Visualization imaging method, device, equipment and storage medium of tumor microenvironment

Abstract

The embodiment of the present invention provides a method, device, equipment and storage medium for visualizing tumor microenvironment. The method obtains MRI scan data of glioma patients, and the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; based on T2 * map sequence, T2map sequence, DWI sequence, as well as gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection, obtain oxygen metabolism and neovascularization parameter information of glioma patients through vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging; determine the imaging parameters of the tumor microenvironment of glioma patients based on the oxygen metabolism and neovascularization parameter information of glioma patients; visualize the tumor microenvironment of glioma patients based on the imaging parameters. Re-establish a set of non-invasive tumor microenvironment imaging system that can be clinically applied to realize the visualization of individualized metabolic heterogeneity of glioma.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of medical magnetic resonance imaging, and in particular to a method, device, equipment and storage medium for visualizing a tumor microenvironment. Background Art

[0002] In recent years, the tumor microenvironment (TME) and its heterogeneity have been considered as key regulators of glioma progression and treatment response, and metabolic reprogramming has been regarded as the main factor and characteristic affecting TME. High hypoxia and metabolic reprogramming are prominent features of high-grade gliomas, mainly manifested as the Warburg effect, that is, aerobic glycolysis, but at the same time, tumor cells can also undergo oxidative phosphorylation, and there is obvious heterogeneity in glioma sugar metabolism. This metabolic heterogeneity can cause cells to have different levels of tolerance to radiotherapy and chemotherapy. In-depth research on tumor metabolism, exploration of key "metabolic checkpoints", and development of a variety of drugs targeting glioma metabolism, and tumor treatment strategies based on metabolic reprogramming can provide guidance for improving the effectiveness of glioma diagnosis and treatment from the level of drug resistance mechanisms, and open up new directions for the treatment of gliomas.

[0003] At present, a variety of metabolic imaging technologies have been developed for tumor microenvironment (TME) metabolic imaging, including positron emission tomography (PET), dynamic hyperpolarized 13C magnetic resonance spectroscopy imaging (HP-13C-MRSI), and deuterium metabolism (DMI). PET quantifies glucose uptake by detecting the radiation intensity of 18F-FDG, and detects the increased glucose uptake that is common in malignant tumor tissues. However, PET is difficult to dynamically monitor the entire glucose metabolism pathway. In the brain with high background glucose uptake, it is limited by the low background contrast; and PET examinations involve ionizing radiation, which is not conducive to longitudinal follow-up. Although HP-13C-MRSI can detect downstream metabolites of glucose and can detect tumor glycolysis and tricarboxylic acid cycle kinetic fluxes, due to the instability of HP-13C (only lasting 1 to 2 minutes) and the complexity of DNP technology, this detection method requires additional hardware support, and its clinical implementation worldwide is still facing challenges, and its clinical transformation is still challenging. Although deuterium metabolism imaging (DMI) can perform non-invasive in vivo imaging of multiple sugar metabolic pathways in tumors, the clinical application of deuterated contrast agents has not yet been popularized. Summary of the invention

[0004] The embodiments of the present invention propose a method, device, equipment and storage medium for visualizing the tumor microenvironment to overcome various deficiencies in existing tumor microenvironment metabolic imaging technology, and to re-establish a set of non-invasive tumor microenvironment imaging systems that can be clinically applied. Multi-parameter imaging based on MRI can realize the visualization and quantitative analysis of individualized metabolic heterogeneity of gliomas.

[0005] In a first aspect, an embodiment of the present invention provides a method for visualizing a tumor microenvironment, comprising:

[0006] Obtain MRI scan data of a glioma patient, wherein the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection;

[0007] Based on the T2 * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, obtaining the oxygen metabolism and neovascularization parameter information of the glioma patient through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging;

[0008] Determining imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and neovascularization parameter information of the glioma patient;

[0009] The tumor microenvironment of the glioma patient is visualized based on the imaging parameters.

[0010] In a second aspect, an embodiment of the present invention further provides a visualization imaging device for a tumor microenvironment, comprising:

[0011] The data acquisition module is used to acquire MRI scan data of glioma patients, wherein the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection;

[0012] Data processing module for * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, obtaining the oxygen metabolism and neovascularization parameter information of the glioma patient through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging;

[0013] An imaging parameter determination module, used to determine the imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and neovascularization parameter information of the glioma patient;

[0014] A visualization module is used to visualize the tumor microenvironment of the glioma patient based on the imaging parameters.

[0015] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0016] one or more processors;

[0017] a memory for storing one or more programs,

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the visualization imaging method as described in any one of the first aspects.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the visualization imaging method as described in any one of the first aspects is implemented.

