CN113571142B - Mental image integrated system - Google Patents
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Abstract
The invention discloses a mental image integrated system, comprising: the mental image standardized acquisition system comprises: the method comprises a magnetic resonance scanning scheme and a clinical data acquisition scheme, wherein the magnetic resonance scanning scheme comprises a scanning sequence, parameter design and quality monitoring, and the clinical data acquisition scheme comprises clinical symptoms and cognitive function assessment related to mental diseases; the visual data management platform is constructed based on a DCMTK image storage system and a MySQL database management system, the image storage system is used for receiving and storing image data and pushing the image data to a MySQL database, and the database management system is used for managing the image data in the MySQL database; a one-key system for generating mental image individuation report comprises an automatic processing module and a report issuing module. The invention can ensure high-quality mental image data acquisition and scientifically and orderly data management of the mental image data through the integrated system architecture, and generate the mental image individuation analysis report, thereby effectively realizing the transformation application of brain function image analysis indexes to clinical auxiliary diagnosis and treatment prognosis.
Description
Technical Field
The invention relates to the technical field of mental image processing, in particular to a mental image integrated system.
Background
Recent epidemiological investigation data in China show that the annual prevalence rate of seven major mental diseases in China is 9.3%, and the lifelong prevalence rate reaches 16.6%, thus bringing heavy burden to individuals, families and society. With the rapid development of neuropsychiatric imaging technology, the brain imaging technology based on magnetic resonance opens a new era of noninvasive living body knowledge of brain fine structure and network function, and the new means of psychoimaging research provides a great deal of research results for deep knowledge of the occurrence and development mechanism of diseases and finding new diagnosis and treatment basis.
In recent years, imaging researches of mental disorders mostly have the problems of poor repeatability, high result heterogeneity and the like, and the reasons of the image heterogeneity are closely related to inconsistent scanning sequence parameters, uneven data acquisition quality, uneven scanning equipment quality control level and the like.
Meanwhile, the magnetic resonance image data for mental image analysis has the characteristics of more scanning sequences, large image quantity, high confidentiality requirement and the like, so that higher requirements are put on the transmission, storage and management of the data. Reasonable scan sequence design, scan scheme optimization and strict quality control are the preconditions for providing high-quality reliable data for image diagnosis and scientific research analysis. And scientific and orderly data management, and ensuring the integrity of the image data are the basis of subsequent research and analysis. Clinical image data can be effectively and scientifically managed due to the existence of a hospital HIS and a PACS system, but mental image data is always a difficult problem, because the existence of multiple sequences of the clinical image data leads to the fact that single image data is very large and can reach 2Gb at maximum, if the existing PACS system for managing the nerve and mental disease image data of a hospital is used, a large amount of resources are occupied, extra cost is brought to the operation of the hospital, and if the clinical image data is separated from the PACS system, various problems such as viruses, data leakage and inconvenience exist in the modes of hard disk storage, disc carving and the like are solved.
Moreover, there are a great deal of research results on brain function image studies for mental disorders at present, but there is no personalized mental image brain function analysis report for guiding clinical diagnosis and efficacy evaluation. The brain function analysis method comprises the steps of constructing an individual brain function network map through a brain function image analysis technology, analyzing brain function activity indexes, visualizing analysis results, and displaying analysis reports to clinicians and patients, so that the brain function network map is used for individual assessment of brain function activity changes of mental disorder patients, and analyzing change tracks of influence of treatment modes on the individual brain function activities. Through the mental image individuation analysis report, the transformation application of brain function image analysis indexes to clinical auxiliary diagnosis and treatment prognosis can be effectively realized. At present, no report of the individual analysis report of the mental image is seen. Particularly, in the aspect of mental image detection report, the report is provided with a big problem based on the data characteristics of mental images.
Disclosure of Invention
The invention aims to provide an integrated system capable of ensuring high-quality mental image data acquisition, scientifically and orderly managing mental image data and generating a mental image individuation analysis report.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a mental image integration system, comprising:
the mental image standardized acquisition system comprises:
an intelligent mental image management system;
a system for generating a brain function report by using one-key mental image individuation.
Preferably, the standardized acquisition system for mental images comprises:
a. designing a magnetic resonance scanning sequence and scanning parameters;
b. acquiring magnetic resonance image data;
c. monitoring the quality of the magnetic resonance image data;
d. clinical information related to mental diseases is collected.
