Diagnostic method for alzheimer's disease using pet-ct images and device therefor

Abstract

The Alzheimer's disease diagnosis method using PET-CT images according to the present application can include: a process of generating a standard brain CT template in MNI (Montreal Neurological Institute) space from a CT image calculated by a PET-CT device; a process of calculating a whole cortex volume of interest (VOI) of a plurality of sub-regions in which the deposition of beta amyloid is above a certain value in a cortex ROI (cortex ROI) region based on the above standard brain CT template; and a process of calculating a percentage unit of each of the above plurality of sub-regions based on the amyloid deposition rate (Standardized uptake value ratio, SUVR) of each of the above plurality of sub-regions. 18 F-florbetaben (FBB) 18 F-florbetaben (FBB) 18 F-flutemetamol (FMM) 18 F-flutemetamol (FMM)

Description

Technical Field

[0001] This invention relates to a method and apparatus for diagnosing Alzheimer's disease (AD) based on centiloid units, and more specifically, to a method and apparatus for diagnosing Alzheimer's disease using PET-CT images as the output of a PET-CT scanner. Background Technology

[0002] Recently, with the aging population, the number of people suffering from cognitive impairment caused by Alzheimer's disease is increasing. Most of the Alzheimer's treatments developed to date are cholinesterase inhibitors (ChEIs), with NMDA receptor antagonists also available; however, these treatments have limitations in their effectiveness in the early stages of the disease.

[0003] In addition, proteins considered to be causative agents of Alzheimer's disease include tau protein and β-amyloid (also known as "Aβ" or "amyloid"). The accumulation of these pathogenic proteins in the brain can indicate the progression of Alzheimer's disease and predict cognitive decline; therefore, these pathogenic proteins can serve as biomarkers for Alzheimer's disease.

[0004] To slow the progression of Alzheimer's disease, early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease is needed, and the importance of identifying these pathogenic proteins as biomarkers is increasing.

[0005] In order to identify β-amyloid protein, the results of various tools used to assess cognitive function, such as neuropsychological factors, genetic factors, aging demographic factors, and brain imaging tools such as magnetic resonance imaging (MRI), computer tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), and PET-CT, can be used as analytical variables. Moreover, the tools for identifying β-amyloid protein are becoming increasingly diversified.

[0006] In this regard, the existing Korean Patent No. 10-2019-0067477 (Method and apparatus for determining the pathogenesis of Alzheimer's dementia using amyloid PET imaging) discloses the following process: obtaining data on the amyloid deposition rate in the brain and analyzing the deposition rate; dividing patients into one of several preset groups based on the amyloid deposition rate in the subcortical structures of the brain and inferring the pathogenesis; however, this does not take into account the different degrees of amyloid deposition in each patient's brain region, and has the limitation of quantitative value fluctuation due to differences in the analysis methods of amyloid PET or the differences in the radiopharmaceuticals used for diagnosis.

[0007] Therefore, in order to determine the degree of amyloid deposition, it is necessary to consider a percentage system as standardized information that can reduce the fluctuation of quantitative values. When considering a percentage system, it is necessary to consider the method of deposition degree according to the subdivided areas of the brain. Summary of the Invention

[0008] Technical issues

[0009] To address the aforementioned problems, the present invention provides a method and apparatus for diagnosing Alzheimer's disease using PET-CT images, which aims to provide a method and apparatus for diagnosing Alzheimer's disease by calculating percentage units of each subdivided brain region using only images from a PET-CT device without performing an MRI examination, and by using the calculated percentage units of each subdivided brain region.

[0010] Technical solution

[0011] To achieve the above objectives, an embodiment of the present invention provides a diagnostic method for Alzheimer's disease using PET-CT images, which may include: generating a standard brain CT template in the MNI (Montreal Neurological Institute) space based on CT images calculated by a PET-CT device; and calculating, based on the standard brain CT template, within a cortical ROI (cortex ROI) region where β-amyloid protein deposition is above a certain value. 18 F-Fluorbitalban ( 18 F-fl orbetaben, FBB) and 18 F-Flumethasone (F-Flumethasone) 18The process of calculating the whole cortex volume of interest (VOI) of multiple sub-regions that can be used together by F-flutemetamol (FMM); and the process of calculating the percentage units of each of the above sub-regions based on the whole cortex volume and the standardized uptake value ratio (SUVR) of each of the above sub-regions.

