Early Alzheimer's disease tissue detection method and system based on terahertz time-domain spectroscopy

Abstract

The invention discloses a brain tissue detection method and system based on terahertz time-domain spectroscopy, and belongs to the technical field of biomedical engineering and terahertz spectrum detection. In-vitro brain tissue is taken as a research object, a brain tissue slice which is fixed and dehydrated and has a preset thickness is prepared, a terahertz time-domain spectroscopy system is used for acquiring a time-domain signal, and a refractive index and an absorption coefficient are extracted through fast Fourier transform and a parameter inversion model. By quantitatively analyzing the optical characteristic difference of normal and diseased brain tissues in a specific frequency band, the judgment on the pathological change related to the Alzheimer's disease is realized. Experiments show that in an effective spectral range, the refractive index and the absorption coefficient of diseased brain tissues are higher than those of normal brain tissues. The method has the characteristics of simplicity, no mark, non-ionizing radiation and the like, is highly sensitive to the microstructure and biochemical composition of the brain tissue, and provides a new physical detection means for early auxiliary analysis of the Alzheimer's disease.

Description

Technical Field

[0001] This invention relates to the field of biomedical photonics and terahertz spectroscopy detection technology, specifically to a method and system for measuring and analyzing the terahertz optical parameters of isolated brain tissue slices based on terahertz time-domain spectroscopy, thereby enabling the detection and auxiliary analysis of pathological features of Alzheimer's disease. Background Technology

[0002] Alzheimer's disease (AD) is an insidious, progressive neurodegenerative disease. Its earliest pathological feature is the abnormal accumulation and deposition of β-amyloid (Aβ) protein in the brain, further accompanied by tau protein hyperphosphorylation and the formation of neurofibrillary tangles (NFTs), ultimately leading to synaptic dysfunction, neuronal loss, and brain atrophy. Because the early pathological changes in AD are subtle and insidious, by the time clinical symptoms appear, neurological damage is usually irreversible. Therefore, developing an early, sensitive, and label-free diagnostic method is crucial for the intervention and treatment of AD.

[0003] Currently, clinical auxiliary diagnostic methods for Alzheimer's disease (AD) mainly include cerebrospinal fluid (CSF) biomarker detection, positron emission tomography (PET), and magnetic resonance imaging (MRI). While CSF testing offers high biochemical specificity, its invasive nature limits its application in large-scale screening. PET imaging, although capable of directly displaying amyloid deposition, is costly and requires the use of radioactive tracers. Conventional MRI technology has relatively limited sensitivity in detecting early microscopic pathological structural changes. Therefore, exploring novel detection technologies based on changes in the physical properties of biological tissues has become a research hotspot in the field of biomedical engineering.

[0004] Terahertz (THz) waves are electromagnetic waves with frequencies between 0.1 THz and 10 THz. Their photon energy is low (1 THz = 4.1 meV), and they do not cause ionizing damage to biological tissues, exhibiting good safety. Because the skeletal vibrational and rotational energy levels of biological macromolecules such as proteins and DNA are located in the terahertz band, terahertz spectroscopy is highly sensitive to conformational changes in biomolecules and the hydration state within tissues. In neurological disease research, the biochemical composition and microstructure of brain tissue in Alzheimer's disease (AD) patients are significantly altered, such as Aβ aggregation, abnormal tau protein modification, and lipid metabolism disorders. These changes directly affect the absorption and scattering characteristics of THz waves in brain tissue.

[0005] In 2009, Png et al. used terahertz time-domain spectroscopy (THz-TDS) to distinguish between protein-rich Alzheimer's disease (AD) tissue and healthy tissue in the human brain. They found a significant difference in the terahertz absorption coefficient between the lesion region and normal tissue, attributed to a collective response of the accumulation of multiple abnormal proteins. Similarly, Shi et al. compared the terahertz spectra of brain tissue from transgenic and wild-type Alzheimer's disease mice, observing that AD tissue exhibited absorption peaks associated with tryptophan torsional vibrational modes at specific frequencies (e.g., 1.44 THz, 1.8 THz, 2.11 THz).