[0020] In the embodiment of the present application, MRI scan data of a glioma patient is obtained, and the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; based on T2 * map sequence, T2map sequence, DWI sequence, as well as gradient echo DSC sequence and spin echo DSC sequence collected after contrast agent injection, obtain oxygen metabolism and neovascularization parameter information of glioma patients through vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging; determine the imaging parameters of the tumor microenvironment of glioma patients according to the oxygen metabolism and neovascularization parameter information of glioma patients; visualize the tumor microenvironment of glioma patients according to the imaging parameters. The embodiment of the present application establishes a set of clinically applicable non-invasive tumor microenvironment imaging system, and MRI-based multi-parameter imaging can obtain quantitative information on oxygen metabolism and neovascularization of glioma patients. According to these parameters, the pathophysiological mechanism of biological tumors can be reflected, and the visualization of oxygen metabolism reprogramming of glioma tumor microenvironment and the quantification of metabolic heterogeneity can be realized, and the visualization and quantitative analysis of individualized metabolic heterogeneity of glioma can be further realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a visual imaging method provided in Embodiment 1 of the present invention;

[0022] Figure 2 A schematic diagram of the structure of a visual imaging device provided in Embodiment 2 of the present invention;

[0023] Figure 3 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0025] Embodiment 1

[0026] Figure 1 This is a flow chart of a visualization imaging method provided in Embodiment 1 of the present invention. This embodiment can be applied to the case of non-invasive visualization imaging of the tumor microenvironment of brain tumor patients, and can also be applied to the case of non-invasive imaging of the oxygen metabolism-reprogrammed microenvironment of brain glioma in clinical work; the method can be executed by a visualization imaging device, which can be implemented by software and / or hardware and can be configured in a computer device, such as a server, a workstation, a personal computer, a medical system, a scanner, a medical detection device, etc., and specifically includes the following steps:

[0027] S110, obtain MRI scan data of glioma patients, the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, as well as gradient echo DSC sequence and spin echo DSC sequence acquired after injection of contrast agent.

[0028] In an embodiment of the present application, MRI scan data of a glioma patient can be obtained by performing an MRI scan on the brain of the glioma patient. The MRI scan data refers to data obtained using magnetic resonance imaging (MRI) technology; the presentation form of the MRI scan data can be in the form of image data. For example, an MRI scanner can be configured to scan an object (such as a glioma patient) to obtain image data. The MRI scanner can detect at least two echo signals by applying an MRI pulse sequence to the object. The embodiment of the present application does not specifically limit this.

[0029] The MRI scan data to be acquired in the present embodiment includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent is injected into the glioma patient.

[0030] It is understood that the relaxation parameters of an MRI scan object in the art generally include T1 (longitudinal relaxation time), T2 (transverse relaxation time), T2 *(the inverse of the transverse relaxation time), etc., T1 can be the time constant for the regrowth of the longitudinal magnetization (for example, along the main magnetic field), T2 relaxation can be the process by which the transverse component of the magnetization decays or dephasing, and T2 can be the time constant for the decay or dephasing of the transverse magnetization. In magnetic resonance imaging (MRI), relaxation parameters are a measure of how quickly hydrogen nuclei in tissues return to their original state, and are usually assessed by measuring the longitudinal relaxation time T1 and the transverse relaxation time T2 of the tissue. Gradient echo and spin echo are two different MRI sequences that can both be used to measure relaxation parameters, but their working principles and characteristics are different.

[0031] In the present application example, T2 * The map sequence can be understood as the T2 * The T2map sequence can be understood as a sequence obtained from an MRI scan that can measure the T2 value (transverse relaxation time). The DWI sequence is diffusion-weighted imaging, a special sequence of MRI that is often used to detect the diffusion movement of water molecules in the body. The nuclear magnetic resonance DWI sequence is a rapid excitation in a magnetic field that produces an oscillation signal, which can detect the movement of water molecules. Some malignant tumor cells grow densely and have less free water, which results in restricted diffusion in the malignant tumor and forms a high signal, that is, the DWI sequence can be used to diagnose tumors.

[0032] Gradient echo DSC sequence refers to the gradient echo sequence (GRE) acquired after the signal intensity and contrast of MRI are enhanced by the dynamic susceptibility contrast (DSC) technique. DSC technology is a special technique for magnetic resonance imaging that uses paramagnetic contrast agents (such as gadolinium) to enhance the signal intensity and contrast of MRI. DSC perfusion imaging begins with the intravenous injection of a bolus of gadolinium chelate, followed by a series of rapid acquisition of gradient or spin echo images on the organ of interest. The imaging energy of MRI is radio frequency pulse (RF), and the gradient echo sequence can be understood as a pulse sequence that generates echo signals and images by switching the gradient field direction after RF excitation in magnetic resonance scanning. Its RF excitation angle is generally less than 90° to obtain a larger transverse magnetization vector in a shorter longitudinal recovery time.

[0033] The spin echo DSC sequence refers to a spin echo sequence (Spin Echo, SE) acquired after using the dynamic sensitivity contrast (Dynamic Susceptibility Contrast, DSC) technology to enhance the signal strength and contrast of MRI. The spin echo sequence (Spin Echo Sequence) is a pulse sequence commonly used in magnetic resonance imaging (MRI). It excites human tissue and collects signals by using a combination of 90° and 180° pulses. Because the imaging energy of MRI is mainly radio frequency pulses (RF), RF is a short-wave electromagnetic wave that is emitted into the magnetic field through a radio frequency coil surrounding the human body. When the order of applying pulses in MRI is first a 90-degree pulse and then a 180-degree pulse, it is called a spin echo sequence.