[0012] According to an embodiment, the aforementioned multiple subdivided regions may include two or more of the following: the frontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula and cingulum.

[0013] According to an embodiment, the above-mentioned PET-CT images can be used 18 F-florbetaben (FBB) or 18 One of F-flutemetamol (FMM) is used as a radiopharmaceutical tracer for calculation.

[0014] According to an embodiment, the aforementioned percentage unit can be calculated by subtracting a second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of a first pre-calculated value.

[0015] According to an embodiment, the PET-CT image is a product of a β-amyloid PET device, which may include PET images and CT images.

[0016] Furthermore, an embodiment of the Alzheimer's disease diagnostic device utilizing PET-CT images may include: a template generation unit that generates a standard brain CT template in the MNI space based on CT images calculated by the PET-CT device; and a subdivision region identification unit that, based on the aforementioned standard brain CT template, calculates within a cortical ROI region where β-amyloid protein deposition is above a certain value. 18 F-florbetaben (FBB) and 18 The F-flutemetamol (FMM) unit can be used to calculate the overall cortical volume of multiple sub-regions; and the percentage unit calculates the percentage units of each of the multiple sub-regions based on the overall cortical volume and amyloid deposition rate of each of the multiple sub-regions.

[0017] According to an embodiment, the aforementioned multiple subdivided regions may include two or more of the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula.

[0018] According to an embodiment, the above-mentioned PET-CT images can be used 18 F-florbetaben (FBB) or 18 One of F-flutemetamol (FMM) is used as a radiopharmaceutical tracer for calculation.

[0019] According to an embodiment, the aforementioned percentage unit can be calculated by subtracting a second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of a first pre-calculated value.

[0020] According to an embodiment, the PET-CT image is a product of a β-amyloid PET device, which may include PET images and CT images.

[0021] The effects of the invention

[0022] As an embodiment of the present invention, the diagnostic method and apparatus for Alzheimer's disease using PET-CT images can also diagnose Alzheimer's disease caused by β-amyloid in patients who cannot undergo MRI examination.

[0023] Furthermore, the diagnostic method and apparatus for Alzheimer's disease using PET-CT images provided as an embodiment of the present invention do not utilize MRI examination results, thus reducing the cost and time required for diagnosing Alzheimer's disease.

[0024] Furthermore, the diagnostic method and apparatus for Alzheimer's disease using PET-CT images provided as an embodiment of the present invention, because it is based on percentage units of each subdivided brain region, can take into account the different degrees of amyloid protein deposition in each patient's brain region, thus achieving a high diagnostic accuracy for Alzheimer's disease. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a diagnostic method for Alzheimer's disease using PET-CT images according to an embodiment of the present invention.

[0026] Figure 2 This is a structural diagram illustrating a diagnostic device for Alzheimer's disease using PET-CT images according to an embodiment of the present invention.

[0027] Figure 3 A flowchart for verifying an embodiment of the present invention of a diagnostic method and apparatus for Alzheimer's disease using PET-CT images.

[0028] Figure 4 A graph illustrating the correlation between dcSUVRs and dcCL of global CTX VOI using an MR-based method and global CTX VOI using a CT-based method according to an embodiment of the present invention.

[0029] Figure 5 This is a graph illustrating the correlation analysis of seven subdivided regions in percentage units between a CT-based method and an MR-based method, according to an embodiment of the present invention.

[0030] Figure 6 This is a graph illustrating the linear correlation between percentage units obtained by using a standard global cortical target VOI and dcCL obtained by a CT-based method in an embodiment of the present invention.

[0031] Figure 7 This is a map illustrating the linear correlation between percentage units obtained by using a standard global cortical target VOI and dcCL obtained by an MR-based method, in an embodiment of the present invention.

[0032] Figure 8 This is a graph illustrating the linear relationship between the SUVR and percentage units of the FBB applied in seven subdivision regions according to an embodiment of the present invention.