[0006] However, current AD research based on THz-TDS technology still faces the following technical challenges: 1. Limited research subjects: Research primarily focuses on ex vivo dried and frozen brain tissue, cell models, or simulated peptides, with a lack of systematic spectral analysis of fresh brain slices. 2. Insufficient correlation studies: Research on the changing patterns of optical parameters in brain tissue during the AD pathological process, and the intrinsic correlation between optical parameters and pathological features, remains inadequate. 3. Lack of diagnostic criteria: Specific spectral biomarkers and quantitative discrimination criteria for AD detection have not yet been established, making it difficult to achieve stable quantitative differentiation between normal and Alzheimer's disease brain tissue within specific terahertz frequency bands.

[0007] Therefore, it is necessary to provide a detection method and system to achieve quantitative differentiation between normal and Alzheimer's disease brain tissue in a specific terahertz frequency band, providing new physical indicators for establishing a non-invasive, rapid, and early auxiliary diagnostic method for AD. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for detecting ex vivo brain tissue based on terahertz time-domain spectroscopy, aiming to solve the problems in the existing technology such as the lack of systematic spectral analysis of brain tissue slices, insufficient research on the correlation between optical parameters and pathological features, and the lack of specific spectral criteria, thereby achieving quantitative differentiation between normal and Alzheimer's disease brain tissue in a specific frequency band.

[0009] To address the aforementioned technical problems, this invention provides a brain tissue detection method based on terahertz time-domain spectroscopy, the method comprising the following steps: S1: Sample preparation: Obtain the isolated brain tissue of the subject to be tested, and after chemical fixation, dehydration and embedding, prepare coronal sections of brain tissue of a predetermined thickness and place the sections on a transparent substrate; S2: Reference signal acquisition: Acquire reference terahertz time-domain signal containing only a transparent substrate in transmission mode; S3: Sample signal acquisition: Acquire terahertz time-domain signals of samples containing isolated brain tissue slices under the same conditions; S4: Frequency Domain Transformation and Complex Transmission Coefficient Calculation: Perform Fast Fourier Transform on the reference signal and the sample signal to calculate the complex transmission coefficient; S5: Optical parameter inversion: Based on the Fresnel transmission model, the refractive index and absorption coefficient of the isolated brain tissue slices are inverted from the complex transmission coefficient; S6: Pathological feature determination: Select a preset characteristic frequency band, compare the refractive index spectrum and absorption coefficient spectrum of the isolated brain tissue to be tested with those of normal brain tissue in the same frequency band, and determine whether it has pathological features related to Alzheimer's disease based on the degree to which the parameters deviate from the normal threshold.

[0010] Furthermore, in step S5, the optical parameters are determined by the amplitude ratio corresponding to the complex transmission coefficient. and phase difference Obtained through mapping. Preferably, a complex refractive index is introduced. Based on the Fresnel transmission model, the refractive index is obtained by inversion from the complex transmission coefficient. and extinction coefficient And further calculate the absorption coefficient Specifically, for example, the refractive index spectrum and absorption coefficient spectrum of the brain tissue slices at different frequencies are calculated using the following relationship: in, The speed of light in a vacuum. Angular frequency, The thickness is the sample thickness. Thus, by introducing more dimensions of optical physical quantities, pathological tissues can be visualized from multiple physical perspectives, such as charge polarization and energy attenuation, improving the reliability of detection results and the stability of judgment, and avoiding the random errors that may exist with a single parameter.

[0011] Preferably, in step S1, the dehydration treatment involves immersing the chemically fixed brain tissue in an aqueous solution containing osmotic sugars or sugar alcohols for dehydration. The osmotic sugars or sugar alcohols include one or more of sucrose, mannitol, and sorbitol. Preferably, the dehydration treatment involves immersing the chemically fixed brain tissue in a sucrose solution for dehydration. Sucrose dehydration not only gently and uniformly removes interstitial water using osmotic pressure, but also, because it has no characteristic absorption in the terahertz frequency band, completely solves the shielding effect of water molecules on the spectrum of biological macromolecules, allowing the measured signal to directly point to pathological biochemical components.

[0012] Preferably, in step S1, the preset thickness is between 550 μm and 600 μm. This thickness range allows for the acquisition of detectable terahertz transmission signals and spatial resolution while ensuring the basic integrity of the brain tissue structure, thereby facilitating subsequent terahertz detection and analysis of early Alzheimer's disease brain tissue.