[0034] S120, based on T2 * map sequence, T2map sequence, DWI sequence, as well as gradient echo DSC sequence and spin echo DSC sequence acquired after injection of contrast agent, obtain oxygen metabolism and neovascularization parameter information of glioma patients through vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging.

[0035] In a specific implementation of the embodiment of the present application, S120 may include the following steps:

[0036] S1201. Determine the surface diffusion coefficient according to the linear registration result between the DWI sequence and the spin echo DSC sequence acquired after the contrast agent is injected.

[0037] Specifically, the diffusion-free gradient weighted data b0 can be extracted from the DWI sequence, and the image presented by the DWI sequence and the image presented by the spin echo DSC sequence acquired after the injection of the contrast agent can be linearly aligned with reference to the b0 map (i.e., the imaging result when the b value is 0). According to the aligned DWI data, the apparent diffusion coefficient (Apparent Diffusion Coefficient, ADC) is calculated based on the formula: ADC = -ln[(S / S0) / b]; wherein S0 is the diffusion-free gradient weighted data, S is the diffusion gradient weighted data, and b is the b value of the diffusion gradient.

[0038] S1202. Process the gradient echo DSC sequence and the spin echo DSC sequence acquired after the contrast agent is injected to obtain cerebral blood volume and cerebral blood flow velocity.

[0039] In some specific examples, S1202 may include the following specific data processing steps:

[0040] S12021. Determine a target image obtained by converting a gradient echo DSC sequence and a spin echo DSC sequence acquired after injection of contrast agent.

[0041] Specifically, an image presented by a gradient echo DSC sequence acquired after the contrast agent is injected may be used as the target image, or an image presented by a spin echo DSC sequence acquired after the contrast agent is injected may be used as the target image.

[0042] S12022. Perform motion correction on the target image, remove the skull, and correct the acquisition time of each layer to obtain a processed target image.

[0043] Among them, the open source toolkit FSL can be used to perform motion correction, skull removal and acquisition time correction on the target image.

[0044] S12023. Determine the relaxation rate change ΔR2(t) caused by the contrast agent corresponding to each pixel in the processed target image.

[0045] Among them, the formula can be used The DSC signal of each pixel in the processed target image is converted into the R2 change ΔR2(t) caused by the contrast agent. It should be noted that TE in the above formula is the echo time of the sequence (spin echo DSC sequence or gradient echo DSC sequence) corresponding to the target image, S(t) is the DSC signal that changes with time, and S0 is the DSC signal before the contrast agent reaches the brain.

[0046] S12024, performing contrast agent leakage correction on ΔR2(t) corresponding to each pixel to obtain a corrected ΔR2(t), so as to reduce the contrast agent leakage effect caused by blood-brain barrier damage.

[0047] S12025. Fit the leakage-corrected ΔR2(t) to obtain a fitting result.

[0048] Among them, the gamma-variate function can be used to fit the leakage-corrected ΔR2(t) to reduce the recirculation effect of the contrast agent.

[0049] S12026. Convert ΔR2(t) corresponding to each pixel into a contrast agent concentration C(t), and perform deconvolution on the contrast agent concentration C(t) corresponding to each pixel to obtain a tissue response function R(t).

[0050] Among them, a block-circulant singular value decomposition algorithm can be used to deconvolve the contrast agent concentration C(t) of each pixel through the arterial input function AIF to obtain the tissue response function R(t).

[0051] S12027. Calculate cerebral blood volume (CBV) and cerebral blood flow (CBF) based on the tissue response function R(t) and the contrast agent concentration C(t).

[0052] Before performing the step of fitting the leakage-corrected ΔR2(t) to obtain the fitting result, the following step of controlling the image quality may be included:

[0053] The arterial input functions (AIF) and venous output functions (VOF) were obtained using an automatic detection algorithm. The AIF and VOF were used to ensure the accuracy of the data for calculating cerebral blood volume and cerebral blood flow velocity.

[0054] The arterial input function was corrected for partial volume effects based on the venous output function.

[0055] Among them, the arterial input function (AIF) can represent the change of contrast agent concentration in the cerebral blood supply artery over time.

[0056] S1203, according to T2 * The linear registration results of the map sequence and T2map sequence with the spin echo DSC sequence acquired after contrast agent injection were used to determine R2 and R2 * Quantitative data.

[0057] Specifically, you can start from T2 * T2 and T2 are extracted from the map sequence and T2map sequence respectively. * Quantitative data, T2 and T2 * The quantitative data were linearly registered with the images presented by the spin echo DSC sequence acquired after the injection of contrast agent, and the registered T2 and T2 * Conversion of quantitative data to R2 and R2 * Quantitative data can be converted according to the following formula: R2 = 1 / T2, R2 * =1 / T2 * .

