CN115089159A - System for realizing transcranial magnetic precise stimulation based on functional nuclear magnetic resonance - Google Patents
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Abstract
The invention provides a system for realizing transcranial magnetic precise stimulation based on functional nuclear magnetic resonance, which comprises: the brain area activity analysis module is used for analyzing an area with abnormal activity in the brain area of the treated person according to the functional magnetic resonance data of the treated person; the position determining module is used for determining the position of the abnormal-activity area in the brain area of the treated person in the head of the patient according to the abnormal-activity area in the brain area analyzed by the brain area activity analyzing module and by combining the structural magnetic resonance data of the treated person; and the stimulation module is used for performing magnetic stimulation on the position determined by the position determination module.
Description
Technical Field
The invention relates to the field of electromagnetic and physical therapy, in particular to a system for realizing transcranial magnetic precise stimulation based on functional nuclear magnetic resonance.
Background
Magnetic resonance functional imaging (fMRI) generally refers to a magnetic resonance imaging (BOLD-fMRI) technique that analyzes brain activity using a blood oxygen level-dependent (BOLD) phenomenon. The blood oxygen level dependent phenomenon refers to that different binding states of hemoglobin and oxygen in blood have different magnetism. Hemoglobin exhibits diamagnetism when bound to oxygen (oxyhemoglobin), and paramagnetism when unbound to oxygen (deoxyhemoglobin). When the local deoxyhemoglobin is decreased, the T2 weighted signal is increased on fMRI images, and BOLD-fMRI images are roughly divided into three types, Task-based fMRI (tfMRI), Resting-State fMRI (R-fMRI) and Natural Stimulus fMRI (N-fMRI).
Transcranial Magnetic Stimulation (TMS) is a safe and noninvasive central stimulation technology, and the principle of the technology is that a pulsed magnetic field acts on the central nervous system without attenuation to change the membrane potential of cortical nerve cells, so that induced current is generated, thereby changing the metabolism and the neuroelectric activity in the brain [ Lefaucher J, Aleman A, Baeken C, et al. instant-based guidelines on the therapeutic use of a reliable transcriptional stimulation (rTMS): an update (2014-2018) [ J ]. Clin neurological, 2020,131(2): 474-528). Repetitive transcranial magnetic stimulation (rTMS) was developed based on TMS, which refers to outputting more than two regularly repeated magnetic stimuli at a time. rTMS is divided into high frequency (> 1Hz) and low frequency (< 1Hz), the high frequency can increase the excitability of the cortex, the low frequency can decrease the excitability of the cortex [ Lefauucheur J, Aleman A, Baeken C, et al. Evan-based peptides on the therapeutic use of a predictive transcriptional stimulation (rTMS): an update (2014-2018) [ J. Clin Neurophysiol,2020,131(2):474-528 ]. The influence of Yi Mingyu, Luo Jing, Hu Xi, et al, high-frequency repeated transcranial magnetic stimulation on cognitive dysfunction after cerebral apoplexy%. Chenyijie and the like [ Chenyijie, radix rubiae, Zuowei, et al, curative effects of low-frequency repeated transcranial magnetic stimulation with different intensities on motor functions and cognitive dysfunction of patients with cerebral apoplexy [% J Sichuan medicine [ J ].2019,40: 657-. Wangshi goose and the like [ Wangshi goose, Scubaceae, Chengwei, et al, influence of repeated transcranial magnetic stimulation with different frequencies on cognitive impairment after stroke% J China physical medical science and rehabilitation journal [ J ].2021,43:721 and 723.] contrast the influence of rTMS with different frequencies on PSCI, and prompt that both low-frequency and high-frequency rTMS can effectively improve the cognitive function of a PSCI patient, and the two have equivalent curative effects, so that the rTMS stimulation parameters can be reasonably selected according to the condition of the patient in clinical treatment.
In the application scenario of transcranial magnetic stimulation, the selection of the stimulation site is mainly determined by analyzing the general distribution of functional brain regions of the human, depending on the anatomical knowledge possessed by the operator. The error between such a roughly determined stimulation point and the actual effective site is often large, so that during the actual stimulation process, the operator often needs to move the stimulation coil back and forth in the target region repeatedly many times in order to find the optimal stimulation point. Even after such repeated seeking process, the accuracy of stimulation cannot be ensured.