[0013] Preferably, in steps S2 and S3, the acquisition of the reference signal and the sample signal is carried out in a sealed environment with a relative humidity of no more than 10%, preferably no more than 5%, so as to reduce the influence of water vapor in the air on the absorption and scattering of terahertz waves on the detection results. When the relative humidity exceeds this range, the increased water vapor content in the air will significantly enhance the absorption and scattering of terahertz waves, thereby leading to increased signal attenuation, increased noise level, and affecting detection accuracy and repeatability.

[0014] Preferably, in step S6, the characteristic frequency band is 0.2THz-0.7THz. This frequency band covers the key regions of protein skeletal vibration and collective energy level transitions. By focusing on this frequency band, severe scattering interference in the high-frequency band and the diffraction limit problem in the low-frequency band are effectively avoided, thus improving detection efficiency and accuracy.

[0015] More preferably, the specific criteria for determining that the brain tissue slice has Alzheimer's disease-related pathological features are as follows: within the range of 0.4THz-0.7THz, the refractive index spectrum is generally higher than the normal threshold; and within the range of 0.2THz-0.6THz, the absorption coefficient spectrum is significantly higher than the normal threshold. This establishes a clear pattern of pathological response characteristics, namely, an overall increase in refractive index and a significant increase in absorption, transforming complex image observation into a clear comparison of physical indicators, thus providing an objective and quantifiable gold standard for the auxiliary determination of AD.

[0016] Meanwhile, the present invention also provides a detection system based on terahertz time-domain spectroscopy for implementing the above method, including a terahertz generation and detection unit for generating terahertz pulses and detecting sample signals; a sealed measurement cavity for containing the sample and maintaining a preset humidity environment; and a host computer for executing the brain tissue detection method based on terahertz time-domain spectroscopy as described in any one of claims 1 to 9.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Label-free, non-ionizing radiation: This method does not require fluorescent labels or radioactive tracers, uses low-energy terahertz waves, is safe for samples, and does not introduce additional chemical or radioactive interference.

[0018] 2. High sensitivity and quantification: By extracting quantitative optical parameters such as refractive index and absorption coefficient, it can sensitively capture protein deposition and microstructural changes in Alzheimer's disease brain tissue, and the judgment criteria are clear and quantifiable.

[0019] 3. Strong resistance to moisture interference: Through sucrose dehydration treatment and low-humidity sealed environment, the influence of moisture on terahertz spectrum is significantly reduced, so that the measured optical differences mainly come from changes in biochemical components and structure within the tissue.

[0020] 4. Good consistency of results: Under various slice thickness conditions, the differences in optical parameters between Alzheimer's disease brain tissue and normal brain tissue in the characteristic frequency band are consistent, which is conducive to establishing stable and reliable diagnostic criteria.

[0021] 5. Suitable for scientific research and auxiliary analysis applications: This invention provides a new physical detection method for the analysis of Alzheimer's disease model animal and human brain tissue samples, which can be used for pathological mechanism research and the development of early auxiliary analysis technology. Attached Figure Description

[0022] Figure 1 A schematic flowchart of a brain tissue detection method based on terahertz time-domain spectroscopy provided by the present invention; Figure 2 A schematic diagram of a detection system based on terahertz time-domain spectroscopy technology provided by the present invention; Figure 3 A comparison of terahertz optical parameters between a 550 μm thick normal brain slice and a brain slice from an Alzheimer's disease (AD) model mouse. Figure 3 The left image in the image is a comparison of refractive index spectra. Figure 3 The right figure in the image is a comparison of absorption coefficient spectra; Figure 4 A comparison of terahertz optical parameters between a 600 μm thick normal brain slice and a brain slice from an Alzheimer's disease (AD) model mouse. Figure 4 The left image in the image is a comparison of refractive index spectra. Figure 4 The right figure in the image is a comparison of absorption coefficient spectra.

[0023] Figure labeling: 1-Femtosecond laser; 2-Beam splitter; 3-Time delay line; 4-Terahertz emitter; 5-Terahertz receiver; 6-Sample; 7-Sealed measurement cavity; 8-Reflector; 9-Host computer; 10-Reflection mode beam splitter. Detailed Implementation

[0024] To better understand the technical solutions of the present invention, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings and experiments. Although exemplary embodiments of the present invention are shown in the drawings and experiments, it should be understood that those skilled in the art can implement the present invention in other forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0025] This invention provides a method for detecting isolated brain tissue based on terahertz time-domain spectroscopy. The basic concept is to perform terahertz time-domain spectroscopy on isolated brain tissue slices that have undergone specific dehydration treatment, and to obtain multiple quantitative optical parameters that can characterize the microscopic pathological changes of the tissue. By combining the parameter response characteristics within a specific frequency band, the identification of pathological features related to Alzheimer's disease can be achieved.