[0058] S1204, according to R2 and R2 * Quantitative data, cerebral blood volume, cerebral blood flow velocity, and oxygen metabolism parameter information of glioma patients were obtained through multi-parameter quantitative blood oxygen level-dependent imaging.

[0059] Among them, oxygen metabolism parameter information includes brain oxygen uptake fraction, brain oxygen metabolic rate, and tissue oxygen tension. Brain oxygen uptake fraction (OEF) reflects the proportion of oxygen obtained by tissues from the blood through the capillary network and is a specific biomarker for assessing tissue function. In hypoperfusion areas with reduced cerebral blood flow, an increase in OEF can often be considered a compensatory mechanism to maintain tissue function. Magnetic resonance imaging (MRI) is a relatively safe and contrast-rich imaging modality. If changes in OEF can be detected, it will have more important clinical application value and greater research flexibility.

[0060] The above-mentioned multi-parameter quantitative blood oxygenation level-dependent imaging (quantitative blood oxygenation level-dependent, qBOLD), namely qBOLD technology, is one of the branch technologies of MRI to measure OEF. It mainly utilizes the blood oxygen level dependence (BOLD) effect to indirectly reflect the changes in intracranial blood oxygen metabolism. Due to the disturbance of deoxyhemoglobin (dHb) on MRI signals, there are certain differences in qBOLD signals at different echo times. With the help of the MR signal attenuation model, qBOLD can achieve OEF quantification at the voxel level of the whole brain. This is also the earliest imaging technology that can achieve local tissue OEF quantification. This technology does not need to obtain the specific geometric information of blood vessels, but only the statistical characteristics of blood vessel distribution. This is one of the important reasons why it can achieve voxel-level quantification.

[0061] In the specific implementation of this embodiment, according to R2 and R2 * The quantitative data, as well as the cerebral blood volume, were used to obtain the cerebral oxygen uptake fraction (OEF); the cerebral metabolic rate of oxygen (CMRO2) was obtained based on the cerebral oxygen uptake fraction and cerebral blood flow rate; and the tissue oxygen tension (PO2) was obtained based on the cerebral metabolic rate of oxygen and the cerebral oxygen uptake fraction.

[0062] In one example, the brain oxygen uptake fraction (OEF) can be obtained by formula (1), the brain oxygen metabolic rate (CMRO2) can be obtained by formula (2), and the tissue oxygen tension (PO2) can be obtained by formula (3):

[0063]

[0064] In formula (1), γ = 2.67502 × 10 8 rad / s / T), is the magnetogyroscopic ratio; Δχ=0.264×10 -6 , is the magnetic susceptibility difference between fully oxygenated and fully deoxygenated hemoglobin; Hct = 0.42 × 0.85, is the microvascular red blood cell pressure fraction, 0.85 is the correction factor for small blood vessels; B0 = 3T, is the main magnetic field intensity of MRI.

[0065] CMRO2=OEF·CBF·Ca (2)

[0066] In formula (2), C a =8.68mmol / mL, which is the arterial blood oxygen content.

[0067]

[0068] In formula (3), P 50 =27mmHg, half-saturated oxygen tension of hemoglobin; h = 2.7, Hill coefficient of oxygen binding to hemoglobin; L = 4.4mmol / Hg / min, oxygen conductivity of tissues. PO2 reflects the balance between oxygen input and consumption of tissues.

[0069] S1205. According to the surface diffusion coefficient and cerebral blood volume, as well as the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, the new blood vessel parameter information of the glioma patient is obtained by the vascular structure construction method.

[0070] Among them, the new blood vessel parameter information may include indicators of microvascular types, microvascular density and blood vessel size. The above-mentioned vascular structure construction method can be understood as vascular architecture imaging (VAI) MRI, which is a technology that uses magnetic resonance imaging MRI technology to study and evaluate vascular structure and function, and is a technology that non-invasively measures parameters to describe the heterogeneity of brain microvascular structure. MRI, as a non-invasive medical imaging technology, uses a strong external magnetic field and a specific radio frequency pulse to act on hydrogen nuclei in the human body to produce a magnetic resonance phenomenon, and reconstructs images by collecting and processing resonance signals, thereby showing the internal tissue structure of the human body. On this basis, vascular architecture imaging (VAI) MRI focuses on detailed imaging of the vascular system, including veins, arteries and other vascular structures, such as coronary arteries, intracranial blood vessels, neck blood vessels, etc. This technology can provide detailed information about vascular morphology, structure and function, which is of great significance for diagnosing vascular-related diseases, evaluating disease progression and monitoring treatment effects. In this embodiment, vascular architecture imaging (VAI) MRI can be used for intracranial vascular imaging, which can be used to determine whether the intracranial blood vessels are deformed, whether there are intracranial aneurysms, and the related conditions of the tumor.

[0071] In some specific examples, S1205 may include the following specific data processing steps:

[0072] A vascular hysteresis loop diagram is constructed based on the fitting results obtained in the specific implementation step of S1202 above.