[0033] Figure 9 This is a graph illustrating the linear relationship between the SUVR and percentage units of an FMM applied across seven subdivided regions according to an embodiment of the present invention. Detailed Implementation

[0034] To achieve the above objectives, one embodiment of the present invention provides a method for diagnosing Alzheimer's disease using PET-CT images, which may include: generating a standard brain CT template in the MNI space based on CT images calculated by a PET-CT device; and calculating, based on the standard brain CT template, within a cortical ROI region where β-amyloid protein deposition is above a certain value. 18 F-florbetab en (FBB) and 18 The process of using F-flutemetamol (FMM) to measure the overall cortical volume of multiple sub-regions that can be used together; and the process of calculating the percentage units of each of the multiple sub-regions based on the overall cortical volume and amyloid deposition rate of each of the multiple sub-regions.

[0035] According to an embodiment, the aforementioned multiple subdivided regions may include two or more of the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula.

[0036] According to an embodiment, the above-mentioned PET-CT images can be used18 F-florbetaben (FBB) or 18 One of F-flutemetamol (FMM) is used as a radiopharmaceutical tracer for calculation.

[0037] According to an embodiment, the aforementioned percentage unit can be calculated by subtracting a second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of a first pre-calculated value.

[0038] According to an embodiment, the PET-CT image is a product of a β-amyloid PET device, which may include PET images and CT images.

[0039] Furthermore, an embodiment of the Alzheimer's disease diagnostic device utilizing PET-CT images may include: a template generation unit that generates a standard brain CT template in the MNI space based on CT images calculated by the PET-CT device; and a subdivision region identification unit that, based on the aforementioned standard brain CT template, calculates within a cortical ROI region where β-amyloid protein deposition is above a certain value. 18 F-florbetaben (FBB) and 18 The F-flutemetamol (FMM) unit can be used to calculate the overall cortical volume of multiple sub-regions; and the percentage unit calculates the percentage units of each of the multiple sub-regions based on the overall cortical volume and amyloid deposition rate of each of the multiple sub-regions.

[0040] According to an embodiment, the aforementioned multiple subdivided regions may include two or more of the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula.

[0041] According to an embodiment, the above-mentioned PET-CT images can be used 18 F-florbetaben (FBB) or 18 One of F-flutemetamol (FMM) is used as a radiopharmaceutical tracer for calculation.

[0042] According to an embodiment, the aforementioned percentage unit can be calculated by subtracting a second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of a first pre-calculated value.

[0043] According to an embodiment, the PET-CT image is a product of a β-amyloid PET device, which may include PET images and CT images.

[0044] Methods for implementing the invention

[0045] The following is a brief explanation of the terminology used in this specification, which serves as a specific description of the structure and function of the preferred embodiments of the present invention for the purpose of implementing the invention.

[0046] The terminology used in this specification has been selected from the most widely used and common terms possible while taking into account the function of the invention, but this may depend on the intent or precedent of those skilled in the art, the emergence of new technologies, etc. Furthermore, in certain cases, terms arbitrarily chosen by the applicant may also be used; in such cases, their meanings will be detailed in the description of the corresponding invention. Therefore, the terminology used in this invention should be defined according to its meaning and based on the entire content of the invention, rather than simply its name.

[0047] Throughout the specification, when a part is referred to as "including" a component, it means that other components may be included, not excluded, unless specifically stated otherwise. Furthermore, the terms "...part," "module," etc., used in the specification refer to a unit that performs at least one function or action, which may be implemented by hardware or software, or by a combination of hardware and software. Also, throughout the specification, when a part is referred to as "connected" to another part, this includes not only direct connections but also connections where "other structures are spaced between them."

[0048] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, enabling those skilled in the art to readily implement the embodiments of the present invention. However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. Furthermore, in the accompanying drawings, parts unrelated to the description have been omitted for clarity, and similar reference numerals have been used for similar parts throughout the specification.

[0049] Proteins considered to be causative agents of Alzheimer's disease include tau protein and β-amyloid protein. The progression of Alzheimer's disease can be assessed by observing the accumulation of these proteins in the brain; therefore, these proteins can serve as biomarkers for Alzheimer's disease. To identify β-amyloid protein, i.e., to determine its accumulation in the brain, brain imaging devices such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and PET-CT (or scanners) can be used.

[0050] The products or outputs of an MRI device can be called MRI images, the products or outputs of a CT device can be called CT images, the products or outputs of a PET device can be called PET images, and the products or outputs of a PET-CT device can be called PET-CT images.

