A method for evaluating cognitive level based on images

Abstract

The present invention provides a method for evaluating cognitive level based on images, belonging to the technical field of cognitive level detection, and comprising the following steps: Step S1, acquiring brain image data of a patient through MRI; Step S2, measuring the included angle of the fourth ventricle using an image processing software tool; Step S3, based on the included angle value of the fourth ventricle measured in Step S2, mapping the included angle value of the fourth ventricle to the corresponding cognitive level score through a scoring calculation formula; Step S4, judging the cognitive function according to the cognitive level score. The present invention adopts the above-mentioned method for evaluating cognitive level based on images, avoiding complex post-processing steps. By directly measuring the included angle and mapping it to a preset scoring system, it aims to simplify the imaging evaluation, enabling clinicians to quickly and objectively evaluate the cognitive state, as an effective supplement or alternative to traditional cognitive scales.

Description

Technical Field

[0001] The present invention relates to the technical field of cognitive level detection, and more particularly to an imaging-based cognitive level assessment method. Background Art

[0002] In clinical practice, the assessment of cognitive level mainly relies on cognitive function scales, such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). These cognitive function scales evaluate cognitive function through the subjective responses of patients and the scoring by doctors. Although they are convenient to use, their results are often affected by subjective factors, which may lead to insufficient accuracy of the assessment results. Especially in the screening of early cognitive function decline, the sensitivity and specificity of the scales are relatively low.

[0003] In recent years, studies have confirmed the potential relationship between ventricular morphological changes and cognitive function. Changes in ventricular morphology, especially ventricular dilation or atrophy, may be related to the decline of cognitive function. In addition, the changes in complex brain network connectivity and fiber tracts have gradually become an important area of cognitive impairment research. By tracking and quantifying brain fiber tracts through techniques such as diffusion tensor imaging (DTI), the connectivity changes between different regions of the brain can be revealed, and these changes are crucial for understanding the decline of cognitive function. However, these methods usually involve a large number of post-processing steps and complex image analysis algorithms, which not only require a high level of professional knowledge but also greatly increase the workload and time cost in clinical applications. Therefore, these imaging-based assessment methods are difficult to be widely used in clinical practice.

[0004] To solve this problem, there is an urgent need to develop a method that can quickly and accurately assess an individual's cognitive level. Summary of the Invention

[0005] The purpose of the present invention is to provide an imaging-based cognitive level assessment method, which avoids complex post-processing steps. By directly measuring the included angle and mapping it to a preset scoring system, it aims to simplify the imaging assessment, enabling clinicians to quickly and objectively assess the cognitive state, as an effective supplement or alternative to traditional cognitive scales.

[0006] To achieve the above purpose, the present invention provides an imaging-based cognitive level assessment method, including the following steps:

[0007] Step S1: Obtain the brain image data of the patient through MRI;

[0008] Step S2: Measure the included angle of the fourth ventricle using an image processing software tool;

[0009] Step S3: Based on the measured fourth ventricle angle value in Step S2, map the fourth ventricle angle value to the corresponding cognitive level score through a scoring calculation formula;

[0010] Step S4: Judge the cognitive function according to the cognitive level score.

[0011] Preferably, in Step S1, the data type of the brain image data is 3D-T1 sagittal sequence image.

[0012] Preferably, in Step S2, use an imaging software tool to measure the angle of the fourth ventricle. The specific operation is as follows:

[0013] S21: Load the brain image data into the image processing software;

[0014] S22: Manually select the image slices where the long axis and short axis of the fourth ventricle are located;

[0015] S23: Use the image processing software to calibrate the long axis and short axis, and automatically calculate the angle between the long axis and short axis.

[0016] Preferably, in Step S3, the scoring calculation formula is as follows:

[0017] ;

[0018] Wherein, is the standardized cognitive score, and the value range is [0, 1]; is the angle of the fourth ventricle, and the value range is [10, 90].

[0019] Therefore, the present invention adopts the above-mentioned imaging-based cognitive level evaluation method, and the beneficial technical effects are as follows:

[0020] (1) The existing imaging evaluation methods rely on complex post-processing steps, such as brain network analysis, fiber tract tracing, and brain volume measurement, etc., which involve high-level professional knowledge and complex image analysis algorithms, and require a large amount of time for processing. The present invention directly generates a standardized cognitive score by measuring the angle of the fourth ventricle, reducing the time and workload of clinicians in image data processing, and the operation is simple and easy;

[0021] (2) The present invention does not require complex image segmentation or brain region division, and can quickly measure the angle of the fourth ventricle and generate a cognitive score by using conventional image processing software. This enables clinicians to complete the evaluation in a short time, especially in the screening of early cognitive dysfunction, and can improve the screening efficiency;

[0022] (3) The present invention adopts a standardized scoring system from 0 to 1, which is convenient for use in different clinical scenarios or automated systems. By mapping the included angle value to the scoring range from 0 to 1, it not only improves the intuitiveness of the evaluation, but also provides a convenient data format for subsequent analysis such as machine learning and statistical models;

[0023] (4) The scoring system of the present invention has been verified based on two datasets of different scales, namely 200 people and 2000 people respectively, verifying the applicability of the angle range. The scoring system can be applied to different types of patient groups and has flexible scalability, capable of adapting to more clinical data and application scenarios in the future;

[0024] (5) The present invention adopts the method of directly generating scores from image data, avoiding the subjectivity of traditional cognitive function scales, and the evaluation results are more objective. In addition, the scoring criteria are clear and easy to repeat, with high reliability and consistency, and are not easily interfered by human factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a framework diagram of a method for evaluating cognitive level based on images according to the present invention;

