CN114936989A - Glucose metabolic rate quantitative analysis method and system based on medical image - Google Patents

Glucose metabolic rate quantitative analysis method and system based on medical image Download PDF

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CN114936989A
CN114936989A CN202210860942.1A CN202210860942A CN114936989A CN 114936989 A CN114936989 A CN 114936989A CN 202210860942 A CN202210860942 A CN 202210860942A CN 114936989 A CN114936989 A CN 114936989A
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medical image
patient
image
quantitative analysis
medical
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崔碧霄
王敏
闫少珍
单艺
卢洁
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Xuanwu Hospital
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Xuanwu Hospital
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computerised tomographs
    • A61B6/037—Emission tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • A61B6/5264—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise due to motion
    • G06T5/80—
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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]
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain

Abstract

The invention provides a quantitative analysis method and system for glucose metabolic rate based on medical images. Since the patient usually has progressive stenosis or obstruction in the unilateral internal carotid artery, the method carries out image input function processing on the unilateral internal carotid artery rock-tip region of the patient based on the PET/MRI image of the patient, and finally gives an absolute value of the glucose proxy rate of the patient. The method provided by the invention effectively reduces the numerical error of quantitative analysis, improves the stability and robustness of quantitative analysis, avoids the defect that the reference brain area of a patient is unstable in the traditional quantitative analysis method, more accurately evaluates the cerebral metabolic activity and reduces the cerebral stroke risk of the patient compared with the existing semi-quantitative method, is non-invasive to the patient, assists a clinician to know the cerebral metabolic activity of the patient more, helps the selection of a clinical treatment scheme, and has important social significance and clinical value.