[0051] A PET-CT device combines the functions of a PET scanner and a CT scanner, generating both morphological (CT) and functional (PET) images of a disease from a single image. It can also separate CT and PET images for individual generation and output. PET-CT devices are also sometimes called Fusion-CT. The CT scanner used in a PET-CT device can be a low-dose CT scanner, resulting in images and content that differ from conventional CT images. While conventional CT scanners can identify structural changes in the human body, PET-CT can determine the state of biochemical changes preceding these structural changes. Therefore, it is suitable for diagnosing Alzheimer's disease caused by the accumulation of β-amyloid protein.

[0052] Typically, PET devices are used to measure the accumulation of β-amyloid protein in vivo. PET devices are used as important biomarkers for diagnosing Alzheimer's disease, and amyloid PET devices can be used to measure the presence or amount of β-amyloid protein deposited in the brain. Amyloid PET devices measure substances bound to amyloid protein and radioisotopes (carbon radioisotopes (C)). 11 ) or fluorine isotopes (F 18 The combination was used to confirm the presence of amyloid protein deposited in the brain by injecting the patient.

[0053] Furthermore, previously, PET-CT images were obtained from a PET-CT scanner to diagnose Alzheimer's disease. The correlation between the two analytical methods was verified by comparing a standard brain CT template generated in the MNI space with an MNI template generated based on MRI images. Specifically, previously, a percentage system could be used to diagnose Alzheimer's disease. To obtain a more accurate percentage system, brain MRI images of the patient were required in addition to PET-CT images. Alzheimer's patients needed to undergo two examinations: one using an MRI scanner and the other using a PET scanner.

[0054] However, MRI devices have the following drawbacks: long imaging time, difficulty in performing on patients with claustrophobia, and even with very small amounts of metal artificial teeth, spinal complements, or other metallic materials, they can hinder diagnosis and interfere with the operation of cochlear implants or bulb pacemakers. Ultimately, MRI devices cannot be used to take MRI images in all patients. Therefore, it is necessary to diagnose Alzheimer's disease based solely on anatomical PET-CT images obtained from PET-CT devices or to determine percentage units for diagnosing Alzheimer's disease.

[0055] In one embodiment of the present invention, a method and apparatus for diagnosing Alzheimer's disease using PET-CT images are provided. Instead of MRI imaging, the cerebral cortex is divided into subdivided regions using only a single PET-CT scan, and the percentage units of each subdivided region are calculated. This allows for the diagnosis of Alzheimer's disease and reduces costs. Furthermore, for patients who are difficult to diagnose using MRI, biomarkers serving as percentage units can be used to diagnose Alzheimer's disease.

[0056] Hereinafter, specific embodiments of the present invention will be described with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart illustrating a method for diagnosing Alzheimer's disease using PET-CT images according to an embodiment of the present invention. Figure 2 This is a structural diagram illustrating a diagnostic device for Alzheimer's disease using PET-CT images according to an embodiment of the present invention.

[0058] Figure 1 Alzheimer's disease diagnostic methods through Figure 2 The Alzheimer's disease diagnostic device is implemented, therefore, it is also explained Figure 1 and Figure 2 .

[0059] Reference Figure 1 The communication unit 211 of the Alzheimer's disease diagnostic device 210, which utilizes PET-CT images, can receive brain images from an external device 220 (step S210). According to an embodiment, the external device 220 can be a brain imaging device, such as a magnetic resonance imaging device, a computed tomography (CT) device, a positron emission tomography (PET) device, or a PET-CT device (or scanner), preferably a PET-CT device (or an amyloid PET-CT device or an amyloid PET).

[0060] According to an embodiment, the brain imaging can be PET-CT images. The external device 220 and the Alzheimer's disease diagnostic device 210 using PET-CT images can directly transmit and receive brain images via wired or wireless communication, or the external device 220 and the Alzheimer's disease diagnostic device 210 using PET-CT images can indirectly transmit and receive brain images through a hospital internal system that is connected to them respectively.

[0061] The communication unit 211 can transmit brain images received from the external device 220 to the control unit 212. The control unit 212 can use the brain images to identify the cortical regions of the brain into multiple sub-regions. For each identified cortical sub-region, the partial deposition of amyloid protein can be calculated using a percentage unit as a standardized value.