Further, the scanning sequence includes: high-resolution T1WI, T2WI structural images, high-space-time resolution resting state functional images and high-space resolution diffusion tensor imaging;
the magnetic resonance scanning parameter design method comprises the following steps:
aiming at mental disorder, substance dependence and attention deficit hyperactivity disorder, the single scanning time of a single sequence of a single mode is 5-10 minutes, and the scanning times are 3-4 times;
aiming at mood disorder and anxiety disorder, the single scanning time of a single sequence of a single mode is 20-30 minutes, and the scanning times are 1-2 times;
the quality monitoring of the magnetic resonance image data comprises the following steps: in resting state functional magnetic resonance imaging, translational and rotational parameters and mean framewise displacement values in the x, y and z directions are monitored and fed back in real time; the real-time monitoring mode is as follows: after the single scanning is finished, adding a scanning pause prompt;
the quality monitoring of the magnetic resonance image data further comprises: a scanning situation questionnaire, the scanning situation questionnaire comprising:
whether tension, anxiety or other discomfort is felt during the scan;
whether the scanning process can be effectively communicated with a scanner or not, and timely feeding back uncomfortable or other conditions felt during scanning;
whether the scanning process remains motionless or as little amplitude as possible, including the frequency and time of occurrence of head or other body part movements;
whether to enter a sleep state or not in the scanning process, such as occurrence of a sleeping situation, occurrence frequency and occurrence time;
when the resting state functional magnetic resonance scanning is carried out, whether the sleep state is entered, whether the eye is open or closed is considered, if the situation is occurred, the frequency and the time of occurrence are the same;
recording head movement parameters of a patient in the scanning process;
the collection of clinical information related to mental disorders includes general data collection tables including, but not limited to, the following tables: demographic scale, history of disease itself, family history, edinburgh manual, nicotine dependency test scale, mitsugan alcohol questionnaire, pittsburgh sleep quality index, west Intelligence test, hamilton depression scale, hamilton anxiety scale;
the collection of clinical information related to mental disorders also includes clinical symptom assessment protocols, the assessment of which include, but are not limited to, the following scales:
schizophrenia, using positive and negative symptoms scales and/or Beck suicide ideation scales;
major depressive disorder, using the hamilton depression scale and/or the Beck suicidal ideation scale and/or the eiseng personality questionnaire;
bipolar disorder using the becker-la Fan Sen mania/yankee mania rating scale;
forced disorder, using the yeru-brownian forced scale;
anxiety disorders, using a panic disorder severity scale and/or a Liebowitz social anxiety scale;
post-traumatic stress disorder, using a clinician-specific PTSD scale and/or a traumatic event questionnaire;
attention deficit hyperactivity disorder using a Conners' parental rating scale;
alcohol and substance dependence, using a screening scale for addiction severity index and/or WHO psychoactive substance use;
the collecting clinical information related to mental disorders further includes neuropsychological testing protocols including, but not limited to, the following: west memory test, rey hearing word learning test, persistence operation test, attention network task, wisconsin card classification test, stroop word test, london tower test, N-back test, face resolution test, emotion Stroop test.
Further, the scanning sequence is directed to mood disorders, anxiety disorders, and further includes high resolution hippocampal structural images and high resolution amygdala structural images.
Preferably, the mental image intelligent management system comprises a visual mental image data management platform; the platform is constructed based on a DCMTK image storage system and a MySQL database management system, wherein the image storage system is used for receiving and storing image data and pushing the image data to a MySQL database; the database management system is used for managing image data in the MySQL database; the database management system comprises project data management, wherein the project data management adopts a B/S architecture, and an interface of the project data management is a browser;
the local storage mode of the image storage system is a directory tree mode, and the query mode of the image data is keyword query;
the visual data management platform also comprises an offline data arrangement module which is used for arranging the offline data stored in the hard disk and the optical disk into a MySQL database;
the MySQL database is positioned on a server, and a terminal positioned in the same local area network with the server is provided with the browser;
the image storage system of the DCMTK is packaged in a thread and is provided with an initialization interface, initialization parameters are obtained by reading a configuration file by an initialization function, and a configuration file processing class is provided by the DCMTK; the thread receives image data by calling a blocking connection function waitForAssociation of the DCMTK;
the image data in the MySQL database comprises DICOM image basic information and a storage path; the keyword query is a B+ Tree algorithm with a sequential access pointer and built in MySQL;
the local storage mode of the image data is a storage structure of an item- > a event ID- > a checking- > a sequence- > an image file, and the key words comprise database field names corresponding to the item and/or the event ID and/or the checking and/or the sequence and/or the image file;
the visual data management platform comprises a login module, and accesses the data including creation and management of clinical and scientific research projects, inquiry and supplement of image data, uploading and management of clinical data, setting and management of scanning parameters and online preview and viewing of various data;
the browser comprises a configuration file input window; the image data is classified and stored according to patients.
