Blood volume detection device and method based on brain angiography image

Abstract

The invention discloses a blood volume detection device and method based on a brain angiography image, and relates to the technical field of blood volume detection of the brain angiography image, the blood volume detection device comprises a detection device body and a substrate, the top surface of the substrate is fixedly provided with a lifting platform, and the surface of the top end of the lifting platform is fixedly provided with a workbench; and the linear motor is installed in the middle of the top face of the workbench, and a movable bed plate is horizontally and fixedly installed on the movable end face of the top of the linear motor. According to the blood volume detection device and method based on the brain angiography image, displacement compensation closed-loop control is achieved through the fine adjustment feedback module of the adjusting mechanism, and the gear meshing clearance compensation error is reduced; the LSTM network and the hybrid FEM-LBM algorithm are cooperatively optimized, the blood flow calculation speed is multiplied, an isolation telescopic sleeve shell and a shielding plate are arranged to form a composite shielding layer, the radiation leakage amount is reduced, the radiation monitoring module achieves real-time alarm of dose exceeding, and the protection level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of blood volume detection in cerebral angiography images, and particularly relates to a blood volume detection device and method based on cerebral angiography images. Background Art

[0002] The blood volume detection of cerebral angiography images is to capture the dynamic information of blood vessels by injecting a contrast agent and using imaging devices (such as DSA, CT, MRI), and combine mathematical models and algorithms to calculate the hemodynamic parameters of specific brain regions. Its core goal is to extract from the images: regional cerebral blood volume (rCBV): the total blood volume in unit brain tissue (unit: ml / 100g); mean transit time (MTT): the time required for blood to flow through a specific region (unit: s); cerebral blood flow (CBF): the blood flow volume per unit time (unit: ml / 100g / min).

[0003] The existing blood volume detection of cerebral angiography images has the following defects. Traditional detection beds rely on manual knobs to adjust the head angle, and the gear transmission gap leads to significant positioning errors, and the microvascular imaging overlap rate is relatively low; the rigid fixing device is likely to cause discomfort to patients, and the connection method between the mattress and the device results in a relatively high vibration transmission rate, affecting the quality of long-time scan images; the accuracy of image acquisition and registration is insufficient, and there are problems with multi-modal temporal coordination: the synchronization between DSA and CT scans is insufficient, and the registration error of the vascular displacement field is relatively large; the displacement compensation algorithm for patient physiological movements (such as breathing and heartbeat) is imperfect, resulting in significant deviations in the calculation of blood flow parameters; the hemodynamic model is overly simplified, and traditional methods assume that blood is a Newtonian fluid, ignoring the elastic deformation of blood vessel walls and the non-Newtonian characteristics of blood, and the estimation deviation of microcirculation blood volume is obvious; the existing algorithms do not fully compensate for the dynamic movement of blood vessel walls, and the integrity of the spatio-temporal fusion model is insufficient. Summary of the Invention

[0004] The main purpose of the present invention is to provide a blood volume detection device and method based on cerebral angiography images, which can effectively solve the problems in the background art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A blood volume detection device based on cerebral angiography images, including a detection device body and a substrate. A lifting platform is fixedly installed on the top surface of the substrate, and a workbench is fixedly installed on the top surface of the lifting platform;

[0007] A linear motor, which is installed in the middle of the top surface of the workbench, and a moving bed board is horizontally fixedly installed on the top moving end surface of the linear motor;

[0008] An adjustment mechanism and a head positioning assembly:

[0009] One end of the movable bedplate close to the detection device body is equipped with an adjustment mechanism, and the other end of the adjustment mechanism is equipped with a head plate;

[0010] A head pad is installed on the top of the head plate;

[0011] The adjustment mechanism includes a sliding groove, a sliding column, a rotating column and an adjustment gear. Two sliding grooves are opened at one end of the movable bedplate. Sliding columns are slidably installed in both of the two sliding grooves. The other ends of the two sliding columns are fixedly connected to one side surface of the head plate together;

[0012] The movable bedplate is rotationally and penetratingly installed with a rotating column. A number of teeth are equidistantly arranged at the bottoms of the two sliding columns. Two adjustment gears are fixedly sleeved on the rotating column. The two adjustment gears are respectively meshed with the teeth at the bottoms of the sliding columns. One end of the rotating column penetrates through the movable bedplate and is fixedly installed with an adjustment knob.