[0062] For amyloid PET, quantitative values ​​fluctuate due to differences in analytical methods or the radiopharmaceuticals used for tracking. Therefore, a standardization method is needed. Control unit 212 calculates a centiloid (CL) as a standardized score based on amyloid, using a standard that sets the mean of amyloid-negative subjects to 0 and the mean of patients with typical Alzheimer's disease to 100. In other words, a centiloid can refer to a unit that, regardless of the results obtained from any amyloid PET, sets the mean score of identified amyloid-negative subjects to 0 and the mean score of patients with typical Alzheimer's dementia to 100.

[0063] According to an embodiment, the control unit 212 can generate or identify amyloid PET-CT images of two radiopharmaceutical tracers based on received brain imaging, and can calculate the percentage units of each subdivided region of the amyloid PET-CT image of the two radiopharmaceutical tracers. According to an embodiment, the radiopharmaceutical tracer is an amyloid PET tracer; for example, the two radiopharmaceutical tracers can be… 18 F-florbetaben (FBB) and 18 F-flute metamol (FMM).

[0064] According to an embodiment, the control unit 212 can calculate a quantitative value in percentage units from the cortical region of the brain where β-amyloid protein is deposited primarily in amyloid PET images.

[0065] According to the embodiment, since the uptake of β-amyloid protein in each patient's brain region is different, the control unit 212 subdivides it and calculates the percentage units of each subdivided region for diagnosing Alzheimer's disease. The region is divided within the cortical region of the brain, and the uptake of β-amyloid protein in a certain region of Alzheimer's disease patient can be calculated as a standardized percentage unit using the percentage units of each subdivided region in the PET images of Alzheimer's disease patients.

[0066] According to the embodiment, the control unit 212 can use the whole cerebellum as a reference region, calculate the SUVR and percentage units of each sub-region, and receive and use the mathematical formulas for calculating the SUVR and percentage units of each sub-region from the storage unit 214. The amount of amyloid protein deposited in amyloid PET images can be calculated by relatively quantifying the amount of amyloid protein in the region to be analyzed based on regions without amyloid protein. In this case, the region of the brain without amyloid protein is called the reference region, and setting an appropriate reference region is necessary for accurate quantification. Typically, cerebellar regions can be used as reference regions to correct the region to be analyzed in amyloid PET images of Alzheimer's disease patients. However, amyloid protein deposition has also been observed in the cerebellum of patients with hereditary Alzheimer's disease, prion diseases, etc., therefore, it is not suitable as a reference region in diseases other than Alzheimer's disease.

[0067] According to an example, SUVR can indicate the standardized uptake value ratio of PiB. PiB (Pittsburgh compound B, C-11) is a β-amyloid protein tracer, a radiopharmaceutical used in amyloid PET. Amyloid protein deposited in the brain binds to PiB to increase the uptake of the radiopharmaceutical, thereby confirming the degree of amyloid protein deposition.

[0068] According to an embodiment, the control unit 212 can generate a standard brain CT template located in the MNI space based on the calculated MNI template from CT images collected from the normal group, and perform CT-guided spatial normalization on PET-CT images of typical Alzheimer's disease patients and young control groups to calculate the whole cortex ROI with high β-amyloid deposition.

[0069] According to an embodiment, in amyloid PET images, the control unit 212 specifies seven subdivided regions of the cortex within the cortical region of high β-amyloid uptake by intersecting with the AAL atlas.

[0070] According to an embodiment, the control unit 212 can identify seven subdivided regions within cortical regions of high β-amyloid deposition or uptake based on amyloid PET-CT images as the intersection result with an Automated Anatomical Labeling (AAL) atlas (also referred to as an "AAL atlas"). For example, the seven subdivided regions can be the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula.

[0071] According to an embodiment, CT-guided spatial normalization may include a normalization correction process. Figure 3 This document details the setup and methods for the control group used to determine the accumulation of amyloid protein.

[0072] Referring to Table 1, the control unit 212 can calculate the SUVR and percentage units for each subdivided region, and can derive a mathematical formula for direct conversion of percentage units or receive and use a mathematical formula for calculating SUVR and percentage units from the storage unit 214.