Description

Glucose metabolic rate quantitative analysis method and system based on medical image
Technical Field
The invention relates to the technical field of medical image processing, in particular to a glucose metabolic rate quantitative analysis method and system based on medical images.
Background
Stroke, heart disease, and malignancies collectively constitute the three most common cases of death in most countries. In particular, ischemic stroke accounts for 60% -80% of all strokes, which is a general term for brain tissue necrosis caused by stenosis or occlusion of blood supply arteries to the brain and insufficient blood supply to the brain. The blood supply arteries of the brain include the Internal Carotid Artery (ICA), the vertebral artery, and the middle cerebral artery. The risk of stroke in patients with ICA progressive stenosis in the internal carotid artery without conventional treatment is up to 13.2% and 29.2% in the first and second years, respectively. Therefore, it is very necessary to develop a method for preventing stroke in advance, especially ischemic stroke.
The Imaging examination (such as PET, Positron emission tomography, MRI, Magnetic Resonance Imaging, etc.) can provide various Imaging parameters for cerebrovascular diseases, and is an important examination means for cerebrovascular disease research.
Among them, 18F-fluorodeoxyglucose (18F-fluoro-2-deoxy-Dglucose, 18F-FDG) Positron Emission Tomography (PET) imaging is considered as a gold standard for measuring glucose metabolism in various regions of the brain. The PET scanning is carried out on the chronic ischemic cerebrovascular patient, and the accurate quantification of the cerebral metabolic activity of the patient is the premise of accurate treatment and is also an important means for screening the high-risk cerebral infarction patient.
In order to ensure that a patient is in the same physiological state during the imaging examination, integrated PET/MRI scanning is often performed, namely PET information and MRI information of the patient are acquired simultaneously, synchronously and under the same physiological state, and a PET metabolic image and an MRI functional image with high resolution and tissue contrast are formed.
The 18F-FDG glucose metabolic rate, i.e. the Standard Uptake Value Ratio (SUVR), is obtained clinically, and the most common method is semi-quantitative analysis: the relative uptake of the targeted region was measured using the non-specifically bound region as a reference brain region. However, in the semi-quantitative analysis method, since the ischemic region of cerebrovascular disease may overlap with the reference brain region in the semi-quantitative analysis method, the result of the glucose metabolic rate may be affected.
In order to accurately obtain the absolute quantitative value of the 18F-FDG glucose metabolic rate, arterial blood sampling of a patient is the gold standard for measurement, but the arterial blood sampling has the defects of great wound and pain on the patient, invasiveness, complexity, multiple acquisition and the like, so that an accurate and effective absolute quantitative analysis method according to medical image information scanned by PET/MRI of the patient is urgently needed.
Disclosure of Invention
The invention provides an absolute quantitative analysis method of medical image information based on patient integrated PET/MRI scanning, which can quickly and accurately obtain the absolute numerical value of the 18F-FDG glucose metabolic rate of a patient, solves the numerical value error of a common semi-quantitative analysis method and avoids the injury of arterial blood collection to the patient.
The invention provides a glucose metabolic rate quantitative analysis method based on a medical image, which is characterized by comprising the following steps of: firstly, acquiring a medical image of a patient, wherein the medical image is a medical image of an integrated PET/MRI scan; secondly, preprocessing the medical image, wherein the preprocessing comprises extracting patient characteristic information in the medical image and converting the image format of the medical image; thirdly, extracting a medical image region of interest from the preprocessed medical image; fourth, patient cephalomotion correction is performed on the medical image region of interest; fifthly, partial volume effect correction is carried out on the medical image area after the head movement correction; sixthly, performing Patlak graphic analysis on the medical image region after the partial volume effect correction to obtain a glucose metabolism parameter image of the patient, and finally obtaining the glucose metabolism rate in the interested medical image region.
Further, in the acquiring a medical image of the patient step, the medical image comprises an FDG PET image, a fast spin echo T2 weighted image, a spin echo T1 weighted image, a liquid attenuation inversion recovery image, and magnetic resonance vessel imaging; the acquisition range covers all brain tissue of the patient from the skull base to the skull top.
Further, in the pre-processing of the medical image, the converting of the image format of the medical image is a converting of a raw medical image in DICOM format into a medical image in digital medical image data format.
Further, in the step of extracting the medical image region of interest from the preprocessed medical image, the following process is included: first, extracting a carotid artery system of the patient based on the preprocessed medical image; secondly, performing unilateral internal carotid artery segmentation on the patient's carotid artery system to obtain a unilateral internal carotid artery rock-region area as the interested medical image area.
Further, the patient region of interest is registered with the dynamic PET image and other MRI sequences based on the patient's own MRI 3D T1 structural image as a reference image, and co-registration is performed using a standardized mutual information registration algorithm.
Further, the partial volume effect correction step uses a Muller-Gartner equinox method.
Further, in the Patlak graph analysis step, a corresponding time activity curve is obtained by using the medical image after the patient is corrected by the head movement, and the Patlak graph analysis at the voxel level is performed on an image driving input function.