[0026] As shown in the attached instruction manual Figure 1 As shown, the method system implemented in this invention first involves a sample preparation step. This step, through the synergistic effect of chemical fixation and deep dehydration, reconstructs the ex vivo tissue into a standard medium suitable for terahertz wave detection. Chemical fixation, such as using formalin solution, locks the microscopic distribution of proteins and cellular structures within the brain tissue, preventing pathological features from degrading or shifting after ex vivo. The subsequent dehydration treatment, such as using a 30% sucrose solution, is of core technical significance; its physical essence is to remove water molecules from the tissue that have a strong absorption and shielding effect on terahertz waves. Because the broadband absorption generated by the dipole rotation of water molecules masks the skeletal vibrational signals of biomolecules, this treatment effectively allows the measured optical differences to truly reflect changes in endogenous components such as β-amyloid protein deposition and abnormal tau protein modification. The final coronal slices, at the hundreds of micrometers scale, establish a stable electromagnetic wave propagation path while ensuring the terahertz wave transmission signal-to-noise ratio.

[0027] After sample preparation, the process moves to signal acquisition. For example, differential measurement is used to extract electromagnetic modulation information from the tissue under a highly consistent optical path topology. This process begins by acquiring a reference signal containing only a transparent substrate, such as a quartz glass slide, defining the system's frequency response function, substrate reflection, and contributions from the measurement environment, such as residual water vapor, as the baseline response. Subsequently, a slice of the brain tissue to be tested is placed at the same location to capture pulse delay, amplitude attenuation, and waveform distortion caused by changes in the tissue's dielectric properties. To extract extremely weak pathological feature signals and suppress random noise, multiple consecutive pulse waveforms are averaged during acquisition, such as accumulating 10,000 waveform averages. This differential measurement ensures that the acquired time-domain signal contains complete information from low to high frequencies and also improves the system's measurement sensitivity.

[0028] Subsequently, the acquired raw time-domain data is quantitatively mapped through frequency-domain transformation and optical parameter inversion steps. The Fast Fourier Transform (FFT) is used to transform the time-domain pulses into a complex electric field distribution in the frequency domain, essentially analyzing the tissue's response characteristics at different frequency components. Based on the Fresnel transmission model, the calculated complex transmission coefficients are further decomposed into indicators that map the essential physical properties of the tissue. Among these, the refractive index microscopically maps the changes in the effective dielectric constant within the tissue, sensitively reflecting differences in local charge distribution caused by protein aggregation. The absorption coefficient reflects the resonant absorption and incoherent scattering loss of terahertz wave energy by pathological components. This inversion logic can accurately reflect the shift in the physical properties of brain tissue caused by Alzheimer's disease.

[0029] Finally, based on the optical parameters obtained from the inversion, the process proceeds to the result analysis and judgment step, thereby outputting the final detection result. The core logic of this step lies in identifying the specific dispersion and absorption patterns of terahertz waves exhibited by tissues in the pathological process of Alzheimer's disease. This invention does not perform a blind search across the entire spectrum, but focuses on characteristic frequency bands sensitive to conformational changes in pathological proteins, such as 0.2-0.7 THz, fully utilizing the high sensitivity of terahertz waves to collective vibrational modes of proteins within this frequency band. The judgment process is based on multi-dimensional joint criteria, including the overall upward trend of the refractive index spectral line and the significant increase of the absorption coefficient spectral line in a specific frequency band. When the physical indicators of the sample exceed the statistical threshold range established by the normal control group, the sample can be determined to have pathological characteristics related to Alzheimer's disease. This criterion based on quantitative physical parameters eliminates the interference of subjective experience and provides a robust detection tool for the study of the pathological mechanism of AD and early auxiliary analysis.

[0030] Example 1: Configuration of Terahertz Time-Domain Spectroscopy System This invention provides a detection system based on terahertz time-domain spectroscopy for implementing the above method, see the appendix to the specification. Figure 2 As shown, the system mainly consists of a terahertz generation and detection section, a sample carrying and environmental control section, and a data processing section.