Preferably, the one-key mental image individuation brain function report generating system comprises:
and an automatic processing module: and processing the acquired brain image data to acquire brain function analysis parameters, and constructing an individual brain network diagram, a local brain function activity diagram and a brain function connection diagram.
And a report issuing module: and extracting the processed brain image data information, generating an individualized report according to the information type and preset rules, and displaying and printing the report.
Further, the automated processing module comprises the following steps:
step s1: collecting brain image data, wherein the brain image data comprises a 3D-T1 structural image and a resting state functional image;
step s2: preprocessing the resting state functional image by using SPM software and FSL software, and processing the 3D-T1 structural image by using Freesurfer software;
step s3: constructing an individualized brain function network;
step s4: constructing a local brain function activity diagram;
step s5: constructing a brain function connection diagram;
the report issuing module comprises the following steps:
step S1, extracting brain function image data information of a patient, and generating an individuation report according to a preset rule according to the information type; the preset rule is as follows: including basic information of the patient, name, sex, age, examination number, examination time; basic steps of selecting data preprocessing include, but are not limited to: temporal layer correction, head motion correction, spatial normalization, smoothing, and filtering; selecting a desired calculated brain function analysis index, including but not limited to: an individualized brain function network diagram, a local brain function activity diagram and brain function connection analysis indexes;
and S2, displaying the report and printing.
Further, in step s2, the preprocessing the rest state function image by using the SPM software and the FSL software includes the following steps:
step s201: correcting distortion caused by magnetic field non-uniformity by using an FSL's FUGUE tool;
step s202: removing the first 10 time points to eliminate the effect of magnetic field inhomogeneity;
step s203: time layer correction;
step s204: head movement correction;
step s205: removing head movement, brain signals, cerebrospinal fluid signals and white matter signals as covariates;
step s206: band-pass filtering and de-linearization drift;
in step s2, the processing of the 3D-T1 structural image using the Freesurfer software includes: reconstructing and registering a cortical surface grid of the 3D-T1 structural image of the subject to a spherical coordinate system, and registering the 3D-T1 structural image and the resting state functional image by adopting a boundary-based registration mode;
in step s3, the constructing an individualized brain function network includes the steps of:
step s301: obtaining reference signals of each brain network of the individual through 18 brain network templates, namely, the average value of time sequences of the reference signals;
step s302: reassigning the maximum correlation of the BOLD signal of each vertex on the brain map and the reference signal to one of 18 brain networks, wherein the ratio between the maximum correlation value and the second maximum correlation value is a confidence value, and after reassigning each vertex, averaging the BOLD signals of the high-confidence vertices in each brain network and defining the BOLD signals as core signals;
step s303: for each brain network, carrying out weighted average on the core signal and the original reference signal, namely multiplying the core signal by a weighted parameter, wherein the weighted parameter comprises a difference value of individual function connection and iteration times, and taking the average signal as a new reference signal of the next iteration;
step s304: repeating the step s302 and the step s303 until a preset stopping condition is reached, namely, the preset iteration times or the result coincidence degree of the two iterations is more than 98%, and obtaining an individualized brain network diagram;
in step s4, the constructing a local brain function activity map includes the steps of:
step s401: calculating a low frequency oscillation amplitude ALFF for all voxels of the whole brain and dividing the ALFF value for each voxel by the whole brain average ALFF value as normalized ALFF value for each voxel;
step s402: calculating low-frequency oscillation amplitude fraction fALFF for all voxels of the whole brain, dividing each frequency energy in a low-frequency range by the whole frequency range to obtain a fALFF value, and carrying out standardization processing on the fALFF value to obtain a standardized fALFF value;
step s403: calculating and comparing the time sequence synchronicity of each voxel of the whole brain and adjacent voxels thereof to obtain a Kendel harmony coefficient, namely a local consistency ReHo value, dividing the ReHo value by a whole brain ReHo mean value for standardization, and carrying out smoothing treatment on the obtained ReHo graph;
in step s5, the constructing a brain function connection graph includes the steps of:
step s501: extracting a region of interest in an individual brain function network, and calculating Pearson correlation coefficients of a time sequence of the region of interest and a time sequence of all voxels of the whole brain, wherein the obtained correlation coefficient r is the functional connection between the region of interest and the whole brain;
step s502: and the Fisher's z transformation is carried out on the function connection value, so that the normalization is improved.