[0073] Table 1

[0074]

[0075] According to an embodiment, the control unit 212 can also perform final verification with percentage units calculated using MRI images. Figure 3 A flowchart illustrating a method and apparatus for diagnosing Alzheimer's disease using PET-CT images, according to an embodiment of the present invention. (Refer to...) Figure 3 In order to calculate the percentage units based on PET-CT images, the participants in the experiment were first grouped using the PET-CT image diagnostic device for Alzheimer's disease, and the PET-CT images could be ensured by the PET-CT device (step S310).

[0076] At this time, participants in the experiment can consist of patients from young controls (YC), old controls (OC), mild cognitive impairment (MCI), subcortical vascular dementia, and Alzheimer's disease dementia (ADD). Participants can undergo examinations using an amyloid PET device employing two radiopharmaceutical tracers (FBB and FMM) and T1-weighted magnetic resonance imaging (MRI). To determine the overall cortical volume of amyloid accumulation based on the definitions of the two radiopharmaceutical tracers, participants can include ADD patients who are positive for amyloid PET with both radiopharmaceutical tracers and OC patients who are negative for amyloid PET with both radiopharmaceutical tracers.

[0077] Subsequently, the diagnostic device for Alzheimer's disease using PET-CT images can generate percentage units using the secured PET-CT images.

[0078] According to an embodiment, an Alzheimer's disease diagnostic device utilizing PET-CT images can register individual PET-CT images along with the MRI images, following a standard percentage unit process based on SPM (Minimum Particular Number). According to the embodiment, spatial normalization can then be performed using the transformation parameters of the SPM8 comprehensive segmentation method for T1-weighted MRI.

[0079] According to an embodiment, the diagnostic device for Alzheimer's disease using PET-CT images can utilize standard centiloid global cortical target volume of interest (CTX VOI) and a whole cerebellum (WC) mask. The whole cerebellum can be used as a benchmark region in the quantitative analysis of FBB and FMM PET images.

[0080] According to an embodiment, the diagnostic device for Alzheimer's disease using PET-CT images can generate MR-based FBB-FMM global CTX VOIs (step S320) in Alzheimer's disease patients (AD-CTX) and elderly control groups (OC-CTX) using spatially normalized FBB and FMM PET images.

[0081] According to an embodiment, in an Alzheimer's disease diagnostic device utilizing PET-CT images, in order to generate the FBB-FMM global CTX VOI, a mask is generated based on the difference between AD-CTX and OC-CTX. Ultimately, two masks, FBB and FMM PET, can be generated.

[0082] According to an embodiment, in an Alzheimer's disease diagnostic device utilizing PET-CT images, two masks are crossed, and the thresholds of the crossed masks are determined, thereby generating an FBB-FMM global CTX VOI with common regions of amyloid accumulation in the brain associated with Alzheimer's disease, using both FBB and FMM PET trackers. According to an embodiment, the Alzheimer's disease diagnostic device utilizing PET-CT images uses both the MR-based FBB-FMM CTX VOI and the standard CTX VOI to calculate individual dcSUVR and SUVR standard values.

[0083] According to an embodiment, in the diagnostic device for Alzheimer's disease using PET-CT images, a brain CT template can be constructed. After generating a co-registration of PET and CT images, a standard brain CT template for CT-guided spatial normalization can be generated. The diagnostic device for Alzheimer's disease using PET-CT images utilizes Honsfield Unit (HU) correction of brain tissue to prepare a brain PET-CT template from PET-CT images.

[0084] According to the embodiments, the orientation of the PET-CT image is changed, or the brightness is adjusted, or Gaussian smoothing (e.g., Gaussian smoothing 8mm) is applied to generate a brain CT template.

[0085] According to an embodiment, in a diagnostic device for Alzheimer's disease using PET-CT images, individual T1 MR images can be spatially normalized in the MNI space, and the spatial normalization parameters of the T1 MR images can be applied to the HU-corrected CT images. The HU-corrected CT images can be flipped to generate symmetrical templates.