[0013] Preferably, a mattress is installed on the top of the movable bedplate. A pull handle is fixedly installed on one side surface of the mattress. A first clamping block is installed at the bottom of the mattress. A clamping groove adapted to the first clamping block is provided on the top of the movable bedplate; A second clamping block is installed at the bottom of the head pad. A limiting groove adapted to the second clamping block is provided on the top of the head plate; Clamping control buttons are provided on both the head plate and the movable bedplate. The first clamping block and the second clamping block are respectively adapted to the corresponding clamping control buttons.

[0014] Preferably, an isolation telescopic sleeve is installed between the workbench and the substrate. The lifting table is accommodated inside the isolation telescopic sleeve. The material of the isolation telescopic sleeve is a radiation-proof polyethylene composite layer (lead equivalent ≥ 0.5mmPb).

[0015] Preferably, a shielding plate is vertically and fixedly installed on the top surface of one end of the lifting table. The shielding plate is flush with the detection end port of the detection device body. The material of the shielding plate is boron carbide-tungsten alloy (neutron absorption rate ≥ 99%).

[0016] Preferably, the adjustment mechanism further includes a fine-tuning feedback module. The fine-tuning feedback module includes:

[0017] A Hall sensor, fixed on the side wall of the sliding column, for detecting the displacement (accuracy ±0.01mm);

[0018] A servo motor, linked with the adjustment knob, automatically compensating for the gear meshing clearance according to the Hall sensor data.

[0019] Preferably, the materials of the mattress and the head pad are multi-layer composite structures, including:

[0020] Surface layer: medical silicone layer (thickness 2mm, hardness 25 Shore A);

[0021] Middle layer: memory foam layer (density 50 kg / m 3 , rebound rate ≥ 90%);

[0022] Bottom layer: piezoelectric sensor array (resolution 1 kPa), used for real-time monitoring of the patient's pressure distribution.

[0023] Preferably, a radiation monitoring module is integrated on the outer surface of the isolation telescopic housing (3), including:

[0024] Geiger counter, used to detect the radiation dose in real time (range 0 - 10 mSv / h);

[0025] Buzzer alarm, which triggers an alarm when the radiation leakage amount ≥ 0.05 mSv / h.

[0026] A blood volume detection method for a blood volume detection device based on cerebral angiography images includes the following steps:

[0027] First step, dual-mode angiography synchronous scanning: Intravenous injection of core-shell nano-contrast agent (iodine shell thickness 2 nm, gadolinium inner core diameter 1 nm), and simultaneously start photon counting CT (M11) and electromagnetic drive DSA (M12) scanning, with a timing error < 5 ms;

[0028] Second step, four-dimensional space-time motion field construction: Based on the displacement data of the sliding column, compensate for the patient movement error, and calculate the blood flow velocity and the blood vessel wall movement velocity

[0029] Adopt the LSTM network to dynamically optimize the weight coefficient α to generate the blood vessel displacement field:

[0030]

[0031] Third step, microcirculation blood volume quantitative calculation:

[0032] Adopt the improved central volume method to calculate the regional blood volume (rCBV), and the formula is:

[0033]

[0034] Fourth step, the arterial input function C artery (t) is calibrated through data analysis, and the density ratio ρ tissue / ρ blood is dynamically calculated through the dual-energy CT value.

[0035] 3D visualization output: Generate a pseudo-color coded blood volume heat map (0 - 80 ml / 100g / min), and automatically divide the ischemic core area (rCBV < 2.0 ml / 100g) and the penumbra (2.0 < rCBV < 4.5 ml / 100g).

[0036] Preferably, in step 2, the time window of the LSTM network is set to 10 sampling points, and the number of neurons in the hidden layer is set to 128; in step 3, the blood flow simulation uses the hybrid FEM-LBM algorithm, the finite element mesh size ≤ 0.1 mm, and the relaxation time τ of the lattice Boltzmann method is 0.6.

[0037] Preferably, step 4 further includes dynamic feedback control. When it is detected that rCBV < 1.5 ml / 100g, the head plate is automatically retracted to a safe position by a servo motor; the support stiffness of the mattress is adjusted according to the data of the piezoelectric sensor, and the pressure balance error ≤ 5%.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. Displacement compensation closed-loop control is achieved through the fine-tuning feedback module of the adjustment mechanism, and the gear meshing clearance compensation error is reduced; the LSTM network and the hybrid FEM-LBM algorithm are synergistically optimized, and the blood flow calculation speed is doubled.

[0040] 2. By setting the isolation telescopic sleeve and the shielding plate to form a composite shielding layer, the radiation leakage amount is reduced, and the radiation monitoring module realizes real-time alarm for dose overrun, and the protection level is improved.

[0041] 3. The multi-layer composite mattress dynamically adjusts the support stiffness through piezoelectric sensors, improving the patient's comfort; the quick-release buckle mechanism supports rapid mattress replacement, enhancing the operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is the overall structural schematic diagram of a blood volume detection device based on cerebral angiography images of the present invention;

[0043] Figure 2 is the front view structural schematic diagram of a blood volume detection device based on cerebral angiography images of the present invention;

[0044] Figure 3 is the three-dimensional structural schematic diagram of the workbench of a blood volume detection device based on cerebral angiography images of the present invention;

[0045] Figure 4 is the three-dimensional structural schematic diagram of the lifting platform of a blood volume detection device based on cerebral angiography images of the present invention;

[0046] Figure 5Schematic three-dimensional structure diagram of the movable bed board of a blood volume detection device based on cerebral angiography images according to the present invention;

[0047] Figure 6 Schematic three-dimensional structure diagram of a blood volume detection device based on cerebral angiography images according to the present invention;

[0048] Figure 7 Schematic three-dimensional structure diagram of the mattress of a blood volume detection device based on cerebral angiography images according to the present invention;

[0049] Figure 8 Schematic three-dimensional structure diagram of the head pad of a blood volume detection device based on cerebral angiography images according to the present invention.

[0050] In the figure: 1, the detection device body; 2, the substrate; 3, the isolation telescopic housing; 4, the baffle; 5, the workbench; 6, the movable bed board; 7, the mattress; 71, the first head groove; 72, the first clamping block; 73, the pull handle; 8, the head pad; 81, the second head groove; 82, the second clamping block; 83, the buckle control button; 9, the adjustment mechanism; 91, the sliding groove; 92, the sliding column; 93, the rotating column; 94, the adjustment gear; 95, the adjustment knob; 96, the feedback module; 962, the servo motor; 10, the head plate, 11, the lifting platform. Detailed implementation manners

[0051] In order to make the technical means, creative features, achieved purposes and functions of the present invention easy to understand, the present invention will be further described below in conjunction with the specific implementation manners.

[0052] As Figure 1-8 shown, a blood volume detection device based on cerebral angiography images, including a detection device body 1 and a substrate 2, the top surface of the substrate 2 is fixedly installed with a lifting platform 11, and the top surface of the lifting platform 11 is fixedly installed with a workbench 5.

[0053] A linear motor 51, the linear motor 51 is installed in the middle of the top surface of the workbench 5, and the top moving end surface of the linear motor 51 is horizontally fixedly installed with a movable bed board 6.

[0054] An adjustment mechanism 9 and a head positioning assembly:

[0055] One end of the movable bed board 6 close to the detection device body 1 is installed with an adjustment mechanism 9, and the remaining end of the adjustment mechanism 9 is installed with a head plate 10.

[0056] The top of the head plate 10 is installed with a head pad 8.

[0057] The adjusting mechanism 9 includes a sliding groove 91, a sliding column 92, a rotating column 93 and an adjusting gear 94. Two sliding grooves 91 are formed at one end of the movable bed plate 6, and sliding columns 92 are slidably installed in both of the two sliding grooves 91. The remaining ends of the two sliding columns 92 are fixedly connected to one side surface of the head plate 10 together.

[0058] The movable bed plate 6 is rotatably penetrated and installed with a rotating column 93. A plurality of teeth are equidistantly arranged at the bottoms of the two sliding columns 92. Two adjusting gears 94 are fixedly sleeved on the rotating column 93. The two adjusting gears 94 are respectively meshed with the teeth at the bottoms of the sliding columns 92. One end of the rotating column 93 penetrates through the movable bed plate 6 and is fixedly installed with an adjusting knob 95.

[0059] In this embodiment, a mattress 7 is installed on the top of the movable bed plate 6. A pull handle 73 is fixedly installed on one side surface of the mattress 7. A first clamping block 72 is installed at the bottom of the mattress 7. A clamping groove adapted to the first clamping block 72 is provided on the top of the movable bed plate 6. A second clamping block 82 is installed at the bottom of the head cushion 8. A limiting groove adapted to the second clamping block 82 is provided on the top of the head plate 10. Clamping control buttons 83 are provided on both the head plate 10 and the movable bed plate 6. The first clamping block 72 and the second clamping block 82 are respectively adapted to the corresponding clamping control buttons 83.

[0060] In this embodiment, an isolation telescopic housing 3 is installed between the workbench 5 and the substrate 2. The lifting platform 11 is accommodated inside the isolation telescopic housing 3. The material of the isolation telescopic housing 3 is a radiation-proof polyethylene composite layer with a lead equivalent of ≥0.5mmPb.

[0061] In this embodiment, a shielding plate 4 is vertically and fixedly installed on the top surface of one end of the lifting platform 11. The shielding plate 4 is flush with the detection end port of the detection device body 1. The material of the shielding plate 4 is boron carbide-tungsten alloy with a neutron absorption rate of ≥99%. Closed-loop control and error compensation system, the fine-tuning feedback module 96 of the adjusting mechanism 9, uses a servo motor to drive a precision gear set, combines a Hall sensor to monitor the rack displacement in real time, and forms a closed-loop feedback control chain. The fine-tuning compensation algorithm is embedded in the FPGA chip, dynamically correcting the original error of the gear meshing clearance of ±1.5mm, and reducing the error after compensation to ±0.05mm.

[0062] Suitable for microvascular intervention scenarios, eliminating mechanical hysteresis during catheter pushing. It can adapt to the coordinated movement of the DSA catheter guiding arm and the CT scanning bed, ensuring the temporal consistency of multi-modal images.

[0063] The LSTM network collaborates with the hybrid FEM-LBM algorithm. The LSTM network extracts the temporal characteristics of the blood vessel wall movement and generates a displacement compensation weight coefficient α in the range of 0.2 - 0.8.

[0064] The hybrid FEM-LBM algorithm jointly solves the non-Newtonian fluid equations and the vascular elastic deformation model, increasing the blood flow calculation speed by 3 times.

[0065] In this embodiment, the adjustment mechanism 9 further includes a fine-tuning feedback module 96, and the fine-tuning feedback module 96 includes:

[0066] The Hall sensor 961 is fixed to the side wall of the sliding column 92 and is used to detect the displacement with an accuracy of ±0.01 mm.

[0067] The servo motor 962 is linked with the adjustment knob 95 to automatically compensate the gear meshing clearance according to the Hall sensor data.

[0068] In this embodiment, the material of the mattress 7 and the head pad 8 is a multi-layer composite structure, including:

[0069] Surface layer: Medical silicone layer thickness 2mm, hardness 25Shore A.

[0070] Middle layer: memory foam layer density 50kg / m 3 , rebound rate ≥90%.

[0071] Bottom layer: Piezoelectric sensor array with a resolution of 1kPa, used to monitor patient pressure distribution in real time.

[0072] In this embodiment, the outer surface of the isolation telescopic casing 3 is integrated with a radiation monitoring module, including:

[0073] Geiger counter, real-time detection of radiation dose range 0-10mSv / h.

[0074] Buzzer alarm, triggers alarm when radiation leakage ≥ 0.05mSv / h.

[0075] A blood volume detection method based on a blood volume detection device for cerebral angiography imaging comprises the following steps:

[0076] The first step is dual-mode imaging synchronous scanning: intravenous injection of core-shell nanocontrast agent with an iodine shell thickness of 2nm and a gadolinium core diameter of 1nm, synchronous start of photon counting CTM11 and electromagnetic drive DSAM12 scanning, with a timing error of <5ms.

[0077] The second step is to construct a four-dimensional space-time motion field: compensate for the patient's movement error based on the displacement data of the sliding column 92, and calculate the blood flow velocity by the optical flow method The velocity of blood vessel wall movement

[0078] The LSTM network is used to dynamically optimize the weight coefficient α to generate the vascular displacement field:

[0079]

[0080] Step 3, Quantitative calculation of microcirculation blood volume:

[0081] The improved central volume method is used to calculate the regional cerebral blood volume rCBV, and the formula is:

[0082]

[0083] Step 4, The arterial input function C artery (t) is calibrated through data analysis, and the density ratio ρ tissue / ρ blood is dynamically calculated through dual-energy CT values.

[0084] Three-dimensional visualization output: Generate a pseudo-color coded blood volume heat map of 0 - 80 ml / 100g / min, and automatically divide the ischemic core area with rCBV < 2.0 ml / 100g and the penumbra with 2.0 < rCBV < 4.5 ml / 100g.

[0085] In this embodiment, in step 2, the time window of the LSTM network is set to 10 sampling points, and the number of neurons in the hidden layer is set to 128. In step 3, the blood flow simulation uses the hybrid FEM-LBM algorithm, the finite element mesh size ≤ 0.1 mm, and the relaxation time τ of the lattice Boltzmann method is 0.6.

[0086] In this embodiment, step 4 also includes dynamic feedback control. When it is detected that rCBV < 1.5 ml / 100g, the head plate 10 is automatically retracted to a safe position by the servo motor. The support stiffness of the mattress 7 is adjusted according to the piezoelectric sensor data, and the pressure equilibrium error ≤ 5%.

[0087] The intelligent support system of the multi-layer composite mattress 7, the piezoelectric sensor array monitors the patient's pressure distribution, and the inflation volume of the air cushion unit is adjusted through the PID controller; the memory foam layer adapts to the physiological curvature of the spine, reduces muscle fatigue during long-term scanning, and the comfort score is increased by 90%. The quick-release buckle mechanism 83, using magnetic locks and slide rail guide grooves, shortens the mattress replacement time from 15 minutes to 30 seconds; supports the quick switching between pediatric special pads and obese patient reinforcement pads, and adapts to different clinical scenarios.

[0088] The core of the present invention lies in the technical closed-loop of multi-modal data fusion - dynamic feedback - closed-loop control, which is collaboratively realized through the following modules:

[0089] Precision mechanical positioning: The servo motor 9 drives the gear-rack transmission mechanism 10, combined with the displacement feedback of the Hall sensor 11, eliminates the gear meshing clearance error, and realizes sub-millimeter positioning of the head plate;

[0090] Intelligent image synchronization: The photon-counting CT and the electromagnetic-driven DSA are triggered for scanning through the spatio-temporal synchronization controller, and the LSTM network compensates for the patient's physiological movement to construct a four-dimensional vascular model;

[0091] Blood flow-mechanical linkage: The hybrid FEM-LBM algorithm calculates blood flow parameters, drives the servo-guided arm to adjust the catheter position, and the piezoelectric catheter tip feedbacks the contact force to achieve risk warning;

[0092] Human factors engineering adaptation: The multi-layer composite mattress 7 dynamically adjusts the support stiffness through piezoelectric sensors, and the quick-release buckle mechanism 83 supports quick replacement to adapt to different patients.

[0093] Workflow: After the patient lies down, the servo motor 962 drives the headboard Hall sensor according to the preset angle to monitor the displacement in real time and feedback it to the FPGA chip, and the fine-tuning module 96 compensates for the gear clearance error; The piezoelectric sensor array synchronously monitors the pressure distribution, and the PID controller adjusts the inflation volume of the air cushion unit to complete the body position adaptive locking.

[0094] The dual-channel high-pressure injection pump injects the core-shell nano-contrast agent, and the photon-counting CT and the electromagnetic-driven DSA are started for synchronous scanning; The spatio-temporal synchronization controller calibrates the timing error of the equipment, the LSTM network extracts the motion characteristics of the blood vessel wall, the optical flow method generates the blood flow velocity field, and outputs the registered four-dimensional blood vessel model.

[0095] The hybrid FEM-LBM algorithm calculates the regional cerebral blood volume rCBV and the wall shear stress WSS, and marks the boundary of the core infarction area and the penumbra; The path planning engine generates the intervention catheter path, avoids the functional area and calcified plaques, and the data is transmitted to the navigation interface in real time.

[0096] The piezoelectric catheter tip monitors the blood vessel contact force in real time. When the threshold is exceeded, it triggers an audible and visual alarm and pauses the servo motor 962; The FEM-LBM calculation unit updates the blood flow prediction model every 0.3 seconds, drives the servo-guided arm to fine-tune the catheter angle, and forms a "calculation-operation-feedback" closed loop.

[0097] The telescopic sheath 3 automatically shrinks and resets after the scan, and the radiation monitoring module generates a dose report; The quick-release buckle mechanism 83 unlocks the mattress 7, and it is replaced with a clean or special model within 30 seconds to prepare for the next examination.

[0098] The circuits, electronic components and control modules involved are all existing technologies, which can be fully realized by those skilled in the art without further elaboration. The content protected by the present invention does not involve improvements to software and methods.

[0099] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A blood volume detection device based on cerebral angiography imaging, characterized in that: A detection device body (1) and a substrate (2), wherein a lifting platform (11) is fixedly mounted on the top surface of the substrate (2), and a workbench (5) is fixedly mounted on the top surface of the lifting platform (11); A linear motor (51), the linear motor (51) being installed in the middle of the top surface of the workbench (5), and a movable bed plate (6) being fixedly installed horizontally on the top movable end surface of the linear motor (51); Adjustment mechanism (9) and head positioning assembly: An adjustment mechanism (9) is installed at one end of the movable bed plate (6) close to the detection device body (1), and a head plate (10) is installed at the remaining end of the adjustment mechanism (9); A head pad (8) is installed on the top of the head plate (10); The adjusting mechanism (9) comprises a sliding groove (91), a sliding column (92), a rotating column (93) and an adjusting gear (94); one end of the movable bed plate (6) is provided with two sliding grooves (91); sliding columns (92) are slidably installed in the two sliding grooves (91); the remaining ends of the two sliding columns (92) are fixedly connected to a side surface of the head plate (10); The movable bed board (6) is rotatably penetrated by a rotating column (93), the bottoms of the two sliding columns (92) are evenly spaced with a plurality of gear teeth, two adjusting gears (94) are fixedly sleeved on the rotating column (93), the two adjusting gears (94) are respectively meshed with the gear teeth at the bottom of the sliding column (92), and one end of the rotating column (93) penetrates the movable bed board (6) and is fixedly installed with an adjusting knob (95).

2. The blood volume detection device based on cerebral angiography according to claim 1, characterized in that: A mattress (7) is installed on the top of the movable bed board (6), a handle (73) is fixedly installed on one side surface of the mattress (7), a first clamping block (72) is installed on the bottom of the mattress (7), and a clamping slot adapted to the first clamping block (72) is provided on the top of the movable bed board (6); a second clamping block (82) is installed on the bottom of the head pad (8), and a limiting groove adapted to the second clamping block (82) is provided on the top of the head board (10); buckle control buttons (83) are provided on both the head board (10) and the movable bed board (6), and the first clamping block (72) and the second clamping block (82) are respectively adapted to the corresponding buckle control buttons (83).

3. The blood volume detection device based on cerebral angiography according to claim 1, characterized in that: An isolation telescopic casing (3) is installed between the workbench (5) and the base plate (2); the isolation telescopic casing (3) contains a lifting platform (11) inside; and the isolation telescopic casing (3) is made of a radiation-proof polyethylene composite layer.

4. The blood volume detection device based on cerebral angiography according to claim 1, characterized in that: A shielding plate (4) is vertically fixedly mounted on the top surface of one end of the lifting platform (11); the shielding plate (4) is flush with the detection end port of the detection device body (1); and the shielding plate (4) is made of boron carbide-tungsten alloy.

5. The blood volume detection device based on cerebral angiography according to claim 1, characterized in that: The regulating mechanism (9) further comprises a fine-tuning feedback module (96), wherein the fine-tuning feedback module (96) comprises: A Hall sensor (961) is fixed to the side wall of the sliding column (92) and is used to detect the displacement; The servo motor (962) is linked with the adjustment knob (95) to automatically compensate the gear meshing clearance according to the Hall sensor data.

6. The blood volume detection device based on cerebral angiography according to claim 2, characterized in that: The mattress (7) and the head pad (8) are made of a multi-layer composite structure, comprising: Surface layer: medical silicone layer; Middle layer: memory foam layer; Bottom layer: Piezoelectric sensor array for real-time monitoring of patient pressure distribution.

7. The blood volume detection device based on cerebral angiography according to claim 3, characterized in that: The outer surface of the isolation telescopic casing (3) is integrated with a radiation monitoring module, comprising: A Geiger counter (311) for real-time detection of radiation dose; The buzzer alarm (312) triggers an alarm when the radiation leakage amount is ≥0.05mSv / h.

8. A blood volume detection method based on the blood volume detection device of any one of claims 1 to 7, characterized in that The following steps are involved: The first step is dual-mode contrast simultaneous scanning: intravenous injection of core-shell nanocontrast agent, synchronous start of photon counting CT and electromagnetic driven DSA scanning; The second step is to construct a four-dimensional space-time motion field: based on the displacement data of the sliding column (92), the patient's movement error is compensated and the blood flow velocity is calculated by the optical flow method. The velocity of blood vessel wall movement The LSTM network is used to dynamically optimize the weight coefficient α to generate the vascular displacement field: The third step is to quantify the microcirculatory blood volume: The modified central volume method was used to calculate the regional blood volume (rCBV) using the following formula: Step 4: Arterial input function C artery (t) After data analysis and calibration, the density ratio ρ tissue / ρ blood Dynamic calculation through dual-energy CT value; 3D visualization output: Generate pseudo-color-coded blood volume heat map to automatically divide the ischemic core area and penumbra.

9. The blood volume detection method of the blood volume detection device based on cerebral angiography images according to claim 8, characterized in that: In step 2, the time window of the LSTM network is set to 10 sampling points, and the number of hidden layer neurons is set to 128; in step 3, the blood flow simulation adopts the hybrid FEM-LBM algorithm, the finite element grid size is ≤0.1mm, and the lattice Boltzmann method relaxation time τ=0.

6.

10. The blood volume detection method of the blood volume detection device based on cerebral angiography images according to claim 8, characterized in that: Step 4 also includes dynamic feedback control. When rCBV < 1.5 ml / 100 g is detected, the head plate (10) is automatically retracted to a safe position by a servo motor (962); the support stiffness of the mattress (7) is adjusted according to the piezoelectric sensor data, and the pressure balance error is ≤ 5%.

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