Disclosure of Invention
The invention mainly aims to provide a functional nuclear magnetic resonance-based transcranial magnetic precise stimulation system, which can accurately acquire the position of a brain region of a patient needing stimulation and perform stimulation.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a system for achieving transcranial magnetic precise stimulation based on functional nuclear magnetic resonance is characterized by comprising:
the brain region activity analysis module is used for analyzing an abnormal activity region in the brain region of the treated person according to the functional magnetic resonance data of the treated person;
the position determining module is used for determining the position of the abnormal-activity area in the brain area of the treated person in the head of the patient according to the abnormal-activity area in the brain area analyzed by the brain area activity analyzing module and by combining the structural magnetic resonance data of the treated person;
and the stimulation module is used for performing magnetic stimulation on the position determined by the position determination module.
Preferably, the brain region activity analysis module comprises a calculation module and a judgment module, the calculation module is used for calculating the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude of each brain region of the treated person through a resting state functional magnetic resonance pipeline type data processing tool in the brain image standardization platform, wherein the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude represent the BOLD signal intensity of spontaneous activity of the brain region.
Preferably, the judging module is configured to determine whether the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude calculated by the calculating module fall within a confidence interval obtained by statistics in advance, if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude fall within the confidence interval, indicate that the activity of the corresponding brain function region is normal, and if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude do not fall within the confidence interval, indicate that the activity of the corresponding brain function region is abnormal, and feed back a sequence number of the corresponding brain function region according to the determined abnormal brain function region; the confidence interval is a result obtained by calculating the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude of each brain functional area by using a resting state functional magnetic resonance pipeline type data processing tool in a brain image standardized calculation platform for functional magnetic resonance data of a certain number of normal people and performing statistical analysis.
Preferably, the position determining module includes a coordinate obtaining module and a position calculating module, the coordinate obtaining module is configured to obtain a standard position coordinate of a brain area corresponding to the serial number in a standard human brain model according to the serial number, the position calculating module is configured to calculate an actual position coordinate of the brain area corresponding to the serial number in the patient brain model through a transformation relation according to the standard position coordinate, and the transformation relation is a corresponding relation between the standard brain model and a brain model of the person to be treated.
Preferably, the transformation relation is obtained by:
step 1): establishing a corresponding relation between the standard brain model and the brain model of the treated person:
q i ＝Rp i +t，
wherein p is i As points in the standard brain model, q i Points in the subject brain model, R, t being the transformation relationship;
step 2): the equivalence optimization problem of the iterative closest point algorithm is expressed as:
the transformation relation R, t between the standard brain model and the brain model of the treated person can be obtained by solving the optimization problem.
Preferably, the patient brain model is obtained using the following steps:
firstly, using a visualization tool suite to import structural magnetic resonance data of a treated person;
secondly, reconstructing a three-dimensional model of the head of the treated person by using a three-dimensional reconstruction algorithm based on volume rendering in the visualization tool suite;
and finally, smoothing the reconstructed three-dimensional model by using a smoothing algorithm in a visualization tool suite and outputting the model.
Preferably, the stimulation device further comprises a camera and a mechanical arm, the stimulation module is mounted on the mechanical arm, the camera is used for shooting an image of the face of a treated person so as to acquire coordinates of eyes and a nose in a coordinate space of the camera, and the position where the mechanical arm drives the stimulation module to move is converted through the following method:
(1) reading the left eye position p from the brain model of the treated person left Right eye position p right And nose position p nose And establishing a first feature space coordinate system by the following formula:
X feature ＝p left -p right ，
Y featur ＝X feature ×Z feature ，
calculating a transformation relation between the first feature space coordinate system and a coordinate system of a brain model of the treated person
(2) Acquiring the left eye position, the right eye position and the nose position in the camera space, calculating a second feature space coordinate system through the formula in the step (1), and calculating the conversion relation between the camera space coordinate system and the second feature space coordinate system
(3) The conversion relation from the camera space to the mechanical arm motion space is solved through a hand-eye calibration algorithm
(4) By the resulting transformation relation
And the transformation relation R, t calculated in the position determination module establishes the transformation relation from the standard brain model to the mechanical arm motion space
(5) Obtaining the position of the corresponding brain region in the standard brain region model multiplied by
The position of the stimulation module driven by the mechanical arm to move can be obtained.
Compared with the prior art, the invention has the following beneficial effects:
the invention can accurately position the abnormal activity of the patient and carry out magnetic stimulation in the accurate positioning.
Drawings
Fig. 1 is a schematic diagram of a preferred embodiment according to the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art.
As shown in fig. 1, a functional nuclear magnetic resonance-based transcranial magnetic precise stimulation system includes:
the brain region activity analysis module is used for analyzing an abnormal activity region in the brain region of the treated person according to the functional magnetic resonance data of the treated person;
the position determining module is used for determining the position of the abnormal-activity area in the brain area of the treated person in the head of the patient according to the abnormal-activity area in the brain area analyzed by the brain area activity analyzing module and by combining the structural magnetic resonance data of the treated person;
and the stimulation module is used for performing magnetic stimulation on the position determined by the position determination module.