[0086] According to an embodiment, an Alzheimer's disease diagnostic device using PET-CT images can generate a CT-based FBB-FMM global CTX VOI (step S330). The device can perform HU correction processing on individual CT images. The respective PET images of the FBB and FMM can be co-registered in the corresponding HU-corrected CT image, which can be spatially normalized using normalization parameters for each HU-corrected CT image in the MNI space through the generated brain CT template. According to an embodiment, the device can generate a global CTX VOI (“FBB-FMM global CTX VOI”) for FBB-FMM using the normalized PET images of the FBB and FMM, in the same manner described above for the MR-based FBB-FMM method. Furthermore, individual dcSUVR and SUVR criteria can be calculated using the CT-based global FBB-FMM CTX VOI and the standard CTX VOI, respectively. The SUVR standard can refer to the SUVR calculated using the CTX VOI generated by PiB in Klunk's paper (2015, The cent iloid project), and can be a different CTX than the CTX calculated according to dcCL. The FBB-FMM global CTX VOI is applicable to the method published in Eur J Nucl Med Mol Imaging 2020 Jul; 47(8):1938-1948. doi:10.1007 / s00259-019-04596-x.Epub 2019 Dec 13, in which ADCI and OC are used, but the difference of this invention is that it has typical ADD and OC, and calculates the FBB-FMM global CTX VOI using CT images in the absence of MRI images.

[0087] According to the embodiment, referring to Table 2, in the diagnostic device for Alzheimer's disease using PET-CT images, the previously calculated FBB-FMM global CT X VOI (FBB-FMM global CTX VOI) can be used to calculate the percentage units.

[0088] Table 2

[0089]

[0090] Applying CT-based FBB-FMM global CTX VOI to FBB and FMM PET to obtain dcSUVR allows for the validation of the validity of CT-based percentage units using MR-based methods.

[0091] Mathematical Formula 1

[0092] CL = 100 × (SUVR) ind -SUVR YC-0 ) / (SUVR ADD-100 -SUVR YC-0 )

[0093] Formula 1 can be used to calculate percentage units (direct comparison of FBB-FMM CL, dcCL). Where, SUVR ind This represents the individual SUVR values ​​for participants in YC-0 and ADD-100. YC-0 and SUVR ADD-100 This represents the average SUVR value for each group.

[0094] According to an embodiment, the Alzheimer's disease diagnostic device using PET-CT images can calculate the percentage units of subdivided regions by the difference between previously generated FBB-FMM global CTX VOIs (step S340). The subdivided region VOI can be defined by the intersection of generated FBB-FMM global CTX VOIs showing high amyloid deposition in both FBB and FMM PET and AAL atlases. The entire sub-region intersecting with the FBB-FMM global CTX VOI can be used, which may include seven subdivided regions: the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula.

[0095] For CT-based and MR-based methods, the subdivision region dcSUVR and subdivision region dcCL values ​​can be calculated using the dcCL conversion equation for the seven subdivision regions. The conversion equation for calculating the percentage units of the seven subdivision regions can be derived from the subdivision region dcSUvR and dcCL.

[0096] According to an embodiment, the diagnostic device for Alzheimer's disease using PET-CT images can be validated using a percentage-based calculation method based on MR subdivision regions.

[0097] Linear regression of the correlation can be performed between CT-based and MR-based methods for FBB and FMM PET. (See reference...) Figure 4 To assess the reliability of the global CTX VOI generated by the CT-based method for FBB-FMM, a correlation was confirmed between dcSUVRs and dcCL using the global CTX VOI generated by the MR-based method and the global CTX VOI generated by the CT-based method.

[0098] Reference Figure 5The overall scale is expanded to percentage units for subdivided regions. Our primary goal is to validate the method for calculating percentage units for CT-based subdivided regions. Therefore, it can be interpreted that the correlation analysis for percentage units of 7 subdivided regions is performed between the CT-based method and the MR-based method.

[0099] Reference Figure 6 The linear equations between dcSUVR and dcCL for FBB using CT images (Y = 171.33X - 160.93) and between dcSUVR and dcCL for FMM using CT images (Y = 168.26X - 154.001) can be confirmed.

[0100] Based on the linear relationship between dcSUVR and dcCL of FBB and FMM using the above CT images, dcCL can be calculated from dcSUVR.

[0101] Reference Figure 7 This confirms the comparison results between the percentage scores according to the standard percentage unit method of FBB and FMM and the percentage score according to the dcCL method (X-axis: standard percentage unit method, Y-axis: dcCL method). The standard percentage unit method does not have subdivided regions; therefore, it is only compared as a global CTX VOI.