The invention also provides a system for quantitatively analyzing the glucose metabolic rate based on the medical image, which is characterized by comprising the following components: a medical image acquisition module that acquires a medical image of a patient, the medical image being a medical image of an integrated PET/MRI scan; a medical image preprocessing module, which preprocesses the medical image, wherein the preprocessing includes extracting patient feature information in the medical image and converting the image format of the medical image; a medical image region of interest extraction module that extracts a medical image region of interest in the preprocessed medical image; a patient cephalomotion correction module that performs patient cephalomotion correction on the medical image region of interest; a partial volume effect correction module, which performs partial volume effect correction on the medical image region after the head movement correction; and the Patlak graphic analysis module is used for carrying out Patlak graphic analysis on the medical image area after the partial volume effect correction to obtain a glucose metabolism parameter image of the patient and finally obtain the glucose metabolism rate in the interested medical image area.
The present invention also provides a computer device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of any one of the above-mentioned methods for screening risk of endometrial cancer based on artificial intelligence.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement the steps of any one of the above-mentioned artificial intelligence-based endometrial cancer risk screening methods.
The method provided by the invention has the advantages that the glucose metabolic rate is quantitatively analyzed based on the 18F-FDG PET/MRI image of the patient, the absolute value of the glucose proxy rate of the patient is given, and the cerebral metabolic activity can be more accurately evaluated compared with the existing semi-quantitative method.
Drawings
FIG. 1 shows a schematic flow chart of the steps of the present invention.
Fig. 2 shows the image after the image format conversion of the present invention.
Fig. 3 shows a schematic view of a segmentation procedure for the carotid arterial system.
Fig. 4 shows a schematic diagram of the segmentation of the region of the internal carotid artery lithosphere.
Fig. 5 shows an image-driven derived time activity curve.
Figure 6 shows the corresponding brain glucose metabolism images obtained after the patlak analysis.
Detailed Description
The following examples and experimental examples are intended to illustrate the present invention, but are not intended to limit the scope of the present invention. The present invention will be further described with reference to specific examples and experimental examples.
The invention provides a quantitative analysis method of glucose metabolic rate based on a medical image. In a preferred embodiment, since the patient usually has progressive stenosis or obstruction in the unilateral internal carotid artery, the method provides a quantitative model for PET/MRI scanning image data sequence analysis of the patient, performs image input function processing on the unilateral internal carotid artery rock region of the patient, finally obtains the glucose metabolic rate of the patient,
FIG. 1 shows a schematic flow chart of the steps of the present invention. As shown in the figure, after medical images of the integrated PET/MRI scanning of the patient are collected, image preprocessing, unilateral internal carotid artery segmentation, patient head motion correction and partial volume effect correction image input function extraction and voxel level-based patlak analysis are respectively carried out, and finally brain glucose metabolic rate images of the patient are obtained, and brain glucose metabolic rate absolute numerical values of the infarct area and the healthy area of the patient are obtained. The method comprises the following specific steps:
first, medical images of an integrated PET/MRI scan of a patient are acquired, and when the PET scan is performed, the acquisition sequences are respectively 9 × 10 seconds, 3 × 30 seconds, 4 × 60 seconds, 6 × 180 seconds, and 9 × 300 seconds, for a total of 31 FDG PET images. In performing an MRI scan, the acquisition sequences are a fast spin echo T2 weighted image, a spin echo T1 weighted image, a fluid-attenuated inversion recovery (FLAIR) image, and magnetic resonance angiography, respectively. In this embodiment, the acquisition range covers the entire brain tissue of the patient from the base of the skull to the top of the skull.
Secondly, the acquired medical image is preprocessed, which comprises the following steps: and information such as the age, the height, the weight, the sex, the blood sugar degree and the like of the patient in the medical image is extracted, so that the subsequent absolute quantitative analysis is convenient. The image format is converted, the original medical image in the DICOM format is converted into the medical image in the digital medical image data format, and the digital medical image data format can enhance the functions of various neuroimaging data processing software and the sharing of processing data. Fig. 2 shows the image after the image format conversion of the present invention.
And automatically segmenting the magnetic resonance blood vessel imaging data in the preprocessed medical image by using the preprocessed medical image, then obtaining the rock-end region of the unilateral internal carotid artery, and selecting the region as the interested region, thereby facilitating the subsequent calculation of the glucose metabolic rate of the patient.
In the method, a neck artery system is extracted based on the preprocessed magnetic resonance blood vessel imaging, as shown in fig. 3, the whole neck artery system of the patient is extracted based on a combined algorithm of a quantile threshold value of a histogram and automatic seed region growing in the implementation, and a quantile intensity of 0.987 corresponding to gray value distribution is selected as an optimal threshold value. To eliminate the residual effect of the patient's neck peripheral fat, the combinatorial algorithm performs automatic seed region growing using connectivity constraints, yielding only the neck artery portion.
The segmented carotid artery system consisting of an internal carotid artery and an external carotid artery is obtained in fig. 3. Determining the value of the glucose metabolic rate requires obtaining the internal carotid artery, and in the present embodiment, only the lithologic region in the internal carotid artery, as shown in fig. 4, which shows the step of segmenting the lithologic region of the internal carotid artery. This step is based on the determination of the region of the internal carotid artery lithosphere, which characterizes the shape of the vessel tree, since only the region of the internal carotid artery lithosphere needs to be obtained, and therefore the morphological feature curve is only calculated for the pre-processed intracranial segment of the magnetic resonance vessel imaging.