[0031] The system uses a femtosecond laser 1 as its light source. In this embodiment, its center wavelength is preferably 1064 nm, the laser pulse width is less than 100 fs, and the repetition frequency is approximately 1000 Hz. The laser pulse emitted by the femtosecond laser 1 is split into two paths after passing through a beam splitter 2: one is a pump optical path used to excite the terahertz transmitter 4 to generate terahertz pulses; the other is a probe optical path, guided by a reflector 8 to a terahertz receiver 5 for coherent detection of terahertz signals. A time delay line 3 is configured in either the probe or pump optical path. By precisely changing the time delay between the two pulses, complete sampling of the terahertz pulse time-domain waveform is achieved. The terahertz transmitter and receiver are mounted on a one-dimensional optical track, generating and detecting terahertz pulses through femtosecond laser pumping. The entire system has a terahertz spectrum coverage of approximately 0.06-4 THz, a time scan range of 160 ps, ​​a time resolution of 0.1 ps, a frequency accuracy of approximately 1.5 GHz, and a signal-to-noise ratio of approximately 80 dB.

[0032] Regarding sample support and environmental control, the system is equipped with a sample stage and a scanning platform for fixing a transparent substrate, such as a quartz glass slide, on which the isolated brain tissue slice sample 6 is placed. The scanning platform has a large scanning stroke and high-precision stepping, enabling localized point measurements of sample 6. For example, it has a scanning stroke of 30cm × 30cm and a minimum stepping of no more than 25μm to achieve accurate detection results.

[0033] More preferably, the terahertz transmitter 4, the terahertz receiver 5, and the sample 6 are all housed within a sealed measurement chamber 7. This sealed measurement chamber 7 is connected to a dry air supply device, which controls the internal relative humidity to a preset range not exceeding 5% by filling the chamber with dry air or nitrogen. This sealed environment design, combined with the aforementioned sample dehydration treatment, can greatly eliminate the characteristic absorption interference of water vapor in the air on the terahertz waves.

[0034] Furthermore, the system can operate in different modes, and even switch between multiple modes. (See attached instruction manual.) Figure 2 As shown, the system can also operate in reflection mode, in which case a beam splitter 10 in reflection mode is configured. The beam splitter 10 in reflection mode guides the terahertz wave to the surface of sample 6 and receives the reflected signal. In transmission mode, the terahertz beam directly penetrates sample 6 and is received by the terahertz receiver 5 on the opposite side.

[0035] Furthermore, the system integrates control and signal processing through a host computer 9. The host computer 9 has a built-in data acquisition module responsible for synchronously controlling the scanning of the time delay line 3 and the signal acquisition of the terahertz receiver 5. The host computer 9 also undertakes core tasks such as performing fast Fourier transforms on time-domain signals, optical parameter inversion calculations, and pathological feature determination. Through dedicated analysis software running on the host computer 9, the refractive index spectrum and absorption coefficient spectrum of the brain tissue slices under test can be presented in real time, and the determination results can be output according to preset normal threshold ranges.

[0036] Example 2: Preparation and processing of isolated brain tissue sections This embodiment details the sample source and processing procedures used for experimental verification. First, approximately 8-month-old C57bl / 6 mice were selected as the normal control group, and age-matched 5×FAD transgenic mice were selected as the Alzheimer's disease (AD) model group. Each mouse weighed approximately 20 g. After euthanizing the mice in an ethically sound manner, the brain tissue was rapidly and completely removed and chemically fixed in a 10% neutral formalin solution to maintain the integrity of the tissue and cell structure.

[0037] After fixation, to eliminate moisture interference, some brain tissue samples were transferred to a 30% sucrose solution for dehydration for 24 hours. This step effectively reduced the water content in the tissue, and since sucrose has no significant characteristic absorption peak in the 0.2-0.7 THz range, it did not affect subsequent spectral analysis.

[0038] Dehydrated brain tissue was embedded in 10% low-melting-point agarose. After solidification, coronal sections of brain tissue with thicknesses of 550 μm and 600 μm were prepared using a vibratory microtome. After each section was prepared, it was laid flat on the surface of a 1 mm thick quartz glass slide, ensuring close contact between the section and the substrate to avoid air bubbles and wrinkles.

[0039] Example 3: Signal Acquisition and Optical Parameter Calculation 1. Reference signal acquisition A sample holder containing only a quartz glass slide is fixed to the sample stage, allowing the terahertz beam to pass through the area where the brain tissue slice will be placed. Reference time-domain signals are acquired via a host computer. To improve the signal-to-noise ratio, the continuously acquired pulses are averaged, for example, by averaging approximately 10,000 waveforms accumulated over about 1 minute. The resulting average waveform is used as the reference signal for that reference position.