Specifically, the brain region activity analysis module includes a calculation module and a determination module, the calculation module is configured to calculate a low-frequency fluctuation Amplitude (ALFF) and a fractional low-frequency fluctuation amplitude (facial fluctuation) of each brain region of the subject through a static functional magnetic resonance pipeline data processing tool (DPARSF) in a brain image standardization platform (a brain for data processing & analysis for bridging, DPABI), wherein the low-frequency fluctuation Amplitude (ALFF) and the fractional low-frequency fluctuation amplitude (facial fluctuation, functional fluctuation) represent the brain activity intensity of the brain region, and the functional fluctuation amplitude (facial fluctuation) represents the brain activity intensity of the brain region.
The judging module is used for judging whether the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude calculated by the calculating module fall within a confidence interval obtained by statistics in advance or not, if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude fall within the confidence interval, the activity of the corresponding brain functional area is normal, if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude do not fall within the confidence interval, the activity of the corresponding brain functional area is abnormal, and the serial number of the corresponding brain functional area is fed back according to the judged abnormal brain functional area. The confidence interval is a result obtained by calculating the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude of each brain functional area by using a resting state functional magnetic resonance pipeline type data processing tool in a brain image standardization calculation platform for functional magnetic resonance data of one hundred normal persons and performing statistical analysis.
The position determining module comprises a coordinate obtaining module and a position calculating module, and the coordinate obtaining module is used for obtaining the standard position coordinates of the brain area corresponding to the serial number under the standard human brain model according to the serial number. And the position calculation module is used for calculating the actual position coordinates of the brain area corresponding to the serial number in the brain model of the patient through a transformation relation according to the standard position coordinates.
The transformation relation is a corresponding relation between a standard brain model and a brain model of a treated person and is calculated through an iterative closest point algorithm (ICP). The patient brain model is generated according to the structural magnetic resonance data of the patient, and the generation process mainly comprises the following steps of firstly importing the structural magnetic resonance data of the treated person by using a Visualization Tool Kit (VTK), then reconstructing a head three-dimensional model of the treated person by using a three-dimensional reconstruction algorithm based on volume rendering in the visualization tool kit, and finally smoothing and outputting the reconstructed head three-dimensional model by using a smoothing algorithm in the visualization tool kit, so that the patient brain model can be obtained in the output head three-dimensional model.
Iterative Closest Point (ICP) algorithm mainly finds the closest point in the two sets of point clouds, calculates the error between the two sets of point clouds according to the estimated transformation relationship, and finally obtains the transformation relationship between the two point clouds by continuously iterating to minimize the error between the two point clouds, i.e. obtaining the transformation relationship between the standard brain model and the brain model of the person to be treated.
The transformation relation is represented by R, t, and the point p in the standard brain model i And point q in the subject's brain model i The following correspondence is satisfied:
q i ＝Rp i +t
further, the equivalence optimization problem of the iterative closest point algorithm can be expressed as:
the transformation relation R, t between the standard brain model and the brain model of the treated person can be obtained by solving the optimization problem.
The system further comprises a camera and the mechanical arm, the stimulation module is installed on the mechanical arm, the camera can acquire coordinates of corresponding objects through photographing, the position of the camera in the system is fixed, in practice, the camera cannot directly photograph a brain area, some coordinates of structures which are convenient for acquiring the coordinates on facial features, such as a nose, eyes and the like, need to be acquired through photographing the facial features, and the position of the brain area to be stimulated is calculated through the coordinates of the nose and the eyes. Meanwhile, the stimulation module is finally driven to move through the mechanical arm, so that the position of the brain area to be stimulated needs to be converted into the position of the mechanical arm in the space.