[0073] The area formed by the hysteresis loop in the vascular hysteresis loop (VHL) is determined as the micro-vessel type indicator (MTI), wherein the sign of the micro-vessel type indicator is determined by the direction of the hysteresis loop in the vascular hysteresis loop; that is, the sign of the MTI is determined by the direction of the VHL (when the VHL rotates clockwise, the sign of the MTI is positive; when the VHL rotates counterclockwise, the sign of the MTI is negative). When the absolute value of the MTI is small (e.g. -5s -5 / 2 <MTI<5s -5 / 2 ), indicating that the tissue is mainly composed of capillaries; when MTI is a small negative value (such as MTI<-60s -5 / 2 ), indicating that the tissue is mainly composed of venous vessels; when MTI is a large positive value (such as MTI>60s -5 / 2 ), indicating that the tissue is mainly composed of arterial blood vessels.

[0074] Microvessel density (MVD) and vessel size index (VSI) are calculated based on the surface diffusion coefficient (ADC) and cerebral blood volume (CBV). Specifically, microvessel density (MVD) and vessel size index (VSI) can be calculated using formula (4) and formula (5):

[0075]

[0076] In the above formula, Q max =max[ΔR 2,GE (t)] / max[(ΔR 2,SE (t)) 3 / 2 ], ΔR 2,GE (t) and ΔR 2,SE (t) is the fitting result obtained during data processing based on the gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; b = 1.6781, which is a constant; is the mean vascular lumen radius.

[0077] S130. Determine imaging parameters of the tumor microenvironment of the glioma patient based on the oxygen metabolism and angiogenesis parameter information of the glioma patient.

[0078] The imaging parameters in the embodiments of the present application refer to the parameters required for visual imaging of the tumor microenvironment, including the type of parameters displayed visually, the numerical range of each parameter, color information, unit, parameter ratio, etc.

[0079] Among them, the oxygen metabolism and neovascularization parameter information of glioma patients may include six types of specific parameter information, namely, brain oxygen uptake fraction (OEF), brain oxygen metabolic rate (CMRO2), tissue oxygen tension (PO2), microvascular type indicator (MTI), microvascular density (MVD) and vascular size (VSI), and these six types of specific parameter information may be determined as parameter types for visualization. The numerical range of each type of parameter may be determined based on the three types of specific parameter information, namely, brain oxygen uptake fraction (OEF), brain oxygen metabolic rate (CMRO2), and tissue oxygen tension (PO2), in the oxygen metabolism parameter information, and further classification may be performed according to the numerical range, and respective color information may be determined based on the classification results. Color information such as warm colors and cold colors can be assigned based on the MTI value of the microvessel type indicator (MTI) in the neovascularization parameter information (for example, positive MTI values ​​are assigned to warm colors, and negative MTI values ​​are assigned to cold colors); the microvessel radius (RU) and microvessel density (NU) can also be further determined based on the blood vessel size (VSI) and the microvessel density (MVD). The microvessel radius (RU) refers to the radius of the microvessels, and this parameter reflects the smallness of the microvessels. The microvessel density (NU) refers to the number of microvessels per unit volume, and this parameter reflects the distribution density of the microvessels per unit volume. The microvessel density is closely related to the physiological function of the organ or tissue.

[0080] In an embodiment of the present application, the specific parameter information in the above-mentioned oxygen metabolism and neovascularization parameter information can also be fused with MRI biomarker information and classified according to the tumor microenvironment (TME). The classification dimensions can consider: classification of mitochondrial oxidation state; classification completeness of tumor neovascular system; fusion of the classification information and the classification of TME in an MRI imaging data set.

[0081] S140. Visualize the tumor microenvironment of glioma patients based on imaging parameters.

[0082] It should be noted that the tumor microenvironment (TME) is mainly composed of tumor cells, their surrounding immune and inflammatory cells, tumor-associated fibroblasts, and nearby stromal tissues, microvessels, and various cytokines and chemokines. It is a complex integrated system that can be divided into an immune microenvironment dominated by immune cells and a non-immune microenvironment dominated by fibroblasts. The cellular composition and functional state of TME may vary greatly depending on the organ where the tumor occurs, the inherent characteristics of cancer cells, tumor stage, and patient characteristics.

[0083] It is understandable that the heterogeneity of oxygen metabolism, changes in neovascularization, and changes in energy metabolism within the tumor will affect its tumor microenvironment (TME) landscape. Therefore, the imaging parameters determined by the oxygen metabolism and neovascularization parameter information are introduced to visualize the tumor microenvironment of glioma patients. This can realize the visualization and quantitative analysis of the individualized metabolic heterogeneity of gliomas, and further realize the visualization and quantification of the oxygen metabolism phenotype of gliomas, including aerobic glycolysis, namely the Warburg effect.

