input of the depth image prior network, and network parameters of the depth image prior network are calculated in combination with the quantitative evaluation index image. In this embodiment, one of the random noise image, the electronic computed tomography image and the positron emission tomography superimposed image is introduced as prior information, so as to improve the denoising effect of the depth image prior network. The electronic computer tomography image and the positron emission tomography superimposed image are introduced to provide prior information of structure and function distribution of tissues, so that good structure, edge and texture data can be kept in the denoising process, and the denoising effect is effectively improved.
In some embodiments of the invention, the registered images include a first registered scan image and a second registered scan image. The first registration scanning image is an image obtained by performing image registration on the first scanning image, and the second registration scanning image is an image obtained by performing image registration on the second scanning image. Accordingly, the registered image is imaged by a graph method according to a dynamic image input function to obtain quantitative evaluation index images, including but not limited to:
and carrying out parameter imaging on the second registration scanning image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image.
In this specific embodiment, the registration image obtained by performing image registration according to the first scan image and the second scan image includes the first registration scan image and the second registration scan image. Specifically, the first registration scanning image is an image of the first scanning image after image registration adjustment, and the second registration scanning image is an image of the second scanning image after image registration adjustment. According to the embodiment, parameter imaging is carried out on the second registration scanning image through a graphic method according to a dynamic data input function, and a quantitative evaluation index image is obtained. The parameter imaging is performed by the Patlak method in this embodiment, and the specific formula is shown in the following formula (2):
wherein C in the formula T (t) is the activity value of the target tissue, C p (t) is an arterial input function, i.e. a dynamic image input function, representing the concentration of tracer in arterial blood, V E Represents the blood volume fraction, t represents the total time of dynamic acquisition, t * Indicating the time point when the in vivo distribution of the drug reaches dynamic balance, wherein the initial time point of the second scan is after the time point, i.e. the second scan can be performed after the in vivo distribution of the drug reaches dynamic balance, K i The ratio of the drug concentration in the tissue to the drug concentration in the plasma after the drug flow in the body reaches the balance is expressed as a tissue uptake rate constant, and is used as a quantitative evaluation index in the embodiment. Illustratively, it will 18 F-FDG (fluorodeoxyglucose) as tracer, then a normal complete PET scan time of one hour will, when the third preset duration is 15 minutes, i.e. the second scan acquisition time is 15 minutes, the second scan time starts from the 45 th minute. The present embodiment uses a second scanned imageLine parametric imaging, i.e. Patlak parametric imaging using 15 minutes of motion after 45 minutes, gives a representation K i The image of the data, i.e. the quantitative evaluation index image, can reflect the ratio of the drug concentration in the tissue to the drug concentration in the plasma through the image.
One embodiment of the present invention also provides a dynamic positron emission tomography parametric imaging system, comprising:
the first acquisition module is used for acquiring the scanning data of a first preset duration to obtain first dynamic data. The scanning data are obtained by scanning the injected tracer.
The second acquisition module is used for acquiring the scanning data of the third preset duration after determining the interval of the second preset duration to obtain second dynamic data.
And the first reconstruction module is used for carrying out image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image.
And the second modeling block is used for carrying out image dynamic framing reconstruction according to the second dynamic data to obtain a second scanning image. Wherein the first scan image and the second scan image are positron emission tomography images.
And the first registration module is used for carrying out image registration according to the first scanning image and the second scanning image to obtain a registration image.
And the function construction module is used for constructing a dynamic image input function according to the registration image.
And the parameter imaging module is used for carrying out parameter imaging on the registration image through a graphic method according to the dynamic image input function to obtain a quantitative evaluation index image.
And the denoising module is used for denoising the quantitative evaluation index image through an unsupervised denoising algorithm to obtain a target parameter image.
Referring to fig. 2, one embodiment of the present invention further provides a dynamic positron emission tomography parametric imaging system, comprising:
at least one processor 210.
At least one memory 220 for storing at least one program.
The at least one program, when executed by the at least one processor 210, causes the at least one processor to implement the dynamic positron emission tomography technique parametric imaging method as described in the embodiments above.