An apparatus, said apparatus comprising: the system comprises a memory, a processor and a one-key type mental image individuation report generating program which is stored in the memory and can run on the processor, wherein the one-key type mental image individuation report generating program is used for realizing a mental image individuation brain function report generating system.
A storage medium, wherein a mental image individuation report generating program is stored in the storage medium, the report generating program is executed by a processor, and the steps included in the mental image individuation brain function report generating system are realized.
The invention has the following beneficial effects:
1. the invention provides a standardized mental image acquisition scheme for mental disorder patients, and solves the problems of single image data acquisition mode, irregular data acquisition flow, uneven data quality and the like in the prior art; through standardized image and clinical data acquisition processes, the compliance of patients and clinical operability are improved, and high-quality multi-dimensional image and clinical integration data are provided for mental image diagnosis and personalized analysis;
2. the invention solves the problems of large image quantity, high confidentiality requirement, large management difficulty and the like of the mental image data, effectively avoids the possible resource waste caused by the fact that the hospital is used for storing, managing and maintaining the mental image data, and reduces the extra operation cost; the integration and archiving storage of images and clinical multidimensional information of patients with mental disorder can be realized, and the storage and management of individualized full-course data can be realized;
3. by constructing an individual brain function map, analyzing brain local function activities and abnormal changes of brain function network connection, the invention provides more brain function image information for assisting clinical diagnosis of psychiatrists; by establishing an individualized brain function network to generate a mental image individualized report, the characteristic mental image representation of the mental disorder can be explored from an individualized angle, and the related brain image biomarker of the specific clinical behavior of the mental disorder can be accurately positioned.
Drawings
FIG. 1 is a block diagram of a system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
A mental image integration system, comprising:
the mental image standardized acquisition system comprises:
an intelligent mental image management system;
a system for generating a brain function report by using one-key mental image individuation.
The standardized acquisition system of the mental image comprises:
a. designing a magnetic resonance scanning sequence and scanning parameters;
b. acquiring magnetic resonance image data;
c. monitoring the quality of the magnetic resonance image data;
d. clinical information related to mental diseases is collected.
The scan sequence includes: high-resolution T1WI, T2WI structural images, high-space-time resolution resting state functional images and high-space resolution diffusion tensor imaging;
the magnetic resonance scanning parameter design method comprises the following steps:
aiming at mental disorder, substance dependence and attention deficit hyperactivity disorder, the single scanning time of a single sequence of a single mode is 5-10 minutes, and the scanning times are 3-4 times;
for mood disorder and anxiety disorder, the single scanning time of single sequence of single mode is 20-30 minutes, and the scanning times are 1-2 times.
The quality monitoring of the magnetic resonance image data comprises the following steps: in resting state functional magnetic resonance imaging, translational and rotational parameters and mean framewise displacement values in the x, y and z directions are monitored and fed back in real time; the real-time monitoring mode is as follows: and after the single scanning is finished, adding a scanning pause prompt.
The quality monitoring of the magnetic resonance image data further comprises: a scanning situation questionnaire, the scanning situation questionnaire comprising:
whether tension, anxiety or other discomfort is felt during the scan;
whether the scanning process can be effectively communicated with a scanner or not, and timely feeding back uncomfortable or other conditions felt during scanning;
whether the scanning process remains motionless or as little amplitude as possible, including the frequency and time of occurrence of head or other body part movements;
whether to enter a sleep state or not in the scanning process, such as occurrence of a sleeping situation, occurrence frequency and occurrence time;
when the resting state functional magnetic resonance scanning is carried out, whether the sleep state is entered, whether the eye is open or closed is considered, if the situation is occurred, the frequency and the time of occurrence are the same;
recording of the patient's head movement parameters during the scan.
The collection of clinical information related to mental disorders includes general data collection tables including, but not limited to, the following tables: demographic scale, history of disease itself, family history, edinburgh manual, nicotine dependency test scale, mitsugan alcohol questionnaire, pittsburgh sleep quality index, west Intelligence test, hamilton depression scale, hamilton anxiety scale.