The morphological characteristic curve includes characteristics such as average intensity, major axis length, ellipticity, and direction of vessel segments present in the cross-axis slice. An axial slice at 90 degrees to the vessel indicates the location of the cavernous segment of the internal carotid artery, which needs to be removed. The internal and external carotid arteries originate in the common carotid artery, and the location of the branch point is critical for trimming the arterial vessel. The morphological feature curve highlights elliptical structures in the image as the peak of the protrusion, the internal carotid artery rock-tip region is determined as the structure with the highest peak, and finally the internal carotid artery rock-tip region is the only remaining part.
Then, a head movement correction is performed for reducing a difference caused by a head shake of the patient in performing the integrated PET/MRI scan.
This step uses the MRI 3D T1 structural image as a reference image, aligns the dynamic PET image with other MRI sequences, and performs co-registration using a standardized mutual information registration algorithm. The registration algorithm minimizes registration errors (e.g., least squares or mutual information) between the PET image and the MRI image based on a cost function. The cost function is aligned according to shared information between the data (e.g., cortical boundaries) such that it depends to some extent on the intensity distribution and resolution of the acquired data.
Then, partial volume effect correction is performed. The Partial Volume Effect (PVE) means that when two or more substances with different densities are contained in the same scanning slice, the measured value is the average of the signal values of the substances, and the signal value of any one of the substances cannot be truly reflected. The partial volume effect affects the accuracy of the PET image, and therefore, partial volume effect correction is required.
In this step, the Muller-Gartner equinox method (MG method) after update is used. The MG method is a three-chamber partial volume correction method, and a two-chamber method for differentiating brain parenchyma and peripheral cerebrospinal fluid signals is expanded and is a commonly used partial volume correction method. The method assumes that the PET signal observed in any given gray matter voxel is the spatially weighted average of the true tracer uptake signals in gray matter voxels, as well as the signals in the surrounding white matter and cerebrospinal fluid, with the spatial weighting determined by the point spread function of the PET scanner. The proposed partial volume correction algorithm includes correcting for possible signal spillover effects to gray matter, and signal spillover to surrounding tissue.
And then, after head movement correction and partial volume effect correction, Patlak graphic analysis is carried out to obtain a cerebral glucose metabolism parameter image of the patient. In this step, the corresponding temporal activity curve is obtained using the head motion corrected PET image, and fig. 5 shows the image drive resulting temporal activity curve.
Voxel-level Patlak graphic analysis was performed on the respective image-driven input functions (IDIF), with the lumped constant set to 0.89 in this example, and a linear function was fitted to the Patlak graphic analysis transformed data, including data from 25 minutes after injection to the end of the study (9 data points). The resulting slope is then multiplied by the patient's plasma glucose level (mmol/L) and divided by the lumped constant (C = 0.89) to generate an image of the patient's brain glucose metabolism parameter in mmol/100 g/min, yielding the absolute values of brain glucose metabolism for the infarct and healthy regions of the patient, respectively. Figure 6 shows the corresponding brain glucose metabolism images obtained after the patlak analysis.
The present invention also provides a system for glucose metabolic rate quantitative analysis based on medical images, the quantitative analysis system comprising: a medical image acquisition module that acquires a medical image of a patient, the medical image being a medical image of an integrated PET/MRI scan; a medical image preprocessing module, which preprocesses the medical image, wherein the preprocessing includes extracting patient feature information in the medical image and converting the image format of the medical image; a medical image region of interest extraction module that extracts a medical image region of interest in the preprocessed medical image; a patient cephalomotion correction module that performs patient cephalomotion correction on the medical image region of interest; a partial volume effect correction module, which performs partial volume effect correction on the medical image region after the head movement correction; and the Patlak graphic analysis module is used for carrying out Patlak graphic analysis on the medical image area after the partial volume effect correction to obtain a glucose metabolism parameter image of the patient and finally obtain the glucose metabolism rate in the interested medical image area.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement any of the steps of the method for glucose metabolic rate quantitative analysis based on medical images.
The present invention also provides a computer readable storage medium having stored thereon a computer program, characterized in that the program, when being executed by a processor, is adapted to carry out the steps of a method for glucose metabolic rate quantitative analysis based on medical images as set forth in any of the preceding claims.
The invention has the beneficial effects that:
according to the invention, based on the PET/MRI scanning image of the patient, the Patlak graph analysis is carried out on the unilateral internal carotid artery rock area to obtain the absolute numerical value of the glucose metabolic rate, the defect of unstable reference brain area in a common semi-quantitative analysis method is avoided, the abnormality of the brain area of the patient can be found in time, the obtained numerical value of the glucose metabolic rate can be used for evaluating the curative effect of the schemes such as surgical treatment and the like or the postoperative recovery condition of the patient, and the clinician and the patient can be helped to better understand the development and recovery condition of the disease.
Meanwhile, different from arterial blood collection, the method provided by the invention does not need a patient to carry out blood collection operation for many times, and reduces the wound to the patient in the blood collection process.
The system can also be displayed in an interactive interface mode, such as an image workstation, and the absolute value of the glucose metabolic rate can be obtained only by inputting the PET/MRI scanning image of the patient without the guidance of a professional clinician, so that the medical resource is saved, and the treatment reference is provided for the majority of clinicians.