[0102] Reference Figure 8 Confirm the linear relationship between the SUVR and percentage units applicable to the FBB for the 7 sub-regions, referring to Figure 9 This confirms the linear relationship between SUVR and percentage units applicable to the FMM for the seven sub-regions. Given the dcSUVR obtained using the percentage unit VOI mask for each sub-region, its dcSUVR can be used as input to calculate the percentage unit score for each sub-region using the linear formula for each sub-region.

[0103] Furthermore, according to an embodiment of the present invention, a computer-readable recording medium may be provided containing a program for executing the aforementioned methods in a computer. In other words, the aforementioned methods can be prepared as a program executable on a computer and implemented in a general-purpose digital computer running the aforementioned program using a computer-readable medium. Moreover, the structure of the data used in the aforementioned methods can be recorded in a computer-readable medium by various means. The recording medium containing executable computer programs or code for executing the various methods of the present invention should not be construed as including temporary objects such as carrier waves or signals. The aforementioned computer-readable medium may include magnetic storage media (e.g., read-only memory, floppy disk, hard disk, etc.) and optically readable media (e.g., optical disc, DVD, etc.).

[0104] The present invention has been described in detail above through representative embodiments. However, those skilled in the art will understand that various modifications can be made to the above embodiments without departing from the scope of the present invention. Therefore, the scope of the present invention is not limited to the described embodiments, but is defined by the scope of the claims and all changes or modifications derived from equivalent concepts.

Claims

1. The use of the template generation unit, the subdivision region recognition unit, and the percentage unit calculation unit in the manufacture of an apparatus for use in a diagnostic method for Alzheimer's disease utilizing PET-CT images, characterized in that, The method includes: The process of generating a standard brain CT template in the MNI space using the template generation unit based on CT images calculated by the PET-CT device; Using the aforementioned subdivided region identification unit, within the cortical ROI region identified based on β-amyloid deposition derived from the aforementioned standard brain CT template, calculations are performed. 18 F-Fluorbital and 18 F-flumetamor can be used in conjunction with multiple sub-regions to process the overall cortical volume; and The process of using the percentage unit calculation unit to calculate the percentage units of each of the aforementioned subdivided regions based on the overall cortical volume and amyloid deposition rate of each of the aforementioned subdivided regions. The aforementioned sub-regions include two or more of the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula. The cortical ROI regions mentioned above were obtained through CT-guided spatial normalization calculations of PET-CT images from Alzheimer's disease patients and control groups, and the overall cortical volume of the aforementioned subdivided regions was calculated under normalization. 18 F-fluoridetaban PET imaging and normalization 18 Both F-fluorometholone PET images show β-amyloid protein deposition.

2. The use according to claim 1, characterized in that, The aforementioned percentage units are calculated by subtracting the second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of the first pre-calculated value.

3. The use according to claim 1, characterized in that, PET-CT images are products of the β-amyloid PET device, which include PET images and CT images.

4. A diagnostic device for Alzheimer's disease using PET-CT images, characterized in that, include: The template generation unit generates a standard brain CT template in the MNI space based on the CT images calculated by the PET-CT device. The subdivision region identification unit calculates within the cortical ROI region based on the β-amyloid deposition identification derived from the aforementioned standard brain CT template. 18 F-Fluorbital and 18 F-Flumethasone can be used in combination to cover the overall cortical volume of multiple sub-regions; as well as The percentage unit calculation department calculates the percentage units for each of the aforementioned subdivided regions based on the overall cortical volume and amyloid deposition rate. The aforementioned sub-regions include two or more of the prefrontal cortex, temporal cortex, parietal cortex, occipital cortex, posterior cingulate cortex, striatum, and insula. The cortical ROI regions mentioned above were obtained through CT-guided spatial normalization calculations of PET-CT images from Alzheimer's disease patients and control groups, and the overall cortical volume of the aforementioned subdivided regions was calculated under normalization. 18 F-fluoridetaban PET imaging and normalization 18 Both F-fluorometholone PET images show β-amyloid protein deposition.

5. The diagnostic device for Alzheimer's disease using PET-CT images according to claim 4, characterized in that, The aforementioned percentage units are calculated by subtracting the second pre-calculated value from the result of multiplying the aforementioned amyloid deposition rate by a real multiple of the first pre-calculated value.

6. The diagnostic device for Alzheimer's disease using PET-CT images according to claim 4, characterized in that, PET-CT images are products of the β-amyloid PET device, which include PET images and CT images.

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