The collection of clinical information related to mental disorders also includes clinical symptom assessment protocols, the assessment of which include, but are not limited to, the following scales:
schizophrenia, using positive and negative symptoms scales and/or Beck suicide ideation scales;
major depressive disorder, using the hamilton depression scale and/or the Beck suicidal ideation scale and/or the eiseng personality questionnaire;
bipolar disorder using the becker-la Fan Sen mania/yankee mania rating scale;
forced disorder, using the yeru-brownian forced scale;
anxiety disorders, using a panic disorder severity scale and/or a Liebowitz social anxiety scale;
post-traumatic stress disorder, using a clinician-specific PTSD scale and/or a traumatic event questionnaire;
attention deficit hyperactivity disorder using a Conners' parental rating scale;
alcohol and substance dependence, using a screening scale for addiction severity index and/or WHO psychoactive substance use.
The collecting clinical information related to mental disorders further includes neuropsychological testing protocols including, but not limited to, the following: west memory test, rey hearing word learning test, persistence operation test, attention network task, wisconsin card classification test, stroop word test, london tower test, N-back test, face resolution test, emotion Stroop test.
The scanning sequence is aimed at mood disorders, anxiety disorders, and also includes high resolution hippocampal structural images and high resolution amygdala structural images.
The mental image intelligent management system comprises a visual mental image data management platform; the platform is constructed based on a DCMTK image storage system and a MySQL database management system, wherein the image storage system is used for receiving and storing image data and pushing the image data to a MySQL database; the database management system is used for managing image data in the MySQL database; the database management system comprises project data management, wherein the project data management adopts a B/S architecture, and an interface of the project data management is a browser; the local storage mode of the image storage system is a directory tree mode, and the query mode of the image data is keyword query; the visual data management platform also comprises an offline data arrangement module which is used for arranging the offline data stored in the hard disk and the optical disk into a MySQL database; the MySQL database is positioned on a server, and a terminal positioned in the same local area network with the server is provided with the browser; the image storage system of the DCMTK is packaged in a thread and is provided with an initialization interface, initialization parameters are obtained by reading a configuration file by an initialization function, and a configuration file processing class is provided by the DCMTK; the thread receives image data by calling a blocking connection function waitForAssociation of the DCMTK; the image data in the MySQL database comprises DICOM image basic information and a storage path; the keyword query is a B+ Tree algorithm with a sequential access pointer and built in MySQL; the local storage mode of the image data is a storage structure of an item- > a event ID- > a checking- > a sequence- > an image file, and the key words comprise database field names corresponding to the item and/or the event ID and/or the checking and/or the sequence and/or the image file.
The visual data management platform comprises a login module, and accesses the data including creation and management of clinical and scientific research projects, inquiry and supplement of image data, uploading and management of clinical data, setting and management of scanning parameters and online preview and viewing of various data.
The browser comprises a configuration file input window; the image data is classified and stored according to patients.
The one-key mental image individuation brain function report generating system comprises:
and an automatic processing module: and processing the acquired brain image data to acquire brain function analysis parameters, and constructing an individual brain network diagram, a local brain function activity diagram and a brain function connection diagram.
And a report issuing module: and extracting the processed brain image data information, generating an individualized report according to the information type and preset rules, and displaying and printing the report.
Meanwhile, the automatic processing module comprises the following steps:
step s1: collecting brain image data, wherein the brain image data comprises a 3D-T1 structural image and a resting state functional image;
step s2: preprocessing the resting state functional image by using SPM software and FSL software, and processing the 3D-T1 structural image by using Freesurfer software;
step s3: constructing an individualized brain function network;
step s4: constructing a local brain function activity diagram;
step s5: and constructing a brain function connection diagram.
And, the report issuing module comprises the following steps:
step S1, extracting brain function image data information of a patient, and generating an individuation report according to a preset rule according to the information type; the preset rule is as follows: including basic information of the patient, name, sex, age, examination number, examination time; basic steps of selecting data preprocessing include, but are not limited to: temporal layer correction, head motion correction, spatial normalization, smoothing, and filtering; selecting a desired calculated brain function analysis index, including but not limited to: an individualized brain function network diagram, a local brain function activity diagram and brain function connection analysis indexes;
and S2, displaying the report and printing. In step s2, the preprocessing of the rest state functional image by using the SPM software and the FSL software includes the following steps:
step s201: correcting distortion caused by magnetic field non-uniformity by using an FSL's FUGUE tool;
step s202: removing the first 10 time points to eliminate the effect of magnetic field inhomogeneity;
step s203: time layer correction;
step s204: head movement correction;
step s205: removing head movement, brain signals, cerebrospinal fluid signals and white matter signals as covariates;
step s206: bandpass filtering and de-linearisation drift.