[0026] Figure 2 is a schematic diagram of angle measurement; wherein, Figure 2 (A) in is the mid-sagittal T1-weighted magnetic resonance image of a healthy control; Figure 2 (B) in is the same MR image depicting and measuring FVRA; Figure 2 (C) in is the mid-sagittal T1-weighted magnetic resonance image of an MCI patient; Figure 2 (D) in is the same MR image depicting and measuring FVRA;

[0027] Figure 3 is a scatter plot of FVRA Score and MMSE Score;

[0028] Figure 4 is a schematic ROC diagram. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following further details the embodiments of the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application. The examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0030] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0031] Similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0032] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is customarily placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0033] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0034] Embodiment 1

[0035] As Figure 1 shown, the present invention provides a method for evaluating cognitive level based on images, including the following steps:

[0036] Step S1: Obtain brain image data of a patient through Magnetic Resonance Imaging (MRI);

[0037] The data type of the brain image data is 3D-T1 sagittal sequence images.

[0038] Step S2: Use an image processing software (such as ITK-SNAP or ImageJ, etc.) tool to measure the angle of the fourth ventricle, specifically including the following steps:

[0039] Step 1: Load the brain image data into the image processing software and manually select the image slices where the long axis and short axis of the fourth ventricle are located;

[0040] Step 2: Use image processing software to calibrate the major axis and minor axis, and automatically calculate the angle between the major axis and the minor axis.

[0041] Step S3: Based on the fourth ventricle angle value measured in Step S2, map the fourth ventricle angle value to the corresponding cognitive level score through a scoring calculation formula;

[0042] The scoring calculation formula is as follows:

[0043] ;

[0044] Where, is the standardized cognitive score, and its value range is [0, 1]; is the angle of the fourth ventricle, and its value range is [10, 90].

[0045] The angle range selection of the present invention is based on the actual results of clinical data, and the angle range is verified through two data sets. Among them, Data Set 1 contains 200 people, and Data Set 2 contains 2000 people. The angle ranges of the two data sets are both between 10° and 90°.

[0046] In order to cope with possible boundary situations of different data sets, an elastic boundary is introduced, allowing cases where the angle is less than 10° or exceeds 90°. For example:

[0047] When the angle is less than 10°, it is mapped to 1 point, indicating the best cognitive function.

[0048] When the angle is greater than 90°, it is mapped to 0 point, indicating the worst cognitive function.

[0049] The introduction of the elastic boundary can ensure that the scoring system is still effective in extreme cases and there will be no situation where it cannot be evaluated.

[0050] Step S4: Judge the cognitive function according to the cognitive level score.

[0051] The present invention will be further described below through a specific example.

[0052] MRI data of the subjects were collected on a Philips 3.0T magnetic resonance scanner (Ingenia, Philips Healthcare, Best, The Netherlands) using a 32-channel head coil. Through the measurement of the fourth ventricle angle ( Figure 2 ), score conversion was performed, and a correlation analysis was carried out with the MMSE score.

[0053] The experimental results show that the FVRA score (of the present invention) is significantly positively correlated with the MMSE score, specifically as follows:

[0054] Correlation coefficient r = 0.6894, statistic t = 7.9564, p The value is 3.5133×10 −12 (p < 0.05), indicating a significant positive correlation between FVRA score and MMSE ( Figure 3 ), and as the FVRA score increases, the MMSE score also increases. This indicates the consistency between FVRA score and MMSE in evaluating cognitive level.

[0055] To further evaluate the diagnostic ability of FVRA score, a comparative analysis was conducted on FVRA score and other indicators (such as gray matter volume, white matter volume, cortical thickness). Through ROC (Receiver Operating Characteristic) curve analysis, the results showed that ( Figure 4 ) FVRA score performed better than traditional imaging indicators such as gray matter volume, white matter volume, and cortical thickness in the diagnosis of cognitive dysfunction.

[0056] ROC analysis showed that FVRA score had higher diagnostic accuracy and sensitivity in distinguishing patients with normal cognitive function and those with declining cognitive level. This further supported the effectiveness of FVRA score as a simple imaging-based cognitive assessment tool.

[0057] Therefore, the present invention adopts the above-mentioned imaging-based cognitive level assessment method, avoiding complex post-processing steps. By directly measuring the included angle and mapping it to a preset scoring system, it aims to simplify the imaging assessment, enabling clinicians to quickly and objectively evaluate the cognitive state as an effective supplement or alternative to traditional cognitive scales.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements do not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An image-based cognitive level assessment method, characterized in that, It includes the following steps: Step S1: Obtain the brain image data of the patient through MRI; Step S2: Use an image processing software tool to measure the angle of the fourth ventricle; Step S3: Based on the angle value of the fourth ventricle measured in Step S2, map the angle value of the fourth ventricle to the corresponding cognitive level score through a scoring calculation formula; Step S4: Judge the cognitive function according to the cognitive level score; In Step S2, when using the image software tool to measure the angle of the fourth ventricle, the specific operation is as follows: S21: Load the brain image data into the image processing software; S22: Manually select the image slices where the long axis and short axis of the fourth ventricle are located; S23: Use the image processing software to calibrate the long axis and short axis, and automatically calculate the angle between the long axis and short axis; In Step S3, the scoring calculation formula is as follows: Where S is the standardized cognitive score, and its value range is [0, 1]; θ is the angle of the fourth ventricle, and its value range is [10, 90]; When the angle is less than 10°, it is mapped to 1 point, indicating the best cognitive function; When the angle is greater than 90°, it is mapped to 0 point, indicating the worst cognitive function.

2. The method for evaluating cognitive level based on images according to claim 1, wherein In Step S1, the data type of the brain image data is 3D-T1 sagittal sequence image.

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