An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions for execution by one or more control processors, e.g., to perform the steps described in the above embodiments.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. A dynamic positron emission tomography technical parameter imaging method, which is characterized by comprising the following steps:
collecting scanning data of a first preset duration to obtain first dynamic data; wherein the scanning data are data obtained by scanning the injected tracer;
determining a second preset time interval, and collecting the scanning data of a third preset time interval to obtain second dynamic data;
performing image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image;
carrying out dynamic framing reconstruction on the image according to the second dynamic data to obtain a second scanning image; wherein the first scan image and the second scan image are positron emission tomography images;
performing image registration according to the first scanning image and the second scanning image to obtain a registration image;
constructing a dynamic image input function according to the registration image;
carrying out parameter imaging on the registration image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image;
and denoising the quantitative evaluation index image through an unsupervised denoising algorithm to obtain a target parameter image.
2. The method of claim 1, further comprising, prior to performing the step of constructing a dynamic image input function from the registered images:
acquiring a plurality of third scanning images; the third scanning image is the positron emission tomography image obtained by scanning for a fourth preset time period after the tracer is injected;
and constructing an input function template according to the third scanning images.
3. The method of claim 2, wherein said constructing a dynamic image input function from said registered images comprises:
constructing a first input function according to the registration image;
and constructing and obtaining the dynamic image input function according to the first input function and the input function template.
4. The method of claim 1, wherein the performing image dynamic frame reconstruction according to the first dynamic data to obtain a first scan image comprises:
framing the first dynamic data according to time to obtain a plurality of frames of third dynamic data;
and carrying out image reconstruction according to the third dynamic data of the frames to obtain the first scanning images of the frames.
5. The method of claim 1, wherein denoising the quantitative evaluation index image by an unsupervised denoising algorithm to obtain a target parameter image comprises:
constructing a depth image prior network;
according to a preset priori image and the quantitative evaluation index image, adjusting network parameters of the depth image priori network to obtain a target priori network;
and inputting the quantitative evaluation index image into the target prior network to obtain the target parameter image.
6. The method of claim 5, wherein the predetermined prior image comprises a random noise image, an electron computed tomography image, and a positron emission tomography overlay image; wherein the positron emission tomography superposition image is obtained by superposition of a plurality of positron emission tomography images;
the adjusting the network parameters of the depth image prior network according to the preset prior image and the quantitative evaluation index image comprises the following steps:
and calculating the network parameter according to one of the random noise image, the electronic computer tomography image and the positron emission tomography superposition image and combining the quantitative evaluation index image.
7. The dynamic positron emission tomography technical parameter imaging method of claim 1, wherein the registered image includes a first registered scan image and a second registered scan image; the first registration scanning image is an image obtained by performing image registration on the first scanning image, and the second registration scanning image is an image obtained by performing image registration on the second scanning image;
the step of performing parameter imaging on the registration image through a graph method according to the dynamic image input function to obtain a quantitative evaluation index image comprises the following steps:
and carrying out parameter imaging on the second registration scanning image through a graphic method according to the dynamic image input function to obtain the quantitative evaluation index image.
8. A dynamic positron emission tomography parametric imaging system, comprising:
the first acquisition module is used for acquiring scanning data of a first preset duration to obtain first dynamic data; wherein the scanning data are data obtained by scanning the injected tracer;
the second acquisition module is used for determining a second preset time interval, acquiring the scanning data of a third preset time interval and obtaining second dynamic data;
the first reconstruction module is used for carrying out image dynamic framing reconstruction according to the first dynamic data to obtain a first scanning image;
the second reconstruction module is used for carrying out the dynamic frame-division reconstruction of the image according to the second dynamic data to obtain a second scanning image; wherein the first scan image and the second scan image are positron emission tomography images;
the first registration module is used for carrying out image registration according to the first scanning image and the second scanning image to obtain a registration image;
the function construction module is used for constructing a dynamic image input function according to the registration image;
the parameter imaging module is used for carrying out parameter imaging on the registration image through a graphic method according to the dynamic image input function to obtain a quantitative evaluation index image;
and the denoising module is used for denoising the quantitative evaluation index image through an unsupervised denoising algorithm to obtain a target parameter image.
9. A dynamic positron emission tomography parametric imaging system, comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the dynamic positron emission tomography technical parameter imaging method as claimed in any one of claims 1 to 7.