Meanwhile, in step s2, the processing of the 3D-T1 structural image using Freesurfer software includes: reconstructing and registering a cortical surface grid of the 3D-T1 structural image of the subject to a spherical coordinate system, and registering the 3D-T1 structural image and the resting state functional image by adopting a boundary-based registration mode.
In step s3, the constructing an individualized brain function network includes the steps of:
step s301: obtaining reference signals of each brain network of the individual through 18 brain network templates, namely, the average value of time sequences of the reference signals;
step s302: reassigning the maximum correlation of the BOLD signal of each vertex on the brain map and the reference signal to one of 18 brain networks, wherein the ratio between the maximum correlation value and the second maximum correlation value is a confidence value, and after reassigning each vertex, averaging the BOLD signals of the high-confidence vertices in each brain network and defining the BOLD signals as core signals;
step s303: for each brain network, carrying out weighted average on the core signal and the original reference signal, namely multiplying the core signal by a weighted parameter, wherein the weighted parameter comprises a difference value of individual function connection and iteration times, and taking the average signal as a new reference signal of the next iteration;
step s304: and repeating the step s302 and the step s303 until a preset stopping condition is reached, namely, the preset iteration times or the result coincidence degree of the two iterations is more than 98%, and obtaining the personalized brain network diagram.
In step s4, the constructing a local brain function activity map includes the steps of:
step s401: calculating a low frequency oscillation amplitude ALFF for all voxels of the whole brain and dividing the ALFF value for each voxel by the whole brain average ALFF value as normalized ALFF value for each voxel;
step s402: calculating low-frequency oscillation amplitude fraction fALFF for all voxels of the whole brain, dividing each frequency energy in a low-frequency range by the whole frequency range to obtain a fALFF value, and carrying out standardization processing on the fALFF value to obtain a standardized fALFF value;
step s403: and (3) calculating and comparing the time sequence synchronicity of each voxel of the whole brain and adjacent voxels thereof to obtain a Kendel harmony coefficient, namely a local consistency ReHo value, dividing the ReHo value by a whole brain ReHo mean value for standardization, and carrying out smoothing treatment on the obtained ReHo graph.
In step s5, the constructing a brain function connection graph includes the steps of:
step s501: extracting a region of interest in an individual brain function network, and calculating Pearson correlation coefficients of a time sequence of the region of interest and a time sequence of all voxels of the whole brain, wherein the obtained correlation coefficient r is the functional connection between the region of interest and the whole brain;
step s502: and the Fisher's z transformation is carried out on the function connection value, so that the normalization is improved.
The invention also includes an apparatus comprising: the system comprises a memory, a processor and a one-key type mental image individuation report generating program which is stored in the memory and can run on the processor, wherein the one-key type mental image individuation report generating program is used for realizing a mental image individuation brain function report generating system.
The invention also comprises a storage medium, wherein the storage medium is stored with a mental image individuation report generating program, the report generating program is executed by a processor, and the steps included in the mental image individuation brain function report generating system are realized.
Of course, the present invention is capable of other various embodiments and its several details are capable of modification and variation in light of the present invention by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. The mental image integration system is characterized by comprising:
the mental image standardized acquisition system comprises:
an intelligent mental image management system;
a one-key mental image individuation brain function report generating system; comprising the following steps:
and an automatic processing module: processing the acquired brain image data to acquire brain function analysis parameters, and constructing an individual brain network diagram, a local brain function activity diagram and a brain function connection diagram;
and a report issuing module: extracting the processed brain image data information, generating an individualized report according to the information type and preset rules, and displaying and printing the report;
the automatic processing module comprises the following steps:
step s1: collecting brain image data, wherein the brain image data comprises a 3D-T1 structural image and a resting state functional image;
step s2: preprocessing the resting state functional image by using SPM software and FSL software, and processing the 3D-T1 structural image by using Freesurfer software;
step s3: constructing an individualized brain function network;
step s4: constructing a local brain function activity diagram;
step s5: constructing a brain function connection diagram;
wherein, in step s3, said constructing an individualized brain function network comprises the steps of:
step s301: obtaining reference signals of each brain network of the individual through 18 brain network templates, namely, the average value of time sequences of the reference signals;
step s302: reassigning the maximum correlation of the BOLD signal of each vertex on the brain map and the reference signal to one of 18 brain networks, wherein the ratio between the maximum correlation value and the second maximum correlation value is a confidence value, and after reassigning each vertex, averaging the BOLD signals of the high-confidence vertices in each brain network and defining the BOLD signals as core signals;