[0084] The embodiment of the present invention obtains MRI scan data of glioma patients, and the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; based on T2 * map sequence, T2map sequence, DWI sequence, as well as gradient echo DSC sequence and spin echo DSC sequence collected after contrast agent injection, obtain oxygen metabolism and neovascularization parameter information of glioma patients through vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging; determine the imaging parameters of the tumor microenvironment of glioma patients according to the oxygen metabolism and neovascularization parameter information of glioma patients; visualize the tumor microenvironment of glioma patients according to the imaging parameters. The embodiment of the present application establishes a set of clinically applicable non-invasive tumor microenvironment imaging system, and multi-parameter imaging based on MRI can obtain quantitative information on oxygen metabolism and neovascularization of glioma patients. According to these parameters, the pathophysiological mechanism of biological tumors can be reflected, and the visualization of oxygen metabolism reprogramming of glioma tumor microenvironment and the quantification of metabolic heterogeneity can be realized, and the visualization and quantitative analysis of individualized metabolic heterogeneity of glioma can be further realized.

[0085] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0086] Embodiment 2

[0087] Figure 2 A structural block diagram of a visualization imaging device provided in Embodiment 2 of the present invention may specifically include the following modules:

[0088] The data acquisition module 210 is used to acquire MRI scan data of glioma patients, wherein the MRI scan data includes T2 *map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection;

[0089] The data processing module 220 is used to process the * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, obtaining the oxygen metabolism and neovascularization parameter information of the glioma patient through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging;

[0090] An imaging parameter determination module 230, configured to determine imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and angiogenesis parameter information of the glioma patient;

[0091] The visualization module 240 is used to visualize the tumor microenvironment of the glioma patient according to the imaging parameters.

[0092] In one embodiment of the present application, the data processing module 220 may include:

[0093] A surface diffusion coefficient calculation submodule, used to determine the surface diffusion coefficient according to the linear registration result between the DWI sequence and the spin echo DSC sequence acquired after the injection of contrast agent;

[0094] A data processing submodule, used for performing data processing on the gradient echo DSC sequence and the spin echo DSC sequence acquired after the contrast agent is injected, so as to obtain cerebral blood volume and cerebral blood flow velocity;

[0095] The quantitative data processing submodule is used to process the T2 * The linear registration results of the map sequence and the T2map sequence with the spin echo DSC sequence (GE-DSC) were used to determine R2 and R2 * Quantitative data;

[0096] The oxygen metabolism parameter information calculation submodule is used to calculate the oxygen metabolism parameter information according to the R2 and R2 * The quantitative data, the cerebral blood volume, the cerebral blood flow velocity, and the oxygen metabolism parameter information of the glioma patient are obtained by multi-parameter quantitative blood oxygen level dependent imaging;

[0097] The neovascularization parameter information calculation submodule is used to obtain the neovascularization parameter information of the glioma patient through a vascular structure construction method based on the surface diffusion coefficient and the cerebral blood volume, as well as the gradient echo DSC sequence and spin echo DSC sequence collected after the injection of the contrast agent.

[0098] In one embodiment of the present invention, the data processing submodule includes:

[0099] A target image acquisition unit, used to determine a target image converted from a gradient echo DSC sequence and a spin echo DSC sequence acquired after the contrast agent is injected;

[0100] A target image preprocessing unit, used for performing motion correction, skull removal and each layer acquisition time correction on the target image to obtain a processed target image;

[0101] A change calculation unit, used to determine a relaxation rate change ΔR2(t) caused by the contrast agent corresponding to each pixel in the processed target image;

[0102] A contrast agent leakage correction unit, used for performing contrast agent leakage correction on ΔR2(t) corresponding to each pixel to obtain a corrected ΔR2(t);

[0103] A fitting unit, used for fitting ΔR2(t) after leakage correction to obtain a fitting result;

[0104] A response unit, used for converting ΔR2(t) corresponding to each pixel into a contrast agent concentration C(t), and performing deconvolution on the contrast agent concentration C(t) corresponding to each pixel to obtain a tissue response function R(t);

[0105] The cerebral blood volume and cerebral blood flow rate calculation unit is used to calculate the cerebral blood volume and cerebral blood flow rate according to the tissue response function R(t) and the contrast agent concentration C(t).

[0106] In one embodiment of the present invention, the data processing submodule further includes:

[0107] An image quality control unit is used to obtain an arterial input function and a venous output function using an automatic detection algorithm, wherein the arterial input function and the venous output function are used to ensure the accuracy of data for calculating the cerebral blood volume and the cerebral blood flow rate; and to perform partial volume effect correction on the arterial input function according to the venous output function.

[0108] In one embodiment of the present invention, the oxygen metabolism parameter information includes brain oxygen uptake fraction, brain oxygen metabolic rate, and tissue oxygen tension; the oxygen metabolism parameter information calculation submodule includes:

[0109] Cerebral oxygen uptake fraction calculation unit, used to calculate the cerebral oxygen uptake fraction according to the R2 and R2 * Quantitative data, and the cerebral blood volume, to obtain the cerebral oxygen uptake fraction;

[0110] a brain oxygen metabolic rate calculation unit, used for obtaining the brain oxygen metabolic rate according to the brain oxygen uptake fraction and the cerebral blood flow rate;

[0111] A tissue oxygen tension calculation unit is used to obtain the tissue oxygen tension according to the brain oxygen metabolism rate and the brain oxygen uptake fraction.