[0040] 2. Sample signal acquisition Without altering the transmitter, receiver, and optical path configuration, the sample holder containing brain tissue slices was placed in the same location as the reference measurement, and the same acquisition and averaging strategies were employed to obtain the sample time-domain signal. The above measurement process was repeated for brain slices of different thicknesses and in different groups (normal vs. AD).

[0041] 3. Frequency Domain Transformation and Complex Transmission Coefficient Calculation Perform Fast Fourier Transform on the reference time-domain signal and the sample time-domain signal respectively to obtain the frequency-domain electric field. and Calculate the complex transmission coefficient: Among them, amplitude ratio and phase difference The value is obtained from the modulus and argument of the above ratio.

[0042] Optical parameter inversion Under the condition that the brain tissue slices are of uniform thickness and have parallel interfaces, complex refractive index is introduced. Based on the Fresnel transmission model, the refractive index is obtained by inversion from the complex transmission coefficient. and extinction coefficient And further calculate the absorption coefficient A parameter inversion algorithm based on Fresnel's formula is used to map the complex transmission coefficient to the refractive index. With absorption coefficient For example, the following relation can be used: in, The speed of light in a vacuum. Angular frequency, The sample thickness is given. Through the above calculations, the refractive index spectrum and absorption coefficient spectrum of brain tissue slices at different frequencies can be obtained.

[0043] Example 4: Result Analysis and Pathological Feature Determination This embodiment verifies the effectiveness of the method of the present invention by statistically comparing the spectral data of brain tissue slices from the normal control group and the AD model group. Optical parameter spectral lines under different thickness conditions were obtained experimentally and used as the basis for judgment.

[0044] The experimental results involved in this embodiment are as shown in the appendix to the instruction manual. Figure 3 and 4 As shown. The instruction manual is attached. Figure 3 This study presents a comparison of terahertz optical parameters between a 550 μm thick normal brain slice and a brain slice from an AD model mouse. (Instruction manual attached) Figure 3 The left graph shows the refractive index on the left (horizontal axis) and the refractive index on the right (vertical axis). The two curves in the graph show the refractive index evolution trends of the normal group and the AD group in the 0.2-0.7 THz frequency band, respectively. (See attached instruction manual.) Figure 3 The right-hand graph in the image also shows the frequency on the horizontal axis and the absorption coefficient on the vertical axis, which is used to show the difference in energy loss between the two groups of samples in this frequency band.

[0045] Instruction manual attached Figure 4 This demonstrates a comparison of terahertz optical parameters between a 600μm thick normal brain slice and a brain slice from an AD model mouse. (Instruction manual attached) Figure 4 The coordinate definitions and curve properties of the left and right graphs are as follows: Figure 3 The aim was to verify the stability of the differences in optical characteristics between the two groups of tissues by changing the variable of physical thickness.

[0046] By referring to the instruction manual Figure 3 and 4 Statistical comparative analysis revealed the following significant characteristics. First, regarding refractive index characteristics, within the approximately 0.4–0.7 THz frequency band, the refractive index spectrum of the AD model group brain tissue was generally higher than that of the normal control group, regardless of whether the thickness was 550 μm or 600 μm. From a physical mechanism perspective, this reflects that the deposition of β-amyloid (Aβ) plaques and the aggregation of abnormal proteins in AD tissue lead to an increase in tissue micro-density or an increase in effective dielectric constant, thereby producing a stronger phase delay for terahertz waves.

[0047] Secondly, regarding the absorption coefficient characteristics, in the frequency band of approximately 0.2-0.6 THz, the absorption coefficient spectrum of the brain tissue in the AD model group was significantly higher than that in the normal control group. This indicates that AD tissue exhibits stronger energy loss to terahertz waves. The reason for this is that abnormal protein modifications and aggregates during the AD pathological process have a higher absorption cross-section for terahertz waves, leading to increased attenuation of transmitted energy.

[0048] The experimental results above show that the differences in optical parameters between AD tissue and normal tissue within the characteristic frequency band exhibit good consistency under different thickness conditions, demonstrating the robustness and repeatability of the method of this invention. Based on this, this invention selects 0.2-0.7 THz as the core frequency band for feature determination, and sets normal threshold ranges for refractive index and absorption coefficient respectively.