Claims (10)

1. A glucose metabolic rate quantitative analysis method based on medical images is characterized by comprising the following steps:
firstly, acquiring a medical image of a patient, wherein the medical image is a medical image of an integrated PET/MRI scan;
secondly, preprocessing the medical image, wherein the preprocessing comprises extracting patient characteristic information in the medical image and converting the image format of the medical image;
thirdly, extracting a medical image region of interest from the preprocessed medical image;
fourth, patient cephalomotion correction is performed on the medical image region of interest;
fifthly, partial volume effect correction is carried out on the medical image area after the head movement correction;
sixthly, performing Patlak graphic analysis on the medical image region after the partial volume effect correction to obtain a glucose metabolism parameter image of the patient, and finally obtaining the glucose metabolism rate in the medical image region of interest.
2. A medical image based glucose metabolic rate quantitative analysis method according to claim 1, wherein in the medical image acquiring step of the patient, the medical image comprises an FDG PET image, a fast spin echo T2 weighted image, a spin echo T1 weighted image, a liquid attenuation inversion recovery image and a magnetic resonance angiography; the acquisition range covers all brain tissue of the patient from the skull base to the skull top.
3. The method of claim 1, wherein the step of pre-processing the medical image comprises converting the image format of the medical image into a DICOM format raw medical image into a digital medical image data format.
4. The method for glucose metabolic rate quantitative analysis based on medical images as claimed in claim 1, wherein in the step of extracting the medical image region of interest from the preprocessed medical image, the method comprises the following steps:
first, extracting a carotid artery system of the patient based on the preprocessed medical image;
secondly, performing unilateral internal carotid artery segmentation on the patient's carotid artery system to obtain a unilateral internal carotid artery rock-region area as the interested medical image area.
5. A method for medical image-based glucose metabolic rate quantitative analysis according to claim 1, wherein the region of interest of the patient is registered with the dynamic PET image and other MRI sequences based on the patient's own MRI 3D T1 structural image as a reference image, and the co-registration is performed using a standardized mutual information registration algorithm.
6. The medical image-based glucose metabolic rate quantitative analysis method according to claim 1, wherein the partial volume effect correction step uses a Muller-Gartner equinox method.
7. The medical image-based glucose metabolic rate quantitative analysis method according to claim 1, wherein in the Patlak graph analysis step, the medical image after the patient's head movement correction is used to obtain a corresponding time activity curve, and the image-driven input function is subjected to voxel-level Patlak graph analysis.
8. A system for glucose metabolic rate quantitative analysis based on medical images, the quantitative analysis system comprising:
a medical image acquisition module that acquires a medical image of a patient, the medical image being a medical image of an integrated PET/MRI scan;
a medical image preprocessing module, which preprocesses the medical image, wherein the preprocessing includes extracting patient feature information in the medical image and converting the image format of the medical image;
a medical image region of interest extraction module that extracts a medical image region of interest in the preprocessed medical image;
a patient cephalomotion correction module that performs patient cephalomotion correction on the medical image region of interest;
a partial volume effect correction module, which performs partial volume effect correction on the medical image region after the head movement correction;
and the Patlak graphic analysis module is used for carrying out Patlak graphic analysis on the medical image area after the partial volume effect correction to obtain a glucose metabolism parameter image of the patient and finally obtain the glucose metabolism rate in the interested medical image area.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of a method for glucose metabolic rate quantitative analysis based on medical images as claimed in any one of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of a method for glucose metabolic rate quantitative analysis based on medical images as claimed in any one of claims 1 to 7.
CN202210860942.1A 2022-07-22 2022-07-22 Glucose metabolic rate quantitative analysis method and system based on medical image Pending CN114936989A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060074290A1 (en) * 2004-10-04 2006-04-06 Banner Health Methodologies linking patterns from multi-modality datasets
CN101172038A (en) * 2007-09-27 2008-05-07 南京信息工程大学 Non-damage quantitative computing method for partial glucose metabolic rate of human brain etc.

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060074290A1 (en) * 2004-10-04 2006-04-06 Banner Health Methodologies linking patterns from multi-modality datasets
CN101172038A (en) * 2007-09-27 2008-05-07 南京信息工程大学 Non-damage quantitative computing method for partial glucose metabolic rate of human brain etc.

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
L. K. S. SUNDAR 等: "Promise of fully integrated pet/mri: noninvasive clinical quantification", 《JOURNAL OF NUCLEAR MEDICINE》 *
L. K. SUNDAR 等: "Towards quantitative [18f] fdg-pet/mri of the brain: automated mrdriven calculation of an image-derived input function for the noninvasive determination of cerebral glucose metabolic rates", 《JOURNAL OF CEREBRAL BLOOD FLOW & METABOLISM》 *
吴义根等: "用rPatlak图解及动态FDG-PET方法生成脑局部葡萄糖代谢率参数图", 《科学通报》 *

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