step s303: for each brain network, carrying out weighted average on the core signal and the original reference signal, namely multiplying the core signal by a weighted parameter, wherein the weighted parameter comprises a difference value of individual function connection and iteration times, and taking the average signal as a new reference signal of the next iteration;
step s304: repeating the step s302 and the step s303 until a preset stopping condition is reached, namely, the preset iteration times or the result coincidence degree of the two iterations is more than 98%, and obtaining an individualized brain network diagram;
wherein, in step s4, the construction of the local brain function activity map includes the steps of:
step s401: calculating a low frequency oscillation amplitude ALFF for all voxels of the whole brain and dividing the ALFF value for each voxel by the whole brain average ALFF value as normalized ALFF value for each voxel;
step s402: calculating low-frequency oscillation amplitude fraction fALFF for all voxels of the whole brain, dividing each frequency energy in a low-frequency range by the whole frequency range to obtain a fALFF value, and carrying out standardization processing on the fALFF value to obtain a standardized fALFF value;
step s403: calculating and comparing the time sequence synchronicity of each voxel of the whole brain and adjacent voxels thereof to obtain a Kendel harmony coefficient, namely a local consistency ReHo value, dividing the ReHo value by a whole brain ReHo mean value for standardization, and carrying out smoothing treatment on the obtained ReHo graph;
wherein, in step s5, the constructing a brain function connection graph includes the following steps:
step s501: extracting a region of interest in an individual brain function network, and calculating Pearson correlation coefficients of a time sequence of the region of interest and a time sequence of all voxels of the whole brain, wherein the obtained correlation coefficient r is the functional connection between the region of interest and the whole brain;
step s502: and the Fisher's z transformation is carried out on the function connection value, so that the normalization is improved.
2. The mental image integration system according to claim 1, wherein: the standardized acquisition system of the mental image comprises:
a. designing a magnetic resonance scanning sequence and scanning parameters;
b. acquiring magnetic resonance image data;
c. monitoring the quality of the magnetic resonance image data;
d. clinical information related to mental diseases is collected.
3. The mental image integration system according to claim 2, wherein:
the scan sequence includes: high-resolution T1WI, T2WI structural images, high-space-time resolution resting state functional images and high-space resolution diffusion tensor imaging;
the magnetic resonance scanning parameter design method comprises the following steps:
aiming at mental disorder, substance dependence and attention deficit hyperactivity disorder, the single scanning time of a single sequence of a single mode is 5-10 minutes, and the scanning times are 3-4 times;
aiming at mood disorder and anxiety disorder, the single scanning time of a single sequence of a single mode is 20-30 minutes, and the scanning times are 1-2 times;
the quality monitoring of the magnetic resonance image data comprises the following steps: in resting state functional magnetic resonance imaging, translational and rotational parameters and mean framewise displacement values in the x, y and z directions are monitored and fed back in real time; the real-time monitoring mode is as follows: after the single scanning is finished, adding a scanning pause prompt;
the quality monitoring of the magnetic resonance image data further comprises: a scanning situation questionnaire, the scanning situation questionnaire comprising:
whether tension, anxiety or other discomfort is felt during the scan;
whether the scanning process can be effectively communicated with a scanner or not, and timely feeding back uncomfortable or other conditions felt during scanning;
whether the scanning process remains motionless or as little amplitude as possible, including the frequency and time of occurrence of head or other body part movements;
whether to enter a sleep state or not in the scanning process, such as occurrence of a sleeping situation, occurrence frequency and occurrence time;
when the resting state functional magnetic resonance scanning is carried out, whether the sleep state is entered, whether the eye is open or closed is considered, if the situation is occurred, the frequency and the time of occurrence are the same;
recording head movement parameters of a patient in the scanning process;
the collection of clinical information related to mental disorders includes general data collection tables including, but not limited to, the following tables: demographic scale, history of disease itself, family history, edinburgh manual, nicotine dependency test scale, mitsugan alcohol questionnaire, pittsburgh sleep quality index, west Intelligence test, hamilton depression scale, hamilton anxiety scale;
the collection of clinical information related to mental disorders also includes clinical symptom assessment protocols, the assessment of which include, but are not limited to, the following scales:
schizophrenia, using positive and negative symptoms scales and/or Beck suicide ideation scales;
major depressive disorder, using the hamilton depression scale and/or the Beck suicidal ideation scale and/or the eiseng personality questionnaire;
bipolar disorder using the becker-la Fan Sen mania/yankee mania rating scale;
forced disorder, using the yeru-brownian forced scale;
anxiety disorders, using a panic disorder severity scale and/or a Liebowitz social anxiety scale;
post-traumatic stress disorder, using a clinician-specific PTSD scale and/or a traumatic event questionnaire;
attention deficit hyperactivity disorder using a Conners' parental rating scale;
alcohol and substance dependence, using a screening scale for addiction severity index and/or WHO psychoactive substance use;
the collecting clinical information related to mental disorders further includes neuropsychological testing protocols including, but not limited to, the following: west memory test, rey hearing word learning test, persistence operation test, attention network task, wisconsin card classification test, stroop word test, london tower test, N-back test, face resolution test, emotion Stroop test.