[0049] When the refractive index of the brain tissue slice being tested is generally higher than the normal threshold in the range of 0.4-0.7 THz, and the absorption coefficient is significantly increased in the range of 0.2-0.6 THz, the brain tissue can be determined to have pathological features related to Alzheimer's disease.

[0050] As can be seen from this embodiment, the present invention can sensitively detect microscopic pathological changes inside brain tissue using quantitative physical indicators without relying on chemical labeling, providing an effective new tool for basic research and early auxiliary analysis of Alzheimer's disease.

[0051] The technical solutions of the present invention have been described in detail above with reference to specific embodiments. However, the above embodiments are only used to illustrate the technical concept of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or substitutions can be made to the technical solutions involved in the present invention without departing from the overall technical concept of the present invention. All equivalent substitutions, modifications, or improvements made to the present invention by those skilled in the art based on their understanding of the technical solutions of the present invention, as long as they do not depart from the technical concept and essence of the present invention, should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for detecting brain tissue based on terahertz time-domain spectroscopy, characterized in that, Includes the following steps: S1: Sample preparation, obtaining isolated brain tissue, and preparing brain tissue slices of a predetermined thickness after chemical fixation and dehydration. S2: Reference signal acquisition, acquiring a reference terahertz time-domain signal that does not contain the brain tissue slice; S3: Sample signal acquisition, acquiring the terahertz time-domain signal of the brain tissue slice sample under the same conditions; S4: Frequency domain transformation and complex transmission coefficient calculation: Perform frequency domain transformation on the reference signal and the sample signal to calculate the complex transmission coefficient; S5: Optical parameter inversion: Based on the electromagnetic wave propagation model, the optical parameters of the brain tissue slice are inverted from the complex transmission coefficient. The optical parameters include the refractive index spectrum and the absorption coefficient spectrum. S6: Pathological feature determination: Within a preset characteristic frequency band, compare the refractive index spectrum, the absorption coefficient spectrum, and the corresponding normal threshold benchmark to determine whether the brain tissue slice has pathological features related to Alzheimer's disease.

2. The detection method according to claim 1, characterized in that, In step S5, the optical parameters are obtained by the amplitude ratio ρ(ω) and phase difference corresponding to the complex transmission coefficient. Obtained through mapping.

3. The detection method according to claim 2, characterized in that, In step S5, the complex refractive index is introduced. Based on the Fresnel transmission model, the refractive index is obtained by inversion from the complex transmission coefficient. and extinction coefficient And further calculate the absorption coefficient .

4. The detection method according to claim 3, characterized in that, In step S5, the refractive index spectrum and absorption coefficient spectrum of the brain tissue slices at different frequencies are calculated using the following formula: in, The speed of light in a vacuum. Angular frequency, The sample thickness.

5. The detection method according to claim 1, characterized in that, In step S1, the dehydration process involves immersing the chemically fixed brain tissue in an aqueous solution containing osmotic sugars or sugar alcohols for dehydration. The osmotic sugars or sugar alcohols include one or more of sucrose, mannitol, and sorbitol.

6. The detection method according to any one of claims 1-5, characterized in that, In step S1, the preset thickness is 550μm to 600μm.

7. The detection method according to any one of claims 1-5, characterized in that, In steps S2 and S3, the acquisition of the reference signal and the sample signal is carried out in a sealed environment with a relative humidity of no more than 10%.

8. The detection method according to any one of claims 1-5, characterized in that, In step S6, the characteristic frequency band is 0.2THz–0.7THz.

9. The detection method according to claim 8, characterized in that, The specific criteria for determining that the brain tissue slices have Alzheimer's disease-related pathological features are as follows: Within the range of 0.4 THz–0.7 THz, the refractive index spectrum is generally higher than the normal threshold reference. Furthermore, within the range of 0.2THz–0.6THz, the absorption coefficient spectrum is significantly higher than the normal threshold reference.

10. A detection system based on terahertz time-domain spectroscopy, characterized in that, include: Terahertz generation and detection unit, used to generate terahertz pulses and detect sample signals; A sealed measurement chamber is used to contain the sample and maintain a preset humidity environment; A host computer is used to execute the brain tissue detection method based on terahertz time-domain spectroscopy as described in any one of claims 1 to 9.

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