[0112] In one embodiment of the present invention, the neovascularization parameter information includes an indicator of microvascular type, microvascular density, and blood vessel size; the neovascularization parameter information calculation submodule includes:

[0113] An image construction unit, used for constructing a vascular hysteresis loop diagram based on the fitting result;

[0114] an indicator acquisition unit, used to determine the area formed by the hysteresis loop in the vascular hysteresis loop diagram as the indicator of the microvessel type, wherein the sign of the indicator of the microvessel type is determined by the direction of the hysteresis loop in the vascular hysteresis loop diagram;

[0115] The blood vessel information acquisition unit is used to calculate the microvessel density and the blood vessel size according to the surface diffusion coefficient and the cerebral blood volume.

[0116] The visualization imaging device provided in the embodiment of the present invention can execute the visualization imaging method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0117] Embodiment 3

[0118] Figure 3 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. Figure 3 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 3 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0119] like Figure 3 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0120] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0121] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0122] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0123] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0124] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the computer device 12, and / or may communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0125] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the visualization imaging method provided by the embodiment of the present invention.

[0126] Embodiment 4

[0127] Embodiment 4 of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned visualization imaging method are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0128] Among them, computer-readable storage media may include, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0129] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for visualizing a tumor microenvironment, characterized in that: include: Obtain MRI scan data of a glioma patient, wherein the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; Based on the T2 * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, obtaining the oxygen metabolism and neovascularization parameter information of the glioma patient through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging; Determining imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and neovascularization parameter information of the glioma patient; Performing visual imaging of the tumor microenvironment of the glioma patient according to the imaging parameters; Based on the T2 * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging to obtain the oxygen metabolism and neovascularization parameter information of the glioma patient, including: determining a surface diffusion coefficient according to a linear registration result between the DWI sequence and the spin echo DSC sequence acquired after the injection of the contrast agent; Processing the gradient echo DSC sequence and the spin echo DSC sequence acquired after the contrast agent is injected to obtain cerebral blood volume and cerebral blood flow velocity; According to the T2 * The linear registration results of the map sequence and the T2map sequence with the spin echo DSC sequence acquired after the injection of contrast agent are respectively used to determine R2 and R2 * Quantitative data; According to R2 and R2 * The quantitative data, the cerebral blood volume, the cerebral blood flow velocity, and the oxygen metabolism parameter information of the glioma patient are obtained by multi-parameter quantitative blood oxygen level dependent imaging; According to the surface diffusion coefficient and the cerebral blood volume, as well as the gradient echo DSC sequence and the spin echo DSC sequence acquired after the contrast agent is injected, the new blood vessel parameter information of the glioma patient is obtained by a blood vessel structure construction method; The step of performing data processing on the gradient echo DSC sequence and the spin echo DSC sequence acquired after the contrast agent is injected to obtain the cerebral blood volume and the cerebral blood flow velocity comprises: Determining a target image converted from a gradient echo DSC sequence and a spin echo DSC sequence acquired after the contrast agent is injected; Performing motion correction, skull removal, and each layer acquisition time correction on the target image to obtain a processed target image; Determine a relaxation rate change ΔR2(t) caused by the contrast agent corresponding to each pixel in the processed target image; Performing contrast agent leakage correction on the ΔR2(t) corresponding to each pixel to obtain a corrected ΔR2(t); Fitting the leakage-corrected ΔR2(t) to obtain the fitting result; Converting ΔR2(t) corresponding to each pixel into a contrast agent concentration C(t), and performing deconvolution on the contrast agent concentration C(t) corresponding to each pixel to obtain a tissue response function R(t); The cerebral blood volume and cerebral blood flow velocity are calculated according to the tissue response function R(t) and the contrast agent concentration C(t).

2. The method according to claim 1, characterized in that Before fitting the leakage-corrected ΔR2(t) to obtain the fitting result, the method further includes: Using an automatic detection algorithm to obtain an arterial input function and a venous output function, wherein the arterial input function and the venous output function are used to ensure the accuracy of data for calculating the cerebral blood volume and the cerebral blood flow rate; The arterial input function is corrected for partial volume effect according to the venous output function.

3. The method according to claim 1, characterized in that The oxygen metabolism parameter information includes brain oxygen uptake fraction, brain oxygen metabolic rate, and tissue oxygen tension; * The quantitative data, the cerebral blood volume, the cerebral blood flow velocity, and the oxygen metabolism parameter information of the glioma patient are obtained by multi-parameter quantitative blood oxygen level dependent imaging, including: According to R2 and R2 * Quantitative data, and the cerebral blood volume, to obtain the cerebral oxygen uptake fraction; Obtaining the cerebral oxygen metabolic rate according to the cerebral oxygen uptake fraction and the cerebral blood flow rate; The tissue oxygen tension is obtained according to the brain oxygen metabolic rate and the brain oxygen uptake fraction.