4. The mental image integration system according to claim 3, wherein: the scanning sequence is aimed at mood disorders, anxiety disorders, and also includes high resolution hippocampal structural images and high resolution amygdala structural images.
5. The mental image integration system according to claim 1, wherein: the mental image intelligent management system comprises a visual mental image data management platform; the platform is constructed based on a DCMTK image storage system and a MySQL database management system, wherein the image storage system is used for receiving and storing image data and pushing the image data to a MySQL database; the database management system is used for managing image data in the MySQL database; the database management system comprises project data management, wherein the project data management adopts a B/S architecture, and an interface of the project data management is a browser;
the local storage mode of the image storage system is a directory tree mode, and the query mode of the image data is keyword query;
the visual mental image data management platform also comprises an offline data arrangement module which is used for arranging the offline data stored in the hard disk and the optical disk to a MySQL database;
the MySQL database is positioned on a server, and a terminal positioned in the same local area network with the server is provided with the browser;
the image storage system of the DCMTK is packaged in a thread and is provided with an initialization interface, initialization parameters are obtained by reading a configuration file by an initialization function, and a configuration file processing class is provided by the DCMTK; the thread receives image data by calling a blocking connection function waitForAssociation of the DCMTK;
the image data in the MySQL database comprises DICOM image basic information and a storage path; the keyword query is a B+ Tree algorithm with a sequential access pointer and built in MySQL;
the local storage mode of the image data is a storage structure of an item- > a event ID- > a checking- > a sequence- > an image file, and the key words comprise database field names corresponding to the item and/or the event ID and/or the checking and/or the sequence and/or the image file;
the visual data management platform comprises a login module, and accesses the data including creation and management of clinical and scientific research projects, inquiry and supplement of image data, uploading and management of clinical data, setting and management of scanning parameters and online preview and viewing of various data;
the browser comprises a configuration file input window; the image data is classified and stored according to patients.
6. The mental image integration system according to claim 1, wherein:
the report issuing module comprises the following steps:
step S1, extracting brain function image data information of a patient, and generating an individuation report according to a preset rule according to the information type; the preset rule is as follows: including basic information of the patient, name, sex, age, examination number, examination time; basic steps of selecting data preprocessing include, but are not limited to: temporal layer correction, head motion correction, spatial normalization, smoothing, and filtering; selecting a desired calculated brain function analysis index, including but not limited to: an individualized brain function network diagram, a local brain function activity diagram and brain function connection analysis indexes;
and S2, displaying the report and printing.
7. The mental image integration system according to claim 1, wherein:
in step s2, the preprocessing of the rest state functional image by using the SPM software and the FSL software includes the following steps:
step s201: correcting distortion caused by magnetic field non-uniformity by using an FSL's FUGUE tool;
step s202: removing the first 10 time points to eliminate the effect of magnetic field inhomogeneity;
step s203: time layer correction;
step s204: head movement correction;
step s205: removing head movement, brain signals, cerebrospinal fluid signals and white matter signals as covariates;
step s206: band-pass filtering and de-linearization drift;
in step s2, the processing of the 3D-T1 structural image using the Freesurfer software includes: reconstructing and registering a cortical surface grid of the 3D-T1 structural image of the subject to a spherical coordinate system, and registering the 3D-T1 structural image and the resting state functional image by adopting a boundary-based registration mode.
8. The mental image integration system according to any one of claims 1 to 7, an apparatus comprising: the system comprises a memory, a processor and a one-key type mental image individuation report generating program which is stored in the memory and can run on the processor, wherein the one-key type mental image individuation report generating program is used for realizing a mental image individuation brain function report generating system.
9. The integrated mental image system according to any one of claims 1 to 7, wherein a storage medium has a mental image individuation report generating program stored thereon, the report generating program being executed by a processor to realize the steps included in the mental image individuation brain function report generating system.
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