10. A computer storage medium having stored therein a processor executable program which when executed by the processor is for implementing the dynamic positron emission tomography technical parameter imaging method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211099136.3A CN116138796A (en) | 2022-09-07 | 2022-09-07 | Dynamic positron emission tomography technical parameter imaging method, system and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211099136.3A CN116138796A (en) | 2022-09-07 | 2022-09-07 | Dynamic positron emission tomography technical parameter imaging method, system and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116138796A true CN116138796A (en) | 2023-05-23 |
Family
ID=86351304
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211099136.3A Pending CN116138796A (en) | 2022-09-07 | 2022-09-07 | Dynamic positron emission tomography technical parameter imaging method, system and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116138796A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150363948A1 (en) * | 2014-06-16 | 2015-12-17 | University Of Southern California | Direct Patlak Estimation from List-Mode PET Data |
| CN108932741A (en) * | 2018-06-14 | 2018-12-04 | 上海联影医疗科技有限公司 | Dynamic PET parameter imaging method, device, system and computer readable storage medium |
| CN111667424A (en) * | 2020-05-28 | 2020-09-15 | 武汉大学 | Unsupervised real image denoising method |
| CN113989231A (en) * | 2021-10-28 | 2022-01-28 | 上海联影医疗科技股份有限公司 | Method and device for determining kinetic parameters, computer equipment and storage medium |
-
2022
- 2022-09-07 CN CN202211099136.3A patent/CN116138796A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150363948A1 (en) * | 2014-06-16 | 2015-12-17 | University Of Southern California | Direct Patlak Estimation from List-Mode PET Data |
| CN108932741A (en) * | 2018-06-14 | 2018-12-04 | 上海联影医疗科技有限公司 | Dynamic PET parameter imaging method, device, system and computer readable storage medium |
| CN111667424A (en) * | 2020-05-28 | 2020-09-15 | 武汉大学 | Unsupervised real image denoising method |
| CN113989231A (en) * | 2021-10-28 | 2022-01-28 | 上海联影医疗科技股份有限公司 | Method and device for determining kinetic parameters, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 崔佳楠: "基于无监督深度学习的PET静态图像及参数图像去噪研究", 《中国博士学位论文全文数据库（电子期刊） 医药卫生科技辑》, pages 060 - 13 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109741284B (en) | System and method for correcting respiratory motion-induced mismatches in PET imaging | |
| CN107203741B (en) | Blood vessel extraction method, device and system | |
| CN109308728B (en) | Positron emission computed tomography image processing method and device | |
| CN110751702B (en) | Image reconstruction method, system, device and storage medium | |
| CN109872312B (en) | Medical image segmentation method, device and system and image segmentation method | |
| US9317915B2 (en) | Computed-tomography system and method for determining volume information for a body | |
| CN111462020A (en) | Method, system, storage medium and device for correcting motion artifact of heart image | |
| CN110136076B (en) | Medical scanning imaging method, device, storage medium and computer equipment | |
| Sundar et al. | Conditional generative adversarial networks aided motion correction of dynamic 18F-FDG PET brain studies | |
| US10657621B2 (en) | Moving structure motion compensation in imaging | |
| CN111260703B (en) | Method, system, medium and storage medium for obtaining spinal straightening image set | |
| TW201219013A (en) | Method for generating bone mask | |
| Hu et al. | Design and implementation of automated clinical whole body parametric PET with continuous bed motion | |
| Shi et al. | Automatic inter-frame patient motion correction for dynamic cardiac PET using deep learning | |
| WO2008062366A2 (en) | Image generation based on limited data set | |
| CN110415310A (en) | Medical scanning imaging method, device, storage medium and computer equipment | |
| van Velzen et al. | AI-based quantification of planned radiation therapy dose to cardiac structures and coronary arteries in patients with breast cancer | |
| CN113989231A (en) | Method and device for determining kinetic parameters, computer equipment and storage medium | |
| CN116630463B (en) | Enhanced CT image generation method and system based on multitask learning | |
| CN110853742B (en) | Image reconstruction method, system, device and storage medium | |
| US20200359985A1 (en) | Method of data processing for computed tomography | |
| Perez-Liva et al. | Ultrafast ultrasound imaging for super-resolution preclinical cardiac PET | |
| JP2009050726A (en) | Method and apparatus for calculating index for local blood flow kinetics | |
| CN116138796A (en) | Dynamic positron emission tomography technical parameter imaging method, system and medium | |
| CN114359431A (en) | Method and system for directly reconstructing parameter image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230523 |