4. The method according to claim 1, characterized in that: The neovascularization parameter information includes an indicator of microvascular type, microvascular density, and blood vessel size; the neovascularization parameter information of the glioma patient is obtained by a vascular structure construction method based on the surface diffusion coefficient and the cerebral blood volume, and the gradient echo DSC sequence and the spin echo DSC sequence collected after the injection of the contrast agent, including: constructing a vascular hysteresis loop diagram based on the fitting results; Determining an area formed by a hysteresis loop in the vascular hysteresis loop diagram as an indicator of the microvessel type, wherein a sign of the indicator of the microvessel type is determined by a direction of the hysteresis loop in the vascular hysteresis loop diagram; The microvessel density and the blood vessel size are calculated based on the surface diffusion coefficient and the cerebral blood volume.

5. The method according to claim 1, characterized in that The imaging parameters include value range, color information, and unit information; Determining the imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and neovascularization parameter information of the glioma patient includes: Determining the color information based on the value of the indicator of the microvessel type in the oxygen metabolism parameter information of the glioma patient; Determine the microvessel radius based on the blood vessel size in the oxygen metabolism parameter information of the glioma patient, and determine the microvessel density based on the microvessel density in the oxygen metabolism parameter information of the glioma patient, and determine the unit information by the microvessel radius and the microvessel density; The numerical range is determined based on the brain oxygen uptake fraction, brain oxygen metabolic rate, and tissue oxygen tension in the oxygen metabolism parameter information of the glioma patient.

6. A visualization imaging device for tumor microenvironment, characterized in that: include: The data acquisition module is used to acquire MRI scan data of glioma patients, wherein the MRI scan data includes T2 * map sequence, T2map sequence, DWI sequence, and gradient echo DSC sequence and spin echo DSC sequence acquired after contrast agent injection; Data processing module for * map sequence, the T2map sequence, the DWI sequence, and the gradient echo DSC sequence and spin echo DSC sequence acquired after the injection of contrast agent, obtaining the oxygen metabolism and neovascularization parameter information of the glioma patient through the vascular structure construction method and multi-parameter quantitative blood oxygen level dependent imaging; An imaging parameter determination module, used to determine the imaging parameters of the tumor microenvironment of the glioma patient according to the oxygen metabolism and neovascularization parameter information of the glioma patient; A visualization module, used for visualizing the tumor microenvironment of the glioma patient according to the imaging parameters; The data processing module includes: a surface diffusion coefficient calculation submodule, which is used to determine the surface diffusion coefficient according to the linear registration result between the DWI sequence and the spin echo DSC sequence acquired after the injection of contrast agent; a data processing submodule, which is used to perform data processing on the gradient echo DSC sequence and the spin echo DSC sequence acquired after the injection of contrast agent to obtain cerebral blood volume and cerebral blood flow rate; a quantitative data processing submodule, which is used to perform data processing on the gradient echo DSC sequence and the spin echo DSC sequence acquired after the injection of contrast agent to obtain cerebral blood volume and cerebral blood flow rate according to the linear registration result between the T2*map sequence and the T2map sequence and the spin echo DSC sequence (GE-DSC) respectively. The registration result determines the quantitative data of R2 and R2*; an oxygen metabolism parameter information calculation submodule is used to obtain the oxygen metabolism parameter information of the glioma patient through multi-parameter quantitative blood oxygen level dependent imaging according to the quantitative data of R2 and R2*, the cerebral blood volume, and the cerebral blood flow rate; a neovascularization parameter information calculation submodule is used to obtain the neovascularization parameter information of the glioma patient through a vascular structure construction method according to the surface diffusion coefficient and the cerebral blood volume, as well as the gradient echo DSC sequence and spin echo DSC sequence collected after the injection of the contrast agent; The data processing submodule includes: a target image acquisition unit, used to determine the target image converted by the gradient echo DSC sequence and the spin echo DSC sequence acquired after the injection of the contrast agent; a target image preprocessing unit, used to perform motion correction, skull removal and each layer acquisition time correction on the target image to obtain a processed target image; a change calculation unit, used to determine the relaxation rate change ΔR2(t) caused by the contrast agent corresponding to each pixel in the processed target image; a contrast agent leakage correction unit, used to calculate the relaxation rate change ΔR2(t) corresponding to each pixel. R2(t) is corrected for contrast agent leakage to obtain a corrected ΔR2(t); a fitting unit is used to fit the leakage-corrected ΔR2(t) to obtain a fitting result; a response unit is used to convert the ΔR2(t) corresponding to each pixel into a contrast agent concentration C(t), and deconvolute the contrast agent concentration C(t) corresponding to each pixel to obtain a tissue response function R(t); a cerebral blood volume and cerebral blood flow velocity calculation unit is used to calculate the cerebral blood volume and cerebral blood flow velocity according to the tissue response function R(t) and the contrast agent concentration C(t).

7. A computer device, characterized in that: The computer device comprises: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the visualization imaging method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the visualization imaging method according to any one of claims 1 to 5 is implemented.

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