The conversion method comprises the following steps:
(1) reading left eye position p from a three-dimensional model of a patient's head left Right eye position p right And nose position p nose And establishing a first feature space coordinate system by the following formula:
X feature ＝p left -p right ，
Y featur ＝X feature ×Z feature ，
calculating a conversion relation between the characteristic space coordinate system and a coordinate system of the three-dimensional model of the head of the patient
(2) Acquiring the left eye position, the right eye position and the nose position in the camera space, calculating a second feature space coordinate system through the formula in the step (1), and calculating the conversion relation between the camera space coordinate system and the second feature space coordinate system
(3) Because the relative position of the camera and the cooperative mechanical arm is fixed, the transformation relation from the camera space to the mechanical arm motion space is solved through a hand-eye calibration algorithm
(4) By the resulting transformation relation
And the transformation relation R, t calculated in the position determination module is used for establishing the transformation relation from the standard brain model to the motion space of the cooperative mechanical arm
(5) Obtaining the position of the corresponding brain area in the standard brain area model multiplied by
The position where the cooperative mechanical arm drives the stimulation module to move can be obtained, and stimulation is further carried out.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A system for achieving transcranial magnetic precise stimulation based on functional nuclear magnetic resonance is characterized by comprising:
the brain region activity analysis module is used for analyzing an abnormal activity region in the brain region of the treated person according to the functional magnetic resonance data of the treated person;
the position determining module is used for determining the position of the abnormal-activity area in the brain area of the treated person in the head of the patient according to the abnormal-activity area in the brain area analyzed by the brain area activity analyzing module and by combining the structural magnetic resonance data of the treated person;
and the stimulation module is used for performing magnetic stimulation on the position determined by the position determination module.
2. The system of claim 1, wherein the brain region activity analysis module comprises a calculation module and a determination module, the calculation module is configured to calculate the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude of each brain region of the treated person through a resting-state functional magnetic resonance pipeline data processing tool in the brain image standardization platform, wherein the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude represent BOLD signal intensity of spontaneous activity of the brain region.
3. The system according to claim 2, wherein the judging module is configured to determine whether the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude calculated by the calculating module fall within a confidence interval counted in advance, if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude fall within the confidence interval, indicate that the activity of the corresponding brain functional region is normal, and if the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude do not fall within the confidence interval, indicate that the activity of the corresponding brain functional region is abnormal, and feed back the sequence number of the corresponding brain functional region according to the determined abnormal brain functional region; the confidence interval is a result obtained by calculating the low-frequency fluctuation amplitude and the fractional low-frequency fluctuation amplitude of each brain functional area by using a resting state functional magnetic resonance pipeline type data processing tool in a brain image standardized calculation platform for functional magnetic resonance data of a certain number of normal people and performing statistical analysis.
4. The system according to claim 1, wherein the position determining module comprises a coordinate obtaining module and a position calculating module, the coordinate obtaining module is configured to obtain a standard position coordinate of the brain area corresponding to the serial number in the standard human brain model according to the serial number, the position calculating module is configured to calculate an actual position coordinate of the brain area corresponding to the serial number in the patient brain model according to the standard position coordinate through a transformation relation, and the transformation relation is a corresponding relation between the standard brain model and the brain model of the treated person.
5. The system of claim 4, wherein the transformation relationship is obtained by:
step 1): establishing a corresponding relation between the standard brain model and the brain model of the treated person:
q i ＝Rp i +t，
wherein p is i As points in the standard brain model, q i Points in the subject brain model, R, t being the transformation relationship;
step 2): the equivalence optimization problem of the iterative closest point algorithm is expressed as:
the transformation relation R, t between the standard brain model and the brain model of the treated person can be obtained by solving the optimization problem.
6. The system of claim 4, wherein the patient brain model is obtained using the steps of:
firstly, using a visualization tool kit to import structural magnetic resonance data of a treated person;
secondly, reconstructing a three-dimensional model of the head of the treated person by using a three-dimensional reconstruction algorithm based on volume rendering in the visualization tool suite;
and finally, smoothing the reconstructed three-dimensional model by using a smoothing algorithm in a visualization tool suite and outputting the model.
7. The system of claim 5, further comprising a camera and a mechanical arm, wherein the stimulation module is mounted on the mechanical arm, the camera is used for shooting the image of the face of the treated person so as to acquire the coordinates of the eyes and the nose in the coordinate space of the camera, and the position of the mechanical arm for driving the stimulation module to move is converted by the following method:
(1) reading the left eye position p from the brain model of the treated person left Right eye position p right And nose position p nose And establishing a first feature space coordinate system by the following formula:
X feature ＝p left -p right ，
Y featur ＝X feature ×Z feature ，
calculating a transformation relation between the first feature space coordinate system and the coordinate system of the brain model of the treated person
(2) Acquiring the left eye position, the right eye position and the nose position in the camera space, calculating a second feature space coordinate system through the formula in the step (1), and calculating the conversion relation between the camera space coordinate system and the second feature space coordinate system
(3) The conversion relation from the camera space to the mechanical arm motion space is solved through a hand-eye calibration algorithm
(4) By the resulting transformation relation
And the transformation relation R, t calculated in the position determination module establishes the transformation relation from the standard brain model to the mechanical arm motion space
(5) Obtaining the position of the corresponding brain region in the standard brain region model multiplied by
The position of the stimulation module to be moved driven by the mechanical arm